esa-sceva/satcom-chunk-collection huggingface dataset:
# Application-Oriented Data Analytics in Large-Scale Metal Sheet Bending
## 5 Conclusions  
In this study, a comprehensive analysis of multiple signals within the context of the bending machine was conducted, leading to the identification of the most-significant signals. The primary focus was on the rolling process used in the production of tanks in the oil-and-gas sector, characterized by substantial operator intervention.  
The findings highlight the potential of applying artificial intelligence (AI) to semi-manual processes, like sheet metal forming, offering prospects for enhanced efficiency, cost reduction, and improved product quality. The integration of temporal-signal-processing and AI techniques, including Markov transition field (MTF), dynamic time warping (DTW), and k-medoids clustering, was successfully employed to monitor and analyze the process state. The results not only demonstrate the efficacy of the proposed model fitting but also yield valuable insights that contribute to optimizing the manufacturing process.  
Comparing these outcomes to that of existing works in the field suggests that AI's application in semi-manual processes holds significant promise for the manufacturing industry. As technology advances, further exploration of these techniques is anticipated to play an increasingly vital role in refining manufacturing processes.  
For future research directions, completing the analysis of the impact of metal sheet dimensions on pressure and conducting a more in-depth examination of machine operations to discern specific bending moments for each head and the central area are recommended. Additionally, exploring lines of future research could involve investigating the integration of real-time feedback mechanisms, exploring novel AI algorithms, and evaluating the scalability of the proposed approach to diverse manufacturing settings. These endeavors aim to continually advance the field and address emerging challenges in the dynamic landscape of sheet metal forming.  
Conceptualization, M.P., C.R., A.M. and T.B.; Data curation, F.V., A.M. and M.P.; Formal analysis, F.V., T.B. and M.P.; Investigation, F.V., A.M. and M.P.; Methodology, F.V. and M.P.; Project administration, V.M., M.P. and C.R.; Supervision, A.M.; Validation, A.G.d.V. and A.M.; Writing--original draft, M.P., F.V. and C.R.; Writing--review and editing, F.V., T.B., A.G.d.V. and M.P. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the funding from the Horizon 2020 Research and Innovation Program of the European Union under grant agreement No. 958303.  
Not applicable.  
Data Availability StatementData will be available on request to the corresponding author. The data are not publicly available due to privacy.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company TECNALIA. Authors [PERSON] and [PERSON] were employed by the company IDESA Ingenieria y Diseno Europeo. 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.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods  
The methodology pipeline for predicting crop yield using remote sensing data and interpretable ML techniques is briefly described below:  
* Data acquisition: Crop yield data for lupin are collected from various fields.
* Data pre-processing and feature engineering: Spectral reflectance data are initially acquired through Sentinel-2 multispectral imagery. Then, 13 commonly used VIs are calculated based on 9 spectral bands. The resulting dataset is cleaned and prepared. Outliers are removed following the interquartile range (IQR) technique. Identification of highly correlated features follows through Spearman correlation analysis, while also Z-score normalization of input features takes place. Finally, data augmentation with Synthetic Minority Over-sampling Technique for Regression (SMOTER) is applied to address imbalanced data distribution.
* ML model training: Six ML regression models are trained as a means of capturing relationships among the features. In all cases, GridSearchCV and 5-fold cross-validation with shuffling are utilized for hyperparameter tuning and performance evaluation via relevant metrics.
* Model interpretation: An interpretability analysis utilizing SHAP values is then performed on the most accurate ML model to identify the factors affecting individual predictions.
### Age  
Unfortunately, cyberbullying has become prevalent among school-age children [33]. In a recent nationally representative survey of teenagers, the Pew Internet and American Life Project found that approximately 60% of teens aged 12-17 years old have personally experienced abusive online behaviors [34]. Precisely identifying the age at which cyberbullying most frequently occurs matters when designing the most efficacious anti-cyberbullying interventions. However, assessing the relationship between age and cyberbullying has proven difficult because there are many external factors. Estimates of victimization and perpetration vary widely depending on demographic (sexuality, gender identity, disability, ethnicity [35]), individual (e.g., previous experiences with victimization and technology use), family, peer, and school factors [36]. Estimates also fluctuate based on the way researchers measured those experiences; in some studies, scholars measured precise age; others measured age ranges, and some just recorded year in school [37]. In other cases, there is imprecision in measurement related to experiences of cyberbullying; in some cases, measuring experiences within a specific time period such as \"in the last week\" and in others asking about experiences over the participant's entire lifespan [37]. Finally, estimates differ depending on whether researchers assess victimization, perpetration, or bully-victims [15; 17]. While there have been mixed results on the precise impact of age, a growing body of evidence shows that cyberbullying is greatest in middle school, raising after fifth grade and peaking in eighth grade [38; 39]. However, many scholars have found that instances of cyberbullying perpetration in middle school continue to increase if not met with impactful anti-cyberbullying campaigns [15; 40]. In fact, some scholars have found that cyberbullying victimization post middle school has been reported at as high as 56%, calling for researchers to continue examining this experience across the age spectrum [41]. Taken together, the preponderance of evidence seems to suggest that, in middle school, those who are older will be more likely to both perpetrate and be victimized by cyberbullying. Therefore, we propose the following hypothesis:  
**Hypothesis 3**.: _There is a positive relationship between age and both cyberbullying perpetration and cyberbullying victimization._
## 2 Proposed Software Reliability Model
### Proposed Software Reliability Model  
The proposed model is also based on the NHPP. The mean value functions of the proposed model and the testing coverage model are similar, but there are some differences.  
