esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Summary of Applications  
As noted, the L8/OLI and S2/MSI data have been applied in different ways, and a summary can guide new users (Figure 8).  
We noted a prevalence of exploring the quality of the L8/OLI and S2/MSI data to calculate VIs, extract phenometrics, biophysical parameters, and select temporal or spatial features. Supervised classification was the principal classification approach adopted in the reviewed papers, by machine learning techniques, deep learning, and artificial neural networks. Automatic classification is growing and can be further explored. Big data analytics and multi-sensor approach expanded recently by the advent of new technologies, such as data cubes, ARD, and cloud computing, in very efficient strategies to implement data fusion, surface reflectance transformation, atmospheric correction, spatial and temporal segmentation, gap filling, and cloud masking algorithms. Jointly with integration methods, this enables multi-temporal and dense time series analysis, improving data mining, and different clustering and compositing techniques. These connections can guide new users to the best practices to apply the L8/OLI and S2/MSI data in future studies.  
Figure 8: Scientific network mapping summarizing applications and approaches adopted with L8/OLI and S2/MSI data. Colors indicate the cluster in which each term was related the most. Lines represent co-occurrence link strength among terms.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 3 Data Sources and Benchmarking
### Benchmark Datasets  
As shown in the previous section, many application areas for stream classification exist. To compare the performances of algorithms across different scenarios, one usually utilizes synthetic, or real-world benchmark datasets [13]. Tables 2 and 3 provide an overview of general-purpose datasets for both categories. For an extended overview, publicly accessible platforms and repositories such as OpenML (see www.openml.org (accessed on 5 September 2022)) by [32] or the UCI Machine Learning Repository (see www.archive.ics.uci.edu (accessed on 5 September 2022)) can be used. It should be noted, though, that the datasets available on these platforms are primarily used in static supervised learning. A recent survey by [33] includes many of the datasets listed below, as well as a comparison of which dataset was used most often in previous studies by other researchers.  
Figure 3: Distribution of outlet disciplines and data types in our literature base.  
The content of Tables 2 and 3 has been derived from the surveys of [4; 34; 35; 36; 37; 38; 39]. Furthermore, the works of [40; 41] about multilabel stream classification as well as semi-supervised problems instances have been included, respectively. In addition, ref. [42] proposes a process for generating artificial problem instances and thus presents several synthetic generators and datasets in our tables. These summarizing works serve as a basis for our tables, since they generally focus on stream classification, covering a broad range of application areas. Other works focus on more specific application scenarios, and thus, they are briefly discussed below:  
Refs. [43; 44] focus on the task of intrusion detection, while ref. [28] give an overview of datasets including medical images. Here, for example, ultrasound images can be used to detect anomalies early. For human activity recognition problems, refs. [13; 14] provide an extensive overview. Activity recognition problems encompass images of cameras that capture people's movements to, for example, monitor patients in hospitals or detect threats via video surveillance in public spaces [14]. For activity recognition problems, the Center for Advanced Studies in Adaptive Systems (CASAS) (see [[http://casas.wsu.edu/](http://casas.wsu.edu/)]([http://casas.wsu.edu/](http://casas.wsu.edu/)) (accessed on 5 September 2022)) also provides a collection of datasets. Ref. [8] list datasets in the IoT domain, while ref. [45] describe datasets for text detection and recognition in images. Although social media is another essential application for text recognition in stream classification, no benchmark exists. This is partly due to the network's strict legal data-sharing restrictions. Ref. [46] propose not to share the data but the algorithms to solve this lack of opportunities for algorithm performance comparison. Then, the researcher holding the data can run all algorithms on one machine based on his or her data set and can publish the comparative results.
