esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 4 Experimental Studies
### Evaluation of Generative Models
#### 4.1.3 Evaluation Results  
The evaluation result of the **aug39k_DL_BN_60+60 ep** and **aug39k_PM_IN_15 ep** models are mostly derived from our previous paper [7], whereby the **aug39k_DL_BN_60+60 ep** model is not directly addressed in the paper but has been excessively evaluated in experiments conducted to obtain the optimal training hyperparameters. In order to compare \"directed line\" (DL) and \"pointing minutiae\" (PM) encoding approaches, we report the evaluation results for three model snapshots (15, 30, and 55 training epochs) with **aug39k_PM_IN** and **aug39k_PM_DL** generative models.  
**Realism of Synthetic Fingerprints.** First, we discuss the NFIQ2 scores of the test datasets, which are shown in Figure 7. The NFIQ2 scores of original Anguli (left column) and URU fingerprints (right column) are taken as a reference. Our general observation is that the visual quality of the reconstructed fingerprints mostly depends on training samples. Since we use high-fidelity CrossMatch fingerprints for training, the mean NFIQ2 values in all cases are higher than 40. The quality of samples from which minutiae were extracted has an indirect influence on the visual quality of the reconstructed fingerprints, meaning that the missed or falsely localized minutiae may worsen the realism of a fingerprint pattern. We can clearly see that the distribution of NFIQ2 scores of Anguli fingerprints is narrower than those of reconstructed fingerprints, but the mean values of all distributions are very close, indicating the similar visual quality of fingerprints. In contrast, the distribution of NFIQ2 scores of URU fingerprints is broader and shifted to the left, indicating that the visual quality of URU fingerprints is significantly lower than that of reconstructed  
Figure 6: (**Top row**): fingerprints generated by Anguli; (**bottom row**): URU fingerprints from the FVC2004 DB2 A+B dataset.  
fingerprints. These observations apply to all generative models independently of the snapshot selection. Based on diagrams, no conclusion can be made about the superiority of the \"directed line\" or \"pointing minutiae\" encoding approach. However, depending on the encoding approach, different snapshots should be preferred if the goal is to obtain the lowest number of patterns with low NFIQ2 values. This fact is evident if reconstructing from URU fingerprints. In the case of \"directed line\" encoding, the model snapshot with 55 training epochs, and in the case of \"pointing minutiae\" encoding, the model snapshot with 15 training epochs, has no secondary peak in the low range of NFIQ2 values. We thus intuit that models with \"directed line\" encoding may benefit from further training.
## 3 Results and Discussion
Table 1 shows the results of the proposed algorithm and other similar approaches. The percentage values given in the table mean that if the algorithm detected one of the two occurring failures (the time of precise detection was not taken into account), the detection of this failure was 50%. Additionally, false detections caused by environmental disturbances are also included. The results show that the proposed algorithm can be a reliable method to detect conveyor damage in industrial cases. Other methods, although also based on similar activities (mainly frequency analysis), were developed for laboratory cases, mostly without tests in other conditions. For this reason, most of the methods that have been used to diagnose conveyors use thermovision or vibration, and sound-based methods are a definite minority, even though they are much simpler and cheaper to implement. This is even more important in the case of implementation on devices that perform automated inspections, such as the presented robot. The implementation of the acoustics-based algorithm requires only a microphone, unlike an expensive thermal imaging camera or the entire vibration  
Figure 9: Percentage of detection over time. measurement procedure, which requires the robot to locate the place to perform the measurement and connect the sensor to it, which is complicated. For the presented method to become a good alternative, it is necessary to conduct tests on a much larger scale, which will include various environmental conditions, types of damage, and disturbances.
## 2 Related Works
### Correlation Filter Based Trackers  
Over the past decade, CF-based trackers [32] have attracted more interest in the research of tracking, as well as attained very effective improvements in diverse challenges [9] and benchmarks [8]. Traditionally, correlation peaks are typically generated for each patch of interest in a frame while producing a low response to the background, which is often used as a detector of the desired model. Although tracking issue can be successfully resolved using these filters, they are not suitable for online tracking as the required training needs. This situation has been changed after the Minimum Output Sum of Squared Error (MOSSE) [13] filter has been proposed. By an adaptive learning method, the MOSSE tracker has robust and efficient results in tracking. According to the fundamental theory of MOSSE, multiple improved algorithms followed. For instance, [PERSON] et al. [14] improve the MOSSE filter by adopting the Circulant Structure of with Kernels (CSK). [PERSON] et al. [33] employ Color Name features that can convey color attributes to improve the tracking performance of the CSK tracker. Since high dimension of color features, the updating scheme [33] is adapted to reduce the dimension of feature by principal Component Analysis (PCA). An improved Kernelized Correlation Filter (KCF) [15] employed multi-channel features to replace raw pixels, which is the most prevalent filter widely adopted since it has good overall performance and high FPS rate. [PERSON] et al. [34] formulate a distribution scheme in a Bayesian optimization framework to alleviate drifting.  