The important point is the proposed model consider uncertainty environment. The actual testing environment and the theoretical test environment of the software are very different. In an actual testing environment, the developer may have unexpected variables like syntax error, which is considered in proposed model.  
Figure 1 shows infrastructure of time testing. Generalized testing time of the SRGMs is 0 \(<\) t. However, if the software that will perform the test has a syntax error, it cannot be compiled and tested. To continue testing it, the code must be modified to remove the syntax errors. Thus, the actual testing time differs from the theoretical testing time. This is a point that the proposed model considered. Testing time of proposed model considers the syntax error, which is an error in the syntax or tokens written in a programming language. So, the testing time of proposed model is t\({}_{0}<\) t.  
The mean value function m(t) of the proposed model is based on the Weibull distribution model [14], which is the expected number of software failures by time t and is defined as follows:  
\[\frac{\mathrm{d}\mathrm{m(t)}}{\mathrm{d}\mathrm{t}}=\eta[\mathrm{b(t)}][ \mathrm{N}-\mathrm{m(t)}] \tag{4}\]  
where b(t) is the failure detection rate function, N is the expected number of failures which exists in the software before testing phase. \(\eta\) is a random variable that represents the uncertainty of the system fault detection rate in the operating environments with a probability density function:  
\[\mathrm{m(t)}\ =\mathrm{N}\left(1-\mathrm{e}^{-\int_{t_{0}}^{t}\mathrm{b(s)ds} }\right)\mathrm{,\ t>t_{0}>0} \tag{5}\]  
\[\mathrm{m(t)}\ =\mathrm{N}\left(1-\frac{\beta}{\beta+\int_{t_{0}}^{t}\mathrm{b(s)ds }}\right)^{\alpha} \tag{6}\]  
where t\({}_{0}\) is the time when debugging starts after modifying the code causing syntax errors.  
b(t) of proposed model follows a Weibull failure detection rate function, that can be written as:  
\[\mathrm{b(t)}\ =\mathrm{a}^{\mathrm{b}}\mathrm{b}\mathrm{t}^{\mathrm{b}-1},\ \mathrm{a},\ \mathrm{b}>0 \tag{7}\]  
where a is a scale parameter and b is a shape parameter, that are known.  
Then, we find solution of m(t) for proposed model with syntax error by substituting b(t) above into Equation (6). Finally, the mean value function is defined by:  
\[\mathrm{m(t)}\ =\mathrm{N}\left(1-\frac{\beta}{\beta+\mathrm{a(t-t_{0})}^{ \mathrm{b}}}\right)^{\alpha}. \tag{8}\]  
Table 1 shows the mean value function m(t) of using NHPP SRGMs for existing and well-known software reliability models.  
Figure 1: Testing time infrastructure.
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 3 Results  
Data from pile load tests represent the reference for bearing capacity of individual pile or measured pile bearing capacity \(Q_{m}\). This value is obtained by the analysis of load/pile cap settlement plot. However, this value can be only obtained by an in situ pile load test. Within the design process, we must design the pile with predicted bearing capacity \(Q_{p}\). Estimation of this value based on CPTu outputs using above mentioned prediction methods are presented further.