## 3 Detection Method Design and Implementation
### Adaptive Adjustment Algorithm for Reference Baselines  
According to the analysis above, if the amount of change in geomagnetism and received signal strength is greater than decision thresholds, it can be considered a vehicle is detected [19; 20]. However, the geomagnetism and received signal strength are easily affected by environmental factors such as temperature and humidity, which cause the reference baselines to drift and affect the vehicle detection accuracy. In this paper, we use the following weighted average method to update the reference baselines:  
\[B(n)=\left\{\begin{array}{c}B(n-1)*(1-\alpha)+\alpha*A(n)\ (n>0\ and\ no\ vehicle\ exists)\\ B(n-1)\ (n>0\ and\ vehicle\ exists)\,\\ A(n)\ (n=0)\end{array}\right. \tag{4}\]  
where \(n\) is the serial number of sampling point. Where \(\alpha\) is a weighting coefficient and the value of \(\alpha\) is 0.05 in the paper. \(B(n)\) and \(B(n-1)\) is the current and previous reference baseline of geomagnetism or received signal strength. \(A(n)\) is the current sampled data of geomagnetism or received signal strength. From Equation (4) we can see that the first sampled value will be used as the reference baselines. After that, the reference baselines only can be updated when no vehicle is detected. Otherwise, it will remain unchanged.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.4 SAR  
SAR images are being widely used to map OSC dynamics and their variability in any weather conditions and at any time of day/night [116; 122; 136]. SAR-based OSC estimations are mainly obtained using two approaches: (1) feature tracking and (2) Doppler-based techniques [136]. The feature tracking methods compare sequential SAR images by measuring a similarity measure (e.g., correlation) between those images [137]. In the best case, the outputs of these methods have the same spatial resolution as the SAR images. On the other hand, the Doppler-based techniques can be divided into two groups based on their design, implementation, and performance: (1) the Doppler Centroid Anomaly (DCA) and (2) along-track InSAR [136]. The DCA methods model OSC at a coarse resolution (e.g., 1-2 km) with only one SAR image and are based on the Doppler shift concept [136; 138]. These methods estimate OSC by determining the line-of-sight rate of the scatterers based on the Doppler shift [112; 139]. On the other hand, along-track InSAR techniques are widely utilized to generate high-resolution OSC maps with two SAR images [136]. The spatiotemporal decorrelation and atmospheric delays strongly reduce the performance of these methods. Therefore, it is necessary to select multitemporal SAR images with appropriate temporal and spatial baselines to achieve better results from these methods. Multiple studies have investigated the potential of SAR-based methods for OSC retrieval. For instance, [PERSON] et al. [140] proposed a new technique to provide maps of shallow water topography and OSC using SAR images. In this method, OSW and OSC were estimated by fitting the observed SAR signals to a simulated shallow water topography radar signal in an iterative procedure. The results showed the high efficacy of the proposed methods. Moreover, [PERSON] et al. [141] investigated the potential of Sentinel-I SAR data for OSC mapping in the Skagerrak Sea with a focus on the Norwegian Coastal Current. They implemented several postprocessing algorithms for scalloping removal and the absolute and interbeam bias correction in the Sentinel-I data. The results showed that the retrieved OSC velocity corresponded well with a regional ocean circulation model with values of \(\approx\)0.8 m/s.
## 2 Experimental Details  
### Test Specimens and Ion Implantation  
The pure titanium TA3 was cut into block samples of 15 mm \(\times\) 15 mm \(\times\) 4 mm. Prior to Nb ion implantation, the samples were prepared by standard metallographic technique to the extent that the surface roughness was below 0.2 \(\upmu\)m. Following this, they were polished to a mirror finish with a silica suspension, washed by ultrasonic in anhydrous ethanol and finally dried in air. Niobium (Nb, 99.99 wt%) ions were injected at an extraction voltage of 125 keV and a vacuum of \(10^{-3}\) Pa with a dose of \(6.85\times 1016\) ions/cm\({}^{2}\).
###### Abstract  
Rotating machinery is fundamental in industry, gearboxes especially. However, failures may occur in their transmission components due to regular usage over long periods of time, even when operations are not intense. To avoid such failures, Structural Health Monitoring (SHM) techniques for damage prediction and in-advance detection can be applied. In this regard, correlations between measured signal variations and damage can be inspected using Artificial Intelligence (AI), which demands large numbers of data for training. Since obtaining signal samples of damaged components experimentally is currently unviable for complex systems due to destructive test costs, model-based numerical approaches are to be explored to solve this problem. To address this issue, this work applied an innovative hybrid Finite Element Method (FEM)-analytical approach, reducing computational effort and increasing performance with respect to traditional FEM. With this methodology, a system can be simulated with accuracy and without geometrical simplifications for healthy and damaged cases. Indeed, considering different positions and dimensions of damages (e.g., cracks) on the tooth roots of gears can offer new ways of damage investigation. As a reference to validate healthy systems and damage cases in terms of eigenfrequencies, a back-to-back test rig was used. Numerical simulations were performed for different cases, resulting in vibrational spectra for systems with no damage, with damage, and with damage of different intensities. The vibration spectra were used as data to train an Artificial Neural Network (ANN) to predict the machine state by Condition Monitoring (CM) and Fault Diagnosis (FD). For predicting the health and the intensity of damage to a system, classification and multi-class classification methods were implemented, respectively. Both sets of classification results presented good prediction agreement.  
components of a system [4]. Not only does it increase economic efficiency [5], it can also increase safety for humans and machines [6]. The detection of a change in the vibration indicators of a rotor system can be approached by employing signal processing methods, which are required to highlight information characteristics prior to load identification [7]. The current state of the art related to the analysis of vibration signals for FD foresees the periodic monitoring of these processed signals and the comparison of them over time. The monitoring of vibration spectra, specifically, can be considered a pattern recognition task. This is historically a primary application of Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANN) [8]. Thus, by using AI methods, emphasis can be given to the fact that when vibrations are used to extract features for FD, the correlation between the measured signal and the current status of the structure of the machine is very important [9].
Moreover, one of the contributions is to analyze the firmness of the spectrum by coinciding with the DFT series. The findings showed that the suggested system outperforms the current technique in terms of BER, PSNR, CC, and MMSE.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Components
#### 2.2.2 Protocol Layer  
The protocol layer consists of communication protocols and wireless standards used to enable communication for wireless devices. The first dedicated standard for Wireless Body Area Networks (WBANs) is the IEEE 802.15.6 released in 2012 [31]. IEEE 802.15.6 is designed to support both medical and non-medical applications and can be easily configured based on application requirements [50]. It ensures communication inside and around the human body and is specifically designed keeping in mind sensor devices that consume less energy and have a low transmission range. Another upcoming standard in this context is the European Telecommunications Standards Institute (ETSI) SmartBan [27; 30]. In addition  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 4: Implantable and Wearable IoHT Devices.  
to IEEE 802.15.6, there are other commonly used standards and technologies such as IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (ZigBee), IEEE 802.11 (WiFi) [35], Bluetooth Low Energy (BLE), NFC, LoRaWAN, UWB [36], RuBee [51], and Z-Wave [52]. The choice of the standard used is based on many factors like data rate, transmission range, number of devices supported, interference due to the coexistence of different technologies, etc. The technical details about these protocols, such as frequency, communication range, data transfer rate, energy consumption, and its security features, are summarized in Table 3.
## 3 Materials and Methodology
### Preprocessing the Categorized Signals
#### 3.3.4 Preprocessing Implicit Signals  
Implicit signals are sequential data such as time spent on modules, click patterns, and engagement trends. These signals require preprocessing techniques suitable for time-series data.
# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 1 Introduction
Through surveys to understand the energy-saving awareness in the retail sector and employing deep learning techniques to analyze whether the aggregator model can effectively reduce electricity consumption for retailers, the experiments demonstrate significant positive results. The findings provide valuable insights into sustainable energy management practices for the retail sector, which could significantly impact energy consumption and carbon emissions. By leveraging the potential of DSM, retailers can reduce energy costs, promote environmental sustainability, and enhance their market competitiveness. The retail industry's role in the economy underscores the need for energy management to ensure sustainable and profitable operations. With a conducive environment for DSM, the retail industry has an opportunity to adopt innovative energy management practices that can support national energy conservation and carbon reduction goals. In summary, demand-side management (DSM) in the retail industry is a crucial aspect of industrial energy management. The willingness to save energy may vary across regions, and there is a need for a model or approach to serve as empirical evidence for the feasibility of DSM. This study introduces a novel DSM model that proactively investigates its potential applicability to the retail industry in Taiwan and validates its feasibility through rigorous small-scale experiments. While the analytical model used in the study is unable to explain how individual stores consume or conserve electricity, it does enable aggregators to quickly select which group of retailers to approach for demand reduction when the power company needs it. The proposed approach has yielded positive outcomes under simulated conditions, and it is expected that this model will aid the industry in saving energy and achieving the corporate philosophy of sustainable ESG (environmental, social, and governance) operations in the future.
## 3 Evaluation Method  
As mentioned in Section 2.6, the current evaluations of decision-map techniques are limited. Due to inevitable errors in the decision-map creation process (detailed next in Section 3.1), using the classifier's accuracy to gauge a decision map's quality is neither appropriate nor comprehensive. Simple visual inspections of a decision map are equally limited in gauging its ability to correctly capture a classifier's behavior since we do not usually know this ground-truth behavior upfront. We aim to improve on this by proposing an extensive set of both quantitative and qualitative evaluations, each characterizing a different desirable property of decision maps, as described below.
## 2 Theoretical Insight
### Negative Stiffness from Buckled Euler Beams  
The different components are drawn in Figure 1. The first element considered is the beam with initial imperfection \(w=Q_{0}\), length \(L\), depth \(b\), and thickness \(h\). As for the case of a straight beam, the beam will not deform until the applied load reaches the critical buckling load \(P_{e}\), but in case of initial imperfection, a corrected expression can be found between the end shortening \(\delta\) and the axial force \(P\), as derived in [34]. Assuming \(w<0.2L\), the force displacement relation is given by (1)  
\[P=P_{e}\left(1-\frac{\pi Q_{0}}{L}\left(\left(\frac{\pi Q_{0}}{L}\right)^{2}+4 \left(\frac{\delta}{L}\right)\right)^{-1/2}\right)\left(1+\frac{1}{8}\left( \frac{\pi Q_{0}}{L}\right)^{2}+\frac{1}{2}\left(\frac{\delta}{L}\right)\right) \tag{1}\]  
with \(P_{e}=nEI(\pi/L)^{2}\) the Euler critical load of the beam with \(n=4\) in case it has both ends clamped, \(EI\) is the bending stiffness, \(L\) is initial the length of the beam.  
The axial force \(P\) is plotted in Figure 2 as a function of the axial displacement \(\delta\). Both quantities are normalized. It is also plotted for different values of initial imperfection, ranging from \(Q_{0}=0\) (straight beam) to \(Q_{0}=0.2L\) (initially curved beam). For the straight beam, the applied displacement results in a linearly growing force while specifying an initial imperfection leads to (i) a sudden decrease in force for very low values of displacement and (ii) a quasi-linear increase in force for large displacement values, which is gradually decreasing when increasing the imperfection. This observation confirms the softening of the system as it experiences an axial force.  
Figure 1: Compressed Euler beam clamped at both ends, physical configuration of the negative stiffness resonator at rest, and working state with initial displacement.  
zero stiffness, although this is not fully reached due to the limit of the analytical description. However, setting the imperfection to its maximum value (admissible by the model) and increasing the horizontal displacement produces a sudden change in the resulting force. This observation confirms the softening of the system as it experiences an axial force.