By further coping to scale variation, SAMF [16] and DSST tracker [17] based on correlation filter have appealing performance, and have defeated the rest of the participating methods from aspects of accurateness and robustness in recent competitions. [PERSON] et al. [35] introduce a variant of SAMF based on water flow driven minimum barrier distance (MBD) algorithm. [PERSON] [18] has learned a model to improve the robustness of both color variations and distortions by combining two notations easily affected by complementary factors. To induce the boundary effects, SRDCF tracker [19] exploits spatially regularized elements in training to punish correlation modulus according to their position. Recently, convolution features have also been used for visual tracking tasks to improve tracking accuracy and robustness. Deep SRDCF [36] employs deep features of single convolution layer in Convolutional Neural Networks (CNN) for tracking, and Continuous Convolution Operator Tracker (C-COT) [37] extends it to multi-layers of convolution.
## 3 Controller and Observer  
This paper presents a novel methodology for the precise tracking of reference signals in multi-joint robotic arms through the implementation of an Objective Sliding Mode Control (OSMC) strategy, which integrates cutting-edge State-Space Control Network (SSCN) technology, as illustrated in Figure 1. The OSMC architecture is meticulously designed to achieve high fidelity in tracking predefined system state objectives. At the heart of the OSMC framework is the formulation of an objective sliding mode surface and the derivation of an equivalent control law, both tailored to substantially enhance control precision.  
Considering the prevalent uncertainties in various system parameters, this study introduces an advanced state observer utilizing SSCN technology. This observer adeptly mitigates these uncertainties, significantly enhancing the controller's robustness against external disturbances and markedly reducing the likelihood of system oscillations.  
Figure 1: Total control strategy.  
SSCN is instrumental in reducing the computational burden, thus enabling more rapid real-time processing. It also augments the effectiveness of the compensatory mechanisms within the control system. Additionally, SSCN ensures rapid correction of positional errors in the robotic arm's joints, achieving convergence to zero within a predefined finite time frame. The effectiveness of this approach is validated through extensive testing under both simulated conditions and actual operational environments, underscoring SSCN's capability to improve the performance and reliability of robotic control systems.
## 2 Performance of a Correct Thermography
### Conditions for Conducting Thermographic Inspections  
Substations are high voltage electrical facilities. Thermographic inspections are performed with the facility energized. In it, the distances between elements are smaller than in other facilities. Therefore, a series of requirements must be met to carry out the work: the personnel who can work must be qualified; and with adverse environmental conditions, the work cannot be carried out (wind, fog, rain, hail, or storm).  
The operator who performs the thermography has a plan of the substations to be inspected during the day. Depending on the time of day, the week of the year, and even the day of the week, circuit load conditions will vary at each substation. The most favorable condition for the detection of hot spots is when the nominal current circulates through the element. This current is the maximum that can circulate through that circuit and corresponds to the facility working at full load. However, that cannot always happen. Therefore, a series of minimum requirements must be met in the inspected element for the correct detection of hot spots [34]: the current that circulates through it must be greater than 20% of the maximum that can circulate; the measurement must be performed at least 15 min after being energized; and the tightening of the connections recommended by the manufacturer must be met. Otherwise, hot spots may not be identified and lead to erroneous conclusions.  
To detect hot spots, a minimum current circulating through the element is necessary. That load is set at 20% of the rated current. Having been crossed by the current, the element acquires the service temperature. At some point after the current begins to circulate, the service temperature is acquired and stabilized. For this reason, at least 15 min must pass from powering on. Subsequently, the change in the current that passes through it does not have as much influence as the one produced since it is de-energized. This is because current variations in a substation are usually not abrupt. Another important requirement that must be met is that the tightening torque of the element connections be those recommended by the manufacturer. Poor tightening can cause incorrect detection of the hot spot.  
Regarding the load, probably when the thermographic inspection is carried out, the nominal current does not circulate. However, it can be reached at any time. In addition, it must be considered that the higher the current through the element, the higher the temperature. Therefore, the results obtained with a given current must be extrapolated to the case in which the nominal current circulates.
# Energy Behaviour of Coal Failure under Uniaxial Cyclic Loading/Unloading
## 5 Discussion
### Prototype of Energy Behaviour Predicting Pattern  
As shown in Figure 16, it can be known that the criterion of the energy behaviour can better reflect the stable and fracture stage of the coal specimen. Stage I and stage II can be regard as a stable state in the failure process of the coal specimen. The demarcation point between stage II and stage III can be regard as the initial predicting point of the coal failure. Furthermore, it is also the turning point of the dissipate energy behaviour of the coal specimen. This point is also the boundary point of the energy state and stability degree of coal specimens.  