## 3 Results
At the next stage of the work, we studied the effect of SRC and IKK\(\beta\) kinase blockers on the resistance of animals to hypoxic and ischemic damage in vivo. The effect of intraventricular application of appropriate blockers on the animals' tolerance to acute hypobaric hypoxia was investigated (Table 2). The survival rate of animals in the Control group (with intraventricular administration of PBS) was 33.3%. Both SRC and IKK\(\beta\) kinase blockade had no effect on survival. It is important to note that inhibition of SRC significantly increased animals' survival time at altitude compared to the Control (Control--3.77 \(\pm\) 0.81 min; SRC inhibitor--6.68 \(\pm\) 0.84 min). Moreover, the blockade of this kinase significantly increased the share of highly resistant animals (Control--8.3%, SRC inhibitor--50%) (Figure 3). Figure 2: Raster diagrams of spontaneous bioelectrical activity of primary cultures of nerve cells with the number of spikes per 50 ms (**top**), patterns of spontaneous bioelectric activity of primary cultures of nerve cells (**middle**), and the time of appearance of spikes in the burst of the network, recorded from the electrodes in ms (**bottom**) on day 7 after modeling hypoxia. Twenty-four h after the AHH modeling, the locomotor and orientation-exploratory activity of the animals was tested in the IR Actimeter. It was shown that the general level of locomotor and orientation-exploratory activity in animals did not change; however, the animals spent significantly more time in the central area of the field (Figure 4). It indicates a change in the natural level of anxiety of the animals since the normal reaction for mice, as for crepuscular burrowing rodents, is to leave brightly lit areas in a center of open space. The application of the studied inhibitors normalized the time and distance spent in the center of the arena to the level of the Intact group. The next step in assessing the neuroprotective effect of the selected kinases was to study the resistance of laboratory animals to ischemic damage. Ischemic stroke was modeled by performing unilateral carotid artery occlusion in mice. The survival rate of control animals was 83%. The survival rate of animals injected with SRC kinase inhibitor did not differ from the Control group. In IKK\(\upbeta\) kinase blockade, the survival rate was 66.6%. Ischemic damage was shown to cause a decrease in the level of orientation-exploratory activity in mice (the number of stances (upright postures) in the Intact group was 33 \(\pm\) 4.53, in the Control group--18.09 \(\pm\) 3.94). In the group with the administration of studied kinase blockers, this parameter did not differ from the corresponding parameters in intact animals. No significant differences were found for the rest of the studied parameters (Figure 5).
## 4 Numerical Modeling with Anisotropic Slate
### Results and Discussion of the SCB Tests
#### 4.3.2 SCB Results and Discussion
Figure 16 illustrates the fracture patterns and load-displacement curves measured from numerically and experimentally with the bedding angles of 30\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and divider  
Figure 16: Comparison of the fracture patterns and load-displacement curves measured the numerically and experimentally with bedding angles of (**a**) 30\({}^{\circ}\), (**b**) 45 \({}^{\circ}\), (**c**) 90\({}^{\circ}\), and (**d**) divider. type. The overall responses of the curves all show brittle failure as a sharp drop, and each numerical result agrees fairly well with the typical experimental results, respectively. Figure 16a,b clearly shows that the dominant fracture initiates from the notch tip and ultimately propagates to the upper loading point. While intersecting with the bedding planes, the crack usually diverted into the bedding planes and kinked back into the host matrix after a certain propagation distance along the weak planes. It was observed that the deflected tensile fracture along the bedding plane was very short under an inclined angle of 45\({}^{\circ}\), but a long deflected tensile fracture along the bedding plane occurred at about 30\({}^{\circ}\). The results show that the fracture patterns agree fairly well with the testing specimens. Figure 16c clearly illustrates the failure pattern of the main fracture crack under an inclined angle of 90\({}^{\circ}\), which is mainly manifested as a vertical straight-through crack from the notch tip to the upper loading end, which is essentially the same as the postmortem for the tested specimen. In Figure 16d, the numerical result of the divider shows that the crack propagation presents a small zigzag from the notch tip upward to the upper loading end, which is completely consistent with the fracture path of the typical tested specimen. From Figure 14 to Figure 16, it can be readily seen that as the oriented angle to bedding increases, the corresponding numerical peak load progressively decreases (223.5, 180.1, 158.4, 149.7, and 127.8 N), while the peak load for the case of divider type is 262.5 N, and the calculated numerical \(K_{IC}\) is shown in the following Figure 17.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications
### Measurements for Other Flaws (Taking Fiber Waviness as an Example)  
When CFRPs are molded, defects may be generated in the CFRP. Fiber waviness, a typical defect, refers to fiber deformation and is induced by axial loading of carbon fibers [55]. In thin laminates,  
Figure 5: Electrical resistance change method with two electrodes.  
out-of-plane motion of carbon fibers is limited, so in-plane waviness may occur [162]. However, under large temperature gradients along the thickness direction, out-of-plane waviness is induced in thick laminates [55; 163].  
[PERSON] and his colleagues [164] proposed a novel probe to detect out-of-plane and in-plane fiber waviness in unidirectional CFRPs and characterized the orientations with an ECT. The novel probe could detect in-plane fiber waviness (amplitude: 1.1 mm; length: 15.9 mm) in a thin unidirectional CFRP specimen at a sufficiently high working frequency. FEM was also used to verify the proposed method. Variations in phase and amplitude of the received signals obtained in numerical simulation were well consistent with experimental data. The probe could detect out-of-plane fiber waviness in a thick CFRP specimen. The out-of-plane fiber waviness was determined based on ring-shaped plots in complex plane. [PERSON] et al. [165] analyzed the obtained signal with extreme values at the edges and waviness vertex and proposed the possibility of precise waviness location identification.  
