esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
### Occurrence State of Trace Elements in Chalcopyrite and Its Indicating Significance
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline  & **F** & **SiO\({}_{2}\)** & **MgO** & **ZrO\({}_{2}\)** & **CaO** & **FeO** & **HfO\({}_{2}\)** & **SnO\({}_{2}\)** & **SrO** & **ThO\({}_{2}\)** & **Total** \\ \hline ZK2204-17-01 & 0 & 1.098 & 0.227 & 0.033 & 0.506 & 1.564 & 0 & 96.252 & 0 & 0.038 & 99.718 \\ ZK2204-17-02 & 0.131 & 1.769 & 0.166 & 0 & 0.527 & 1.706 & 0 & 95.086 & 0 & 0.022 & 99.407 \\ ZK2204-17-03 & 0.058 & 1.271 & 0.15 & 0.056 & 0.548 & 2.188 & 0.15 & 95.853 & 0.032 & 0.019 & 100.325 \\ ZK2204-17-04 & 0 & 1.207 & 0.152 & 0 & 0.384 & 1.332 & 0.156 & 96.38 & 0.024 & 0 & 99.635 \\ ZK2204-17-05 & 0 & 2.693 & 0.42 & 0 & 0.498 & 4.158 & 0 & 91.456 & 0.071 & 0 & 99.296 \\ ZK2204-17-06 & 0 & 1.844 & 0.216 & 0 & 0.488 & 3.093 & 0.388 & 94.169 & 0 & 0 & 100.198 \\ ZK2204-17-07 & 0.101 & 0.993 & 0.122 & 0.098 & 0.407 & 1.629 & 0.058 & 96.288 & 0 & 0 & 99.696 \\ KZ0001-05-01 & 0 & 0.571 & 0.076 & 0.033 & 0.535 & 0.393 & 0 & 98.272 & 0.008 & 0.008 & 99.896 \\ KZ0001-05-02 & 0 & 0.641 & 0.115 & 0.056 & 0.484 & 1.111 & 0 & 96.667 & 0.031 & 0 & 99.105 \\ KZ0001-05-03 & 0 & 1.605 & 0.162 & 0.089 & 0.61 & 0.99 & 0 & 96.381 & 0.013 & 0 & 99.85 \\ KZ0001-05-04 & 0 & 0.989 & 0.157 & 0.005 & 0.565 & 0.904 & 0.063 & 97.805 & 0 & 0 & 100.488 \\ \hline \end{tabular}
\end{table} 
Table 2: Electron microprobe analysis results of cassiterite in the Shuangjianzishan silver polymetallic deposit. in chalcopyrite reflects that chalcopyrite was formed in a medium- and high-temperature environment. There is a positive correlation between Sb and Tl (Figure 8a), which indicates that Sb and Tl may enter chalcopyrite through coupling substitution. Chalcopyrite in the Shuangjianzishan deposit has a high content of Ni (with an average of 530.23 ppm), but [PERSON] et al. [25] think that Ni is not obviously enriched in chalcopyrite, but exists in pyrite, so Ni may mainly exist in chalcopyrite as pyrite or pyrrhotite inclusions, instead of replacing Fe in chalcopyrite by isomorphism.
## 6 Analysis and Results
### AOIs and Resolution Factors in Geomorphology Classification  
Because there is no geomorphological map available for this study area, this research included exploration of geomorphological classification. In this section, the factors for classification were tested with four samples: AOIs 1 and 2, which differed in terms of scene coverage, and the resolutions for each scene, i.e., 250 and 1000 m (see Figure 2). Each DEM sample was classified using the methodology of [PERSON] and [PERSON] (2007); that is, the automated DEM pixel-base geomorphological classification based on slope, curvature, and texture. For the results, the units from each sample covered the difference coverage pixel. Thermal change detection was applied to detect the movement of the class change in each pixel between each sample, according to the changes in the AOIs and resolutions. The number of class changes in every direction case was counted; for example, the class of the pixel changed from 1 to 2 or 2 to 1, with the number of class changes and the change direction for each case.  
