esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Analysis of Real-World Energy Blockchain Initiatives
## 3 Methodology
### Threats and Limitations  
There are many inherent threats and limitations associated with systematic approaches that relate to data collection, information extraction and data analysis. The primary threats and limitations associated with this research are summarised below;  
* **Relevance of identified projects:** this research uses energy blockchain initiatives from an existing systematic study, which means initiatives omitted from the original study shall be excluded from the analysis and results presented. However, given the original study was peer-reviewed, one can reasonably assume that (a) prominent projects and initiatives were captured, and (b) unbiased selection methods were used to identify initiatives.
* **Project selection and filtering:** the criteria chosen for filtering energy blockchain initiatives were derived solely from discussions between researchers, which naturally introduces the possibility of biases influencing the final dataset. However, such biases were largely mitigated using top-level and generic criteria to isolate energy blockchain initiatives that were associated with real-world cases. Indeed, only nine initiatives were filtered from the 140 initiatives presented in the original study.
* **Soft and inconsistent data:** superficial reporting of commercial real-world energy blockchain initiatives may affect the robustness of the final dataset. In particular, insufficient technology, engineering and business details could lead to misclassified characteristics. Although not realistic to completely mitigate data quality issues stemming from superficial reporting, encountering quality concerns served to highlight the need for improved information sharing and reporting to progress the research agenda.
## 3 ZTF Data Processing Overview  
The Infrared Processing and Analysis Center (IPAC) has developed PTF and ZTF Science Data Systems (ZSDS) [33]. These include data-processing pipelines, data archives, long-term curation infrastructure, and data retrieval user services. The ZSDS was created to provide a processing and archiving system capable of producing outputs of scientific grade. The data are analyzed in real time to provide real-time changes to the sky's information [20]. It is maintained as a repository for research works on numerous astronomical fields. These data are processed using a variety of processes to provide a variety of outputs for use in other scientific endeavors. There are nine pipelines, i.e., Raw Data Ingestion pipeline, Image Splitting pipeline, Calibration derivation pipelines (Bias-image Generation, Flat-field Image Generation), instrumental and photometrical pipeline, reference image pipeline, real-time image subtraction and extraction pipeline, light curve pipeline, and ZMODE: ZTF Moving Object Discovery Engine, which run on different timescales [19]. All astronomy data must be made publicly accessible to astronomers worldwide, as required by the grant agencies. These archival data are freely accessible to the public via the browseable web directory of the Infrared Science Archive (IRSA/IPAC) [34]. Through their online system, ZTF Science Exposure Metadata, calibration metadata, raw metadata, and reference image metadata can be accessed.
# Appearance-Based Gaze Estimation as a Benchmark for Eye Image Data Generation Methods
## 4 Methodology
### Evaluation Metrics  
Using a synthetic framework as a data augmentation tool requires certain key aspects to be maintained in the generated images. First, they should be photorealistic and follow the visual style of a given reference image. Otherwise, their perceptual features may suffer a domain shift from a distribution mismatch with the real data. Second, the output of a generative method should be consistent in content with the input reference image and its segmentation mask. Failure to achieve good content consistency will introduce systematic bias in feature labels. For instance, the ground-truth gaze vector of a Blender render will no longer be an accurate label for an image obtained by passing that render through a StarGANv2-based method with poor content consistency. This will most likely prevent any improvement in the performance of end-to-end models when fake data are mixed with the real samples in the training set.  
The Frechet Inception Distance (FID) is a metric proposed for assessing the perceptual quality of the generated images [63]. It is one of the most common metrics of photorealism used in the literature, as it was found to be consistent with human judgment [40]. The FID builds on the assumption that the perceptual features extracted from a set of images follow a Gaussian distribution. The Wasserstein-2 distance is then computed between the mean (\(\mu\)) and covariance (\(cov\)) of features extracted from the real and generated image sets (\(P_{r}\) and \(P_{g}\), respectively). The VGG-16 pre-trained on an ImageNet is typically used as a feature extractor \(\phi\). As a distance metric, the lower FID means better image quality, but the lowest achievable FID is data-dependent. The FID is then defined as follows, where \(tr()\) is the matrix trace operator:  
\[\text{FID}(\phi(P_{r}),\phi(P_{g}))=||\mu_{r}-\mu_{g}||+tr(cov_{r}+cov_{g}-2 \sqrt{cov_{r}cov_{g}}) \tag{1}\]  
The segmentation masks were used in this study as a proxy for image content. The most common metric of agreement between segmentation masks is an intersection-over-union (IoU), which is a ratio of the area of overlap between two masks and the area of their union. It was computed for every segmentation class (i.e., background, sclera, iris, and pupil) separately as a mean ratio across all masks. The single metric of content consistency for the whole data was computed as a mean over the per-class IoUs.