The variation of the electrical resistivity of CFRP specimens could be taken as a damage analogue and the related methods could be used in situ as a non-destructive technique (NDT) for continuously monitoring the working condition of CFRPs [58]. These methods could improve the quality of composite panels and monitor structural health of CFRP components [63].
# Design and Implementation of Industrial Accident Detection Model Based on YOLOv4
## 4 Evaluation and Consideration
### Detection and Judgment Precision  
From the object detection and risk judgment accuracies, it is confirmed that the actual model accurately identifies objects and determines hazardous situations. The formula for obtaining the precision is the same as Equation (1). In object detection, a true positive is when the object predicted by the model matches the object information directly labeled by the person in the JSON file; thus, the model is correct. A false positive is when the model does not identify an object, and the model is incorrect because the object information was entered in the JSON file.  
In contrast, in risk judgment, a true positive refers to a case where the model classifies an event as hazardous and correctly organizes it as a hazardous event in the JSON file. A false positive is a case where the model classifies the event as not hazardous but the judgment is wrong (i.e., a risk event exists in the JSON file and an actual risk has occurred). Table 8 lists the results regarding object detection and risk judgment precision.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Object Detection** & **Hazardous Situation Detection** \\ \hline True positive & 37,686 & 958 \\ False negative & 11 & 84 \\ False positive & 0 & 29 \\ True negative & - & 7870 \\ Precision & 100.0 & 97.06 \\ Recall & 99.97 & 91.93 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the object and hazardous situation detection.  
Figure 14: Total processing time.  
Looking at Table 8, the object detection accuracy is based on YOLOv4, so the precision and recall show good results. However, although the model showed 97.06% precision for the risk situation judgment, it showed a slightly lower result for the rate of recall. This represents a false negative, meaning that a situation was judged as hazardous in the model but not labeled as hazardous in the JSON file. Owing to these false alarms, the recall rate is slightly lower.
## 1 Introduction
In particular, since loose coupling cannot provide updates during periods of insufficient satellite visibility [5], thus causing decreased performance relative to the tight integration case, the role of adding GLONASS data to the different architectures is considered (an interesting application of GNSS/INS integration with loosely coupled strategy is performed in [20]). In a similar manner, the role of non-holonomic constraints, as well as aiding information from GNSS-derived heading and from height constraints must also be re-evaluated given the improvement in GNSS measurements availability. With this in mind, the main contributions of the paper are as follows: first, by adding GLONASS to existing GPS/INS systems, the role of the improved satellite availability on system accuracy is assessed in an urban environment. Second, the relative performance of loose and tight integration algorithms is assessed with and without GLONASS. In so doing, it is shown that in some cases the loose integration approach with GLONASS can yield similar performance to the GPS-only case with a tight integration. This information is useful to system designers since loose integration algorithms are generally easier to implement than their tight integration counterparts. Third, the benefit of non-holonomic constraints, GNSS-derived yaw aiding, and height constraints are also assessed in the presence of GLONASS to assess their benefits relative to the GPS-only case. Finally, it is worth noting that although the focus is on the role of GLONASS for operational reasons, the results presented should also apply to other upcoming GNSS such as Galileo and/or Compass. The rest of the paper is organized as follows: first, the relevant theories in the context of this paper are briefly reviewed with focus given to the different integration algorithms (loose _versus_ tight coupling) and the available aiding information (GNSS-derived heading and non-holonomic constraints). Second, the tests used to assess the performance of the various integration strategies are described. Results are then presented and analyzed, followed by a summary of the main conclusions.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 4 Discussion
### Computational Cost  
Although the considered semantic segmentation models for module detection require training in representative datasets, transfer learning techniques could be used to reduce training times. However, once the chosen model has been fully trained, the time required to perform inference to detect PV modules on new VHR images is not an intensive task. On the other hand, the proposed SA-based optimization of the tilt angle can be computationally demanding due to multiple iterations in the estimation of solar irradiance for each PV array. However, the irradiance estimation approach considered by the authors can be parallelized efficiently using GPUs, as the authors showed in [48], which can substantially reduce computational burden, especially when estimating shadowing effects. Furthermore, for a larger-scale deployment, the proposed methodology could be run within high-performance computing (HPC) environments, or distributed cloud platforms could be employed to handle more computationally demanding tasks. Finally, the proposed method does not require real-time deployment, as such optimization should be run only once for a given PV system, unless the effects from environmental obstructions change in the near future (e.g., new high-rise buildings or taller vegetation) or new PV systems are installed.