Changing the resolution mainly affects the changes within a major geomorphological group. For example, for the major geomorphological group of the weak rock plain (see Figure 3), which included unit numbers 11, 15, 9, and 13 (see Figure 4), changing the resolution affects the changes between those unit numbers. From V\({}_{\text{S30}}\) in Section 6.2, classes 11 and 15 have significantly different V\({}_{\text{S30}}\) values, reflecting a substantial difference in soil properties. Changes in the AOIs mainly affect the changes between the major geomorphology groups of the geomorphological units (see Figure 3), e.g., it shifts from mountain to weak rock plain, mountain to the high-land slope, or from weak rock plain to low-land plain. The AOI suitability is understood by comparing the results with an available geological map or other maps to confirm its boundaries. Therefore, a 250 m resolution AOI2 was selected for PBC.
## 1 Introduction
These techniques of acquiring water vapour data enabled major progress in understanding the processes happening in the atmosphere, while suffering from drawbacks, such as their low spatiotemporal resolution and high operating costs [9]. It is acknowledged that our climate and its extremes are changing [10]. Reliable forecasts of extreme weather and climate events, on short and long time scales, are critical in reducing the potential risks and related damages thereof [11]. Forecasting and monitoring the arrival times and path of extreme thunderstorms, as well as of tropical cyclones, are indispensable tools for issuing early warnings to the public and governments. A so-called Cevenol episode that occurred in the Gard region (Southern France) was studied by means of GPS data by [12]. GPS-derived PWV time series enabled the monitoring of the complete evolution of the convective system associated with the rainfall from the water vapour-accumulation stage through the stagnation of the convective cell, up to the final breakup of the system. The authors of Refs. [13; 14] studied a double thunderstorm cluster that developed above the Campania region on 12 October 2012. The storm broke in a heavy rainfall event, which severely damaged Napoli city (with a population of about one million inhabitants) and neighbouring areas. From the analysis of a subset of permanent GPS stations belonging to the NeVoCGPS (Neapolitan Volcanoes Continuous GPS) network, they found clear evidence of an increase in PWV a few hours before the storm. GPS observations in the vicinity of the heavy rainfall area were all consistent with the water vapour depicted in the infrared channel of the Meteo satellites (MSG) in the Euro-Atlantic area. [PERSON] et al. [15] studied the characteristics of hurricane Florence, which struck the Eeast coast of the USA in 2018, using a dense ground network of permanent GPS stations. They detected a rise in GPS-derived PWV occurring several hours before Florence's manifestation. In this study, data acquired on the dense GNSS NeVoCGPS network, as well as on a meteorological station co-located with one of the network sites were employed. The analysis of the longest time series (11 years) allows the estimation of climatological trends of increasing moisture content in the air, which has resulted in increased rainfall, e greenhouse effect and associated climatic feedback. Moreover, analyses carried out with different methods (differential and PPP) of data collected on two episodes of heavy rain are discussed, as well. GNSS-meteorology is, potentially, effective tool for understanding, modeling and predicting weather and climate extremes, and can contribute toward the implementation of more effective action for disaster risk reduction. The main goals of this study are: (1) to estimate the long-term temporal variation of water vapor content from the analysis of the NeVoCGPS data, and (2) to investigate, by means of different techniques of GNSS data analysis, the time evolution of PWV during three extreme weather events in the Neapolitan area.
# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
## 5 Data Analysis and Results
### R-Square (R2)  
R- square Measure the variance that is explained in each of the endogenous variables by independent variable/s (measure the explanatory power) Shmueli [73]. In-sample predictive power is another name for the R2 Rigdon [74]. R2 values range between 0 to 1, where higher values indicate stronger explanatory power. R2 values of 0.6, 0.33, and 0.19 are regarded as substantial, moderate, and weak, respectively [63; 65]. The obtained value of R2 is 0.256 for intention and 0.250 for pedestrian behavior. This shows that attitude toward the intention, subjective norms, and perceived behavioral control altogether explained 25.5% (moderate) variance in intention, and perceived behavioral control and intention explained 25% (moderate) variance in pedestrian behavior.