# Wind-Induced Response Control of High-Rise Buildings Using Inerter-Based Vibration Absorbers
## 4 Wind-Induced Response Mitigation Using Inerter-Based Vibration Absorbers
### Effects of the Inerter-Based Vibration Absorbers on Wind-Induced Accelerations
Based on Equation (20), the factor of vibration-absorbing \(F_{var}\) related to the top-floor acceleration response is evaluated and listed in Table 5 for a few emblematic wind directions and loading conditions. Figure 17: Profile of extreme wind-induced acceleration response at the \(x\)-axis along the building height corresponding to three different wind directions: (**a**) 0\({}^{\circ}\) wind direction; (**b**) 45\({}^{\circ}\) wind direction; (**c**) 90\({}^{\circ}\) wind direction. Figure 18: Extreme top-floor acceleration response at the \(x\)-axis corresponding to wind directions ranging from 0\({}^{\circ}\) to 345\({}^{\circ}\). In all wind directions, the TID dramatically reduces wind-induced top-floor accelerations. The worst vibration-absorbing effect of TMDI takes place for 180\({}^{\circ}\) wind direction, where the corresponding \(F_{\text{vu}}\) is 38.09% and is 1.6 times than that of the TMD having same physical mass. Generally, structural engineers are mainly concerned about the maximum extreme acceleration response of top floor, which may cause discomfort to residents. The maximum value of the acceleration of the original structure is 0.1232 m/s\({}^{2}\) corresponding to a wind direction of 75\({}^{\circ}\). The TMD, TMDI and TID can reduce such extreme acceleration value of more than 40%, namely from 0.1232 m/s\({}^{2}\) to 0.0701 m/s\({}^{2}\), 0.0685 m/s\({}^{2}\) and 0.0602 m/s\({}^{2}\), respectively. These results show that the TID has a significant acceleration-reduction effect due to the enormous inertia benefitting from the inerter device, despite employing the same physical mass of the TMD. An important aspect for practical implementation of TMDI/TID systems is the force generated by the inerter, as bigger inertances bring also higher forces that are difficult to handle in a conventional structure [59]. In the present example, the maximum resistance force produced by inerter is 5319 kN at wind direction of 330\({}^{\circ}\). Such requirement for inerter force can be practically implemented by installing several parallel inerter devices as shown in Figure 1.
## 3 Results and Discussions
### Mean-Squared Displacement (MSD) as a Function of Time  
The mean-square displacement (MSD) characterizes the dynamic behavior of particles by showing how far, on average, they move from their initial position in a given time. At sufficiently large times, the MSD has linear dependence on time, indicating the establishment of a diffusion mode of particle motion (Figure 6).  
At low temperatures (\(\Gamma\) is large), the MSD of dense beryllium calculated by the QMD method and the MSD of the Yukawa system practically coincide. In this regime, the system remains sufficiently correlated, and interatomic interactions play a dominant role. The Yukawa model reproduces the structure of the system well, indicating that the Yukawa one-component plasma model is an accurate approximation of the forces acting between beryllium ions.  
Figure 4: Radial distribution function (RDF) for dense beryllium at different temperatures (\(T=4000,\ 8000,\ 16,\!000,\ 32,\!000\) K) and density \(\rho=1.85\ \mathrm{g/cm^{3}}\) (metallic density) computed using the QMD method.  
As the temperature increases (\(\Gamma\) decreases), the kinetic energy of the particles begins to dominate over the interparticle forces, which leads to a change in their trajectories. In this regime, the MSD discrepancy between the models becomes noticeable, which is due to the simplification of interactions in the Yukawa potential. In real dense beryllium, the interaction of ions is determined not only by the screened Coulomb forces but also by more complex electronic structure effects, which are not taken into account in the  
Figure 5: Comparison of the radial distribution function (RDF) for dense beryllium from the QMD simulations (solid lines) and the Yukawa–MD calculations (dashed lines) at different temperatures: (**a**) \(T=4000\) K, \(\Gamma=500\), \(\kappa=3\); (**b**) \(T=8000\) K, \(\Gamma=240\), \(\kappa=3\); (**c**) \(T=16\),000 K, \(\Gamma=120\), \(\kappa=3\); (**d**) \(T=32\),000 K, \(\Gamma=60\), \(\kappa=3\).  
Figure 6: Mean-squared displacement (MSD) for dense beryllium from the QMD and Yukawa–MD simulations at different temperatures.  
Yukawa model. This leads to the fact that in the QMD simulations, the particles can show a somewhat different diffusion character than in the Yukawa model.  
Nevertheless, despite these differences, the QMD and Yukawa-MD results for the MSD show similar trends and are in overall good agreement, which confirms the adequacy of using the Yukawa model as an approximate but sufficiently accurate method to describe the dynamic properties of dense beryllium in the considered temperature range.