## 4 Model Simulations with FEM-3D
### Analysis of the Magnetic Field Density in the Airgap  
To analyze the magnetic field density in the airgap, we represent in Figure 9 its spatial evolution (third component) along the airgap, \(B_{gz}\) with FEM-3D. The distribution of \(B_{gz}\) was calculated over the central teeth and the lateral teeth, with three paths located exactly in the middle plane of the airgap. The blue line corresponds to the component z of the magnetic field density when it crosses the airgap over the central teeth. The other two lines (yellow and green), that are superimposed, belong to the third component of the magnetic field density when it crosses the lateral teeth. For all lengths of the airgap, measured in m in Figure 9, the value of \(B_{gz}\) over the lateral teeth was approximately half with respect to the value of \(B_{gz}\) over the central teeth and it was in the opposite direction (Figure 9).  
A similar result was obtained in [16] that included the measurements in the laboratory of the magnetic field density at the top of the lateral and central teeth using explorer coils located around each tooth. We have used these results from [16] to check the validity of our method.  
Consider the following points to design the magnetic field density along the airgap:  
* Figures 9 and 10 show that the useful magnetic flux only operated across the transverse sections and the longitudinal magnetic flux was canceled. Equation (15) shows this configuration of the transverse magnetic flux operating into the TFLIM. \[\varnothing_{useful}\approx\varnothing_{Tranversal}\rightarrow\varnothing_{ right_{truth}}\approx\varnothing_{left_{truth}}\approx\varnothing_{Central_{truth}}/2\] (15) where \(\varnothing_{useful}\) is the magnetic flux that is involved in the electromechanical conversion (Wb); \(\varnothing_{Tranversal}\) is the magnetic flux closed along transverse sections (Wb); \(\varnothing_{Central_{truth}}\) is the magnetic flux that crosses the central teeth (Wb); \(\varnothing_{right_{truth}}\) is the magnetic flux that crosses the right-sided teeth (Wb), and \(\varnothing_{left_{truth}}\) is the magnetic flux that crosses the left-sided teeth (Wb).
* The high values reached for magnetic flux density in Figure 10 were obtained due to the linear behavior of the B-H curve that did not include the saturation effect inside the ferromagnetic regions. These values were unreachable inside real ferromagnetic steel, but the goal of this simulation was to demonstrate that the only existing magnetic flux was the transverse. To validate this simulation in an experimental test it is necessary to decrease the excitation in order to work near to the saturation zone from the B-H curve.  
Figure 9: Distribution of the magnetic field density along the airgap.
## 3 Heliostat Curvature
### Spherical Curvature  
The focal length (\(f\)) and the radius of the sphere (\(r\)), as a function of the heliostat projected half-diagonal (\(x\)) and deflection (\(z\)), are given by the following (see Figure 4):  
\[(r-z)^{2}+x^{2}=r^{2}\ \ \ \Rightarrow\ \ r=\frac{z}{2}+\frac{x^{2}}{2z} \tag{3}\]  
\[\alpha_{max}=\arcsin\frac{x}{r} \tag{4}\]  
\[f=r-\frac{r}{2\cos\alpha} \tag{5}\]  
Unlike a parabolic curvature, the focal length in a spherical curvature is not constant and depends on the angle \(\alpha\) (see Figure 4). The maximum focal length \(f_{max}\) occurs in the center, where \(\alpha=0^{\circ}\) and \(f_{max}=\frac{r}{2}\). The minimum focal length \(f_{min}\) occurs at the border, where \(\alpha=\alpha_{max}\). When \(\alpha_{max}>60^{\circ}\), \(f_{min}\) becomes negative, because the parallel ray is reflected again before reaching the spherical axis.  