## 4 Results and Discussion
### Comparison of Our FC-YOLOv4 with YOLOv4 and YOLOv3
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total Parameters** & **Detection Time** & **Accuracy (mAp)** & **Size** \\ \hline FC-YOLOv4 & 63,959,226 & 33 milliseconds & 95.87\% & 266 MB \\ \hline YOLOv4 & 63,959,226 & 32 milliseconds & 95.80\% & 244 MB \\ \hline YOLOv3 & 61,545,274 & 32 milliseconds & 55.67\% & 234 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of parameters, detection time, accuracy, and size of three models. Figure 15: P-R curve. a single image to test the case of images containing more than one category. As seen in Figure 17o-q, our model can precisely identify the various categories inside each image. In our research of how popular object detectors such as YOLOv4 can be modified to detect objects, we identified architectural modifications that yield a significant performance boost over the original model at a relatively cheap cost, as the new model retains speed. The scenario in which we applied the suggested approach, namely autonomous racing, might benefit significantly from such enhancements. We not only enhanced the performance of the base model greatly in this study but also discovered some specialized techniques that may be applied to various applications requiring the detection of objects. Consequently, a modified model with pseudo-labeling outperforms a YOLOv4 class model while keeping a detection speed suitable for fashion applications. Finally, while this study reveals a sizeable empirical benefit of the recommended architectural adjustments, the study's consistency and generalizability may and should be further studied. For instance, the analysis would benefit significantly from more testing with diverse data sets and the issues that may occur when detecting screen printing logo images, for example. While we have established the utility of the various strategies presented, they can only be improved and better understood via application to several  
Figure 16: Detection results of YOLOv3, YOLOv4, and our proposed FC-YOLOv4. (**a**,**d**,**g**,**j**) YOLOv3 Detection Results; (**b**,**e**,**h**,**k**) YOLOv4 Detection Results; (**c**,**f**,**i**,**i**) FC-YOLOv4 Detection Results.
## 3 Greedy Scanning Data Collection Strategy
### Data Collection
#### 3.3.2 Sink Trajectory Planning
It also needs to adjust the parking time in each grid cell according to the collecting period counter values. There are three choices for the sink: the left column, the current column and the right column. The mobile sink chooses the column \(Cs=max(Counter_{left},Counter_{middle},Counter_{right})\) as its next collecting period column. When two or more counter values are equal, the mobile sink stays in the original column. The moving operation is shown in Figure 10.  
As mentioned above, in the first collecting period, the parking time in each grid cell is equal. However, in real environments, obstacles and external disturbance may cause the distribution of sensor node and source events density uneven. Our GSDCS takes advantage of \(Counter(i)\) to adjust the parking time for each gird cell in the following collecting periods. If a grid cell has a larger \(Counter(i)\), the mobile sink will park in the grid for longer. Then the mobile sink calculates the ratio of the total data from one row to all rows, which can be formulated by \(\frac{Counter(j)}{\sum Counter(i)}\). The adjustment the parking time of mobile sink in the grid cell of row \(i\) is shown as follows:  
Figure 10: Sink moves to next column. If the grid of row \(i\) is a non-boundary grid, then the parking time in this grid is:  
\[t_{park}(i)=\frac{T\times\frac{Counter(i)}{\sum Counter(i)}}{2} \tag{8}\]  
The total time sink spends in this grid for a single trip is:  
\[t(i)=\frac{T\times\frac{Counter(i)}{\sum Counter(i)}}{2}+\frac{a}{v} \tag{9}\]  
If the grid of row \(i\) is a boundary grid, then the parking time in this grid is:  
\[t_{park}(i)=T\times\frac{Counter(i)}{\sum Counter(i)} \tag{10}\]  
The total time sink spends in this grid is:  
\[t(i)=T\times\frac{Counter(i)}{\sum Counter(i)}+\frac{a}{v} \tag{11}\]  
Note, when a grid is a non-boundary grid, the mobile sink would travel through this grid twice in one collecting period. All the counters are set to zero at the beginning of a collecting period. When one collecting period completes, the mobile sink calculates the parameter \(t(i)\) of next period for each grid according to all the counters. The sink moving process can be described by the flow chart in Figure 11.
## 2 System Description
### Analog CAP Demodulator
By replacing \(F_{I}\) and \(F_{Q}\), we obtain:  
\[rx_{I}(t)=\Bigg{[}\Re A\sqrt{2}\sum_{m=1}^{M}\int_{-\infty}^{\infty }\Big{(}s_{I}^{m}(\tau)p(t-\tau)cos\big{(}\omega_{c}^{m}(t-\tau)\big{)}-\\ -s_{Q}^{m}(\tau)p(t-\tau)sin\big{(}\omega_{c}^{m}(t-\tau)\big{)} \Big{)}d\tau+\eta(t)\Bigg{]}cos(\omega_{LO}t) \tag{11}\]  
\[rx_{Q}(t)=-\Bigg{[}\Re A\sqrt{2}\sum_{m=1}^{M}\int_{-\infty}^{ \infty}\Big{(}s_{I}^{m}(\tau)p(t-\tau)cos\big{(}\omega_{c}^{m}(t-\tau)\big{)}- \\ -s_{Q}^{m}(\tau)p(t-\tau)sin\big{(}\omega_{c}^{m}(t-\tau)\big{)} \Big{)}d\tau+\eta(t)\Bigg{]}sin(\omega_{LO}t) \tag{12}\]  
Considering phase alignment between \(cos\big{(}\omega_{c}^{m}(t-\tau)\big{)}\) and \(cos(\omega_{LO}t)\), meaning that \(\tau~{}=~{}2n\tau,\forall n\in\mathbb{N}_{0}\), trigonometric products arise from the equations:  
\[rx_{I}(t)=\frac{\Re A\sqrt{2}}{2}\sum_{m=1}^{M}\int_{-\infty}^{ \infty}\Big{(}s_{I}^{m}(\tau)p(t-\tau)[cos\big{(}(\omega_{c}^{m}-\omega_{LO}) \tau\big{)}+cos\big{(}(\omega_{c}^{m}+\omega_{LO})\tau\big{)}]-\\ -s_{Q}^{m}(\tau)p(t-\tau)[sin\big{(}(\omega_{c}^{m}-\omega_{LO}) \tau\big{)}+sin\big{(}(\omega_{c}^{m}+\omega_{LO})\tau\big{)}]\Big{)}d\tau+\eta (t)cos(\omega_{LO}t) \tag{13}\]  
\[rx(t)=\Re A[y_{d}(t)*h_{t}(t))]+\eta(t) \tag{6}\]0  
Equations (13) and (14) are the mathematical description of the signal at the receiver mixers' output. High-frequency signal components are discarded by the usage of low-pass filters at the mixers' output, \(g(t)\), with a cut-off frequency around \(\omega_{c}^{1}\). Considering \(f(t)=p(t)*g(t)\), the signals at the filter output are given by:  
\[rx(t)=\Re A[y_{d}(t)*h_{t}(t))]+\eta(t) \tag{6}\]1  
\[rx(t)=\Re A[y_{d}(t)*h_{t}(t))]+\eta(t) \tag{6}\]2  
If \(\omega_{LO}=\omega_{c}^{m}\), some of the trigonometric terms cancel out, and by applying the convolution definition, the output signals are:  
\[rx(t)=\Re A[y_{d}(t)*h_{t}(t))]+\eta(t) \tag{6}\]3  
\[rx(t)=\Re A[y_{d}(t)*h_{t}(t))]+\eta(t) \tag{6}\]4Equations (17) and (18) show that, by carefully selecting a pair of filters \(p(t)\) and \(g(t)\), it is possible to recover the original symbols \(s_{t}^{m}(t)\) and \(s_{Q}^{m}(t)\). Nevertheless, the received symbols may have a non-unitary gain, and bandwidth limited noise is added.
## 2 State of the Art
### Generative Adversarial Networks  
Generative Adversarial Networks (GANs) have become another very popular type of deep learning model for segmentation and image processing. Most GANs consist of two different subnetworks known as the 'generator' and the 'discriminator.' The goal of the generator is to create images, often using noise vectors, such that the discriminator would not be able to tell the difference between real and