The dissipated energy demonstrates a sudden acceleration in stage III. Although coal specimens will not be completely unstable at this stage, it will have produced a large number of cracks and begun to gradually fracture. Then, the dissipation energy of the coal specimen grows faster. The stage IV is regarded as the critical fracture stage of the coal specimen, where the value of the stored dissipation energy is higher, but the growth drops obviously. The internal cracking of the coal specimen has been basically fractured and it is in the critical damage stage.  
The demarcation points between stage III and stage IV, namely the peak of the dissipate energy tangent factor, can be regarded as the predicting key point of the coal failure. It is also the inflection point that the growth rate of other energy behaviours slows down. Therefore, it is appropriate to take this point as the key predicting point of coal fracture. Moreover, it can well describe and judge the stable state of the coal specimen. Then, the parameters that are based on the dissipated energy behaviour also show a changing trend of slowing down or decreasing in a relatively short time.  
Additionally, many studies were not further enough in this paper, and there are still many problems which need to be further explored in the next studies. The stress gradient was 2 MPa and that is still relatively large. It is difficult to accurately capture the energy behaviour of the coal specimen in the critical failure state. Next, it will be important to continue lowering the gradient to improve the accuracy of the experiment Ten cycles have been carried out in each stage under the equal amplitude cyclic loading/unloading. This means it is not possible to study the detailed effects of repeated damage on the energy behaviour. Next, the stress gradient will be further reduced, and the number of cycles will be increased to improve the accuracy of the experiment.  
Figure 16: The energy behaviour predicting pattern of the coal failure process. The green line is used to delineate the different stages through which the coal samples in the diagram have gone. (**a**) Dissipative energy stage, \(k_{CF}\), \(k_{GF}\), \(k_{DF}\) and \(k_{TF}\) of the coal sample S1. (**b**) Dissipative energy stage, \(k_{CF}\), \(k_{GF}\), \(k_{DF}\) and \(k_{TF}\) of the coal sample S2.
## 1 Introduction  
The development of technology has initiated a transformation in our behaviors and habits. In contemporary times, activities such as shopping, watching movies, expressing opinions and managing accounts have migrated to digital platforms. The advent of databases, coupled with enhanced data storage capabilities, has facilitated the seamless recording of individual behaviors.  
Businesses, recognizing the value of data, increasingly adopt data-driven methodologies and predictive techniques to optimize user experiences, formulate effective marketing strategies and reach success targets. This data encompasses not only human behaviors but also instances of machine malfunctions, sensor-derived values from machinery, server logs and analogous information. The advancement of data science has resulted in substantial financial benefits for companies engaging in behavior prediction. Despite existing academic studies and applications in behavioral analytics, there is a notable gap necessitating an exploration of the effectiveness of predictive methods applied across diverse domains to anticipate individual behaviors.  
In this research, we undertake the task of predicting individual behaviors by leveraging a novel real-world dataset and an online transaction dataset generously shared by UC Irvine. Due to the impracticality of crafting autoregressive moving average (ARMA) models tailored for each person and the inherent limitations of buy till you die (BTYD) models, the efficacy of deep learning models assumes a vital importance. The precision of individual-level predictions holds significant implications for businesses, enabling them to distinguish between individuals based on their anticipated future behaviors and implement customized actions to enhance overall operational effectiveness. This approach, in contrast to prevalent industry practices relying on past behaviors for segmentation and decision-making, offers a forward-looking perspective. We propose a novel approach and conduct a comparative analysis of the performances of gated recurrent unit (GRU) and long short-term memory (LSTM) models.  
This paper is structured as follows: in Section 2, we provide a brief overview of previous studies. Subsequently, in Section 3, we describe the models applied in this study, followed by an explanation of the dataset and evaluation metrics in Section 4. Next, in Section 5, we present the results and compare the performance of the models. Finally, in Section 6, we examine the benefits and constraints of the models and provide recommendations for prospective research in the field.
## 2 State-of-the-Art and Related Topics
### Traffic Prediction  
Short-term traffic prediction is one of the most important topics in ITS research and practice. Aside from the ARIMA series prediction method, there have also been many other methodologies engaged for this purpose.  
The Kalman filtering method [24; 25], which is based on historical data and present data to predict a future state, has been widely used in the forecasting of traffic flow. However, its computational complexity is too high for complex urban traffic flow prediction. Neural Networks (NNs) models [26; 27] have also been utilized to predict traffic flow for their high prediction accuracy. The weak point of NNs is the long model training time. Other research studies have referred to Support Vector Machines (SVM) [28]. However, the error of SVM is high under the circumstance of peak periods and blocking traffic accidents compared with the Bayesian network [29].  