In heliostat fields, typical focal distances (\(f\)) are usually between 25 m and 250 m (spherical radius between 50 m and 500 m). As shown in Table 1 for a typical 200 cm \(\times\) 200 cm (diagonal \(\times\) diagonal) heliostat, those values correspond to deflections \(z\) between 1 cm (1%) and 0.1 cm (0.1%), and rim angles \(\alpha_{max}\) (see Figure 4) between 1.15\({}^{\circ}\) and 0.11\({}^{\circ}\).  
In our ray-tracing model, random points (simulating the points where the sun rays are reflected) are generated in a horizontal rectangle representing the projected heliostat. For each reflection point, a sun ray is generated, using the astronomical algorithm and random scrambling transformations described elsewhere [18]. Each reflection point and sun vector is then used to calculate the ray interception and corresponding normal vector with the curved surface, immediately below the rectangle. How to analytically calculate the ray interception with a parabolic or spherical surface, the corresponding normal vector, and output reflected vector is discussed in our previous work [19; 20].  
Figure 4: (**a**) Spherical radius \(r\) and focal length \(f\) (see [19] for details) are functions of heliostat deflection z shown in (**b**) and heliostat projected half-diagonal \(x\) depicted in (**c**). Random reflection points and incident sun rays are generated in the rectangle (in red) at height 0 (lateral and top views) before intercepting the heliostat curvature (in black) below, where the reflection actually occurs.
## 2 Constraints and Challenges of Agricultural Sprayer UAV
### Challenges  
A manually flown UAV needs an expert pilot for flying, which may lead to low machinery expertise and thus a decrease in spraying efficiency. Because of human error, autonomous sprayer UAVs are becoming more popular. Similar to agricultural autonomous vehicles, the autonomous sprayer UAV also follows a coverage route plan. Numerous studies have been conducted on route planning for agricultural field coverage. Some route planning research focus on different geometrical field shapes [62; 63; 64; 65], while others have been done without the concern of the geometry of the field [66; 67; 68; 69]. A typical path planning structure is shown in Figure 2.  
Figure 1: Agricultural sprayer UAV (Quadrotor).  
Figure 2: (a) Path plan without considering geometry [67]; (b) path plan considering geometry [63].  
[PERSON] et al. [70] proposed an algorithm of path planning from a 2D image for optimizing battery consumption. Using satellite imagery, other path-planning methods also have been constructed, which focus on coverage plans without larger obstacles visible from satellite [71; 72; 73]. [PERSON] et al. [74] also developed a path-planning method excluding obstacle circles for crop protection UAVs using satellite imagery. Figure 3 shows the filtered path plan excluding the visible satellite obstacles.
## 5 Conclusion  
This paper evaluates the tribological properties of an expanded graphite-alloy steel pair operating at high loads (200 and 650 N) under dry and humid conditions. It was found that:  
1. For the tested loads and operating conditions in dry conditions, the friction torque remained stable, whereas in humid conditions, cyclic variations were observed due to the application of water to the friction zone. Similar trends of change and behavior after the application of water are observed for COF. Under dry conditions, an increase in load from 200 to 650 N results in an increase in average friction torque of about 235%, and under humid conditions, by about 209%. In contrast, no clear average COF changes were observed for the operating and load conditions tested.
2. Compared to dry running conditions, humid conditions contribute to a decrease in friction node temperature of about 12% for a 200 N load and about 18% for a 650 N load.
3. Regardless of the load, compared to dry operating conditions, humid conditions contribute to significantly higher wear on the graphite surface. Water acts as a lubricant, reducing direct contact between surfaces while weakening the cohesive bonds of the graphite. For a load of 200 N, an increase in wear of about 1179% was recorded, and for 650 N, an increase of about 100%.
4. Compared to dry conditions, under humid conditions at a 200 N load, the mass applied to the steel surface increased by about 114%, while at 650 N it decreased by about 250%, indicating a dependence on the amount of deposited graphite on load and operating conditions.
5. After a dry friction cycle with loads of 200 and 650 N, an unevenly deposited graphite layer, traces of its delamination, and isolated wear products were observed on the steel surface. After a humid friction cycle at a load of 200 N, large areas of graphite are observed on the steel surface. The graphite takes on a characteristic layered structure, and local secondary plateaus and areas of microcracks in the graphite layers can also be seen. At a load of 650 N, only sparse and small areas of graphite are observed on the steel surface.
6. The research results obtained may be helpful for engineers looking for possibilities to increase the durability and efficiency of technical seals operating in demanding conditions.  
Conceptualization, A.R.; methodology, A.R. and K.L.; software, A.R., K.L., and L.W. validation, A.R., K.L., L.W., and K.P.; formal analysis, A.R., K.L., and K.P.; investigation, A.R., K.P., and L.W.; resources, A.R.; data curation, A.R. and K.L.; writing--original draft, A.R., K.L., and K.P.; writing--review and editing, A.R. and K.L.; visualization, A.R. and K.P.; supervision, A.R., L.W., and K.L.; project administration, A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
COF Coefficient of Friction
# An Attention-Based Method for Remaining Useful Life Prediction of Rotating Machinery
## 2 Attention-Based RUL Prediction of Rotating Machinery
### Attention-Based GRU for RUL Prediction  
A gated recurrent unit (GRU) is a variant of an RNN. Conventional RNNs suffer from the problems of exponential explosion of weights and vanishing gradients and cannot capture long-term relations in the time sequence. However, GRU provides a solution to these problems by introducing gates. Moreover, compared with LSTM, GRU has fewer parameters and is therefore easier to train [21]. One weakness of GRU is that it cannot calculate similarity weights between input vectors and network hidden states. If the attention mechanism is introduced to GRU, the model is can discriminate features, assign different weights to them, and extract additional important information. In this way, the model can produce more accurate results without increasing the computing overhead or memory load of the model.
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 2 Key Features in the Energy Internet
### Communication Infrastructure
Based on the importance of the establishment of the access or the importance of the main data (payloads), several MTC technologies are used. For example, in the Long-Term Evolution (LTE) standard, to establish the connection of User Equipment (UE) with a higher level Evolved Node B (eNodeB), it is possible that a collision happens during the process [54]. The collision may happen if two or more UEs try to send preambles simultaneously. Then, as eNodeB is not able to recognize a collision happens, it will send a Random Access Response (RAR) to the recognized UEs. To increase the chance of successful connection, several attempts will be done by UEs accordingly. This access reservation procedure has a probabilistic characteristic, and the packet may be lost during the data transmission both in the uplink and downlink processes, or even failure and limitations in the system bottlenecks [54; 55]. Different perspectives of MTC challenges are defined and tried to overcome in the third Generation Partnership Project (3 GPP) by different solutions and technologies like LTE and Low Power Wide Area Network (LPWAN) introduced as NarrowBand IoT (NB-IoT) [56; 57]. Aside from probability characteristic of communication systems, especially wireless communication systems, coverage range, throughput, latency, capacity, power efficiency, and complexity are the issues raised in this field [58].
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
### Signal Model  
Say that a signal \(\mathsf{S}\) is a series of rank \(r\) if its \(L\)-trajectory matrix \(\mathsf{S}=\mathcal{T}(\mathsf{S})\) is rank-deficient and has rank \(r\) for any \(r<L<N-r+1\). It is known that the definition of the series of rank \(r\) is equivalent to the equality to the rank \(r\) of a trajectory matrix with \(L=r+1\) if \(N>2r\) [Corollary 5.1] in [15]. If a signal has rank \(r\), we call it a low-rank series.  
For an infinite time series \(\mathsf{S}\) of rank \(r\), there exists a governing linear recurrence relation (LRR) of order \(r\): \(s_{n}=\sum_{i=1}^{r}a_{i}s_{n-i}\), \(n=r+1,\ldots,N\), \(a_{r}\
eq 0\) [Chapter XVI, Section 10, Theorem 7] in [16]. A well-known result that specifies the explicit form of LRR governed series in parametric form is: \(s_{n}=\sum_{i}P_{i}(n)\exp(a_{i}n)\cos(2\pi\alpha_{i}n+\psi_{i})\), where \(P_{i}(n)\) are polynomials in \(n\) [Theorem 3.1.1] in (cf. [17] and [Theorem 5.3] in [3]).  
The rank of a signal is referred to as the model order, since in the complex-valued case, a sum of \(r\) complex exponentials has rank \(r\). Consequently, the rank estimation problem is known as model order selection.
## 4 Conclusions  
This paper presents the experimental study on vortex-induced vibration of risers with the following parameters: material properties (modulus and density), end condition, top tension force and flow velocity. Based on the variables of each parameter, 36 experiment cases were carried out in total. The natural frequencies of each riser situations, the response microstrain for all experiment cases, and the frequency of VIV for the risers were obtained. According to these results, the effect of each parameter was discussed and the grey relational grade of these parameters on the amplitude of VIV for riser was calculated using grey relational analysis.  
It was found in this paper:  
1. The modulus of the riser, number of constraints and the top tension force is positively correlated with the natural vibration frequency.
2. The increase of modulus, number of constraints and top tension force and the reduction of velocity lead to a decrease in the amplitude of VIV for riser.
3. The lock-in phenomenon does not happen for Al riser under all experiment cases, while for the PMMA riser and the UPVC riser, the lock-in phenomenon occurs in specific experiment cases.
4. The S-F end condition leads to higher frequencies of VIV for the risers.
5. The grey relational grade of the influence on the amplitude of VIV for riser are: \(r_{02(\text{velocity})}>r_{04(\text{constraints})}>r_{05(\text{density})}>r_{03 (\text{tension})}>r_{01(\text{modulus})}\).  
Conceptualization, C.W.; Methodology, C.W. and Y.C.; Validation, C.W. and Z.J.; Formal Analysis, C.W and Y.C.; Data Curation, Y.C. and S.G.; Investigation, C.W., Y.C. and M.S.; Writing--Original Draft Preparation, C.W. and S.G.; Writing--Review & Editing, C.W. Supervision, C.W.  
This research was funded by Shandong Provincial Natural Science Foundation (ZR2017 LEE031), National Natural Science Foundation of China (51806130), Science and Technology Development Project of Zibo (2017 KJ040017), and Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (BS2014 SF013).  
The authors declare no conflict of interest.
# Composite Foundation Settlement Prediction Based on LSTM-Transformer Model for CFG
## 2 Monolithic Framework  
Based on foundation settlement monitoring data, an LSTM-Transformer model for settlement prediction was constructed, as shown in Figure 1. The advantage of this model lies in its ability to deeply mine the sequence-related features hidden in foundation deformation monitoring data. It achieves the integration of temporal features and influencing factors, dynamically measures the contribution of each factor to the deformation, and explores the reasons behind changes in the deformation effects. By fully utilizing the rich dataset of subgrade settlement measurements for post-construction foundation settlement prediction, this model can provide references for subgrade engineering scheme design, saving the time and financial resources required for construction.
# Special Issue on \"Mapping and Monitoring of Geohazards\"
## 1 Introduction  
According to the Emergency Events Database ([[https://public.emdat.be/](https://public.emdat.be/)]([https://public.emdat.be/](https://public.emdat.be/))), in 2019 at least 51 geohazards in the form of earthquakes, landslides, and volcanoes were reported globally, claiming the lives of 1010 people, affecting \(\sim\)1.7 million others, and costing 2.6 billion US$. There is therefore an urgent demand to better understand the risk and mitigate the human and socio-economic impact of geohazards, which is currently being supported through use of a wealth of earth observation (EO) data and products that are either freely available or more readily accessible [1; 2; 3]. Better disaster management strategies in which new EO technologies and data have been incorporated have likely contributed to the decreasing trend between 1900-2008 and 2009-2019 in the average number of deaths (\(\sim\)1100 vs. \(\sim\)500) and people affected (\(\sim\)89,000 vs. \(\sim\)79,000) per geological disaster event.  
With this in mind, this Special Issue was introduced to showcase some of the latest EO-based research on the 'Mapping and Monitoring of Geohazards'. A set of diverse state-of-the-art methodologies are presented, which can be applied to the mapping and monitoring of geohazards to ultimately help build more resilient societies. A total of 12 papers were accepted for publication in the Special Issue, addressing a variety of issues associated with earthquakes, landslides, and volcanoes.