The K-NN method (K-nearest neighborhood) [30] performs well in short-term forecasting even when accidents have occurred. However, this algorithm has high complexity and needs a large amount of calculation when searching for class neighbors. Markov-based models [31] have also shown good performance on traffic flow prediction since the traffic condition at the next interval is closely related to the recent states. However, in these models, there are many states to consider. New technologies such as big data [32] and particle filtering [33; 34] have also been used in traffic predication and other urban mobility applications.  
Although these traffic prediction approaches have provided good prediction results in some scenarios, most of them are too complex or require a long training time. Thus, there have been respectable efforts put towards improving various ARIMA prediction models.
# Design and Optimization of a Compliant Morphing Trailing Edge for High-Lift Generation
## Acronyms and Symbols  
A/C & Aircraft  
ACTE & Adaptive compliant trailing edge  
AoA & Angle of attack  
CFD & Computational fluid dynamics  
CL & Lift coefficient  
CLEEN & Continuous Lower Energy, Emissions, and Noise  
C\({}_{\rm L}\) & Lift coefficient  
C\({}_{\rm L,MAX}\) & Maximum lift coefficient  
D & Deviation between the target morphed shape and the current one  
DOF & Degree of freedom  
EAP & Electric aircraft propulsion  
EMA & Electro-mechanical actuation  
f\({}_{\rm D}\) & Factor weighting the deviation from the target morphed shape  
f\({}_{\rm\lambda}\) & Factor weighting the buckling performance layout  
f\({}_{\rm s}\) & Factor weighting the stress level of the current layout  
f\({}_{\omega}\) & Factor weighting the modal frequency of the current layout  
FAA & Federal Aviation Administration  
Fact & Actuation force  
FE & Finite element  
FEM & Finite element model  
GHG & Greenhouse Gas  
HER & Hybrid electric regional  
HERA & Hybrid electric regional aircraft  
HERWINGT & Hybrid Electric Regional Wing Integration Novel Wing Technologies  
KTAS & Knots true airspeed  
\(\lambda\) & 1 st buckling eigenvalue  
LDO & Leonardo S.p.A.--Aircraft Division  
LE & Leading edge  
LoD & Lift over drag  
MAS & Morphing aircraft structures  
NA & Not applicable  
NASA & National Aeronautics and Space Administration  
P\({}_{1-11}\) & Optimization parameters  
PoliMi & Politecnico of Milan  
RANS & Reynolds-averaged Navier-Stokes  
s & Curvilinear abscissa  
\(\sigma_{max}\) & [PERSON] stress  
TE & Trailing edge  
TLAR & Top-level aircraft requirements  
TRL & Technology readiness level  
TUD & Technical University of Delft  
UHSS & Ultra-high-strength steel  
\(\omega\) & 1 st normal frequency
# Compaction Characteristics and Minimum Void Ratio Prediction Model for Gap-Graded Soil-Rock Mixture
## 3 A New Model for Predicting Minimum Void Ratio of Gap-Graded SRMs Incorporating Particle Breakage
### Validation of the Model  
In order to evaluate the model, we use the vibrating compaction test data of gap-graded SRMs with gap interval of 5-20 mm to obtain the model parameters like \(k\) and \(k^{\prime}\) by fitting \(k\) and \(k^{\prime}\) with \(D\). Together with the data of \(e_{c}\) and \(e_{f}\), we can predict the minimum void ratio of gap-graded SRM with gap intervals of 2-5 mm, 0.5-2 mm, and 0.25-0.5 mm using Equations (14) and (15). The model parameters are listed in Table 2. The comparison of predicted minimum void ratio and measured minimum void ratio by vibrating compaction tests is shown in Figure 17. It can be seen from Figure 16 that the predicted minimum void ratio is close to the measured values, which shows the validation of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Gap Interval** & \(\epsilon_{c}\) & \(e_{f}\) & \(k\) & \(k^{\prime}\) \\ \hline
2–5 mm & 0.433 & 0.442 & & \\ \hline
0.5–2 mm & 0.413 & 0.467 & \(k=0.094D^{2}-0.44D+0.49\) & \(k^{\prime}=-0.24D^{2}+1.11D-1.13\) \\ \hline
0.25–0.5 mm & 0.403 & 0.472 & & \\ \hline \end{tabular}
\end{table}
Table 2: Model parameters for the prediction of void ratio of gap-graded SRM with gap intervals of 2-5 mm, 0.5–2 mm and 0.25–0.5 mm.  
Figure 16: Fitting the material constants \(k\) and \(k^{\prime}\) with a quadratic polynomial function of fractal dimension \(D\).  
Figure 15: Changes of minimum void ratio of gap-graded SRM with \(f_{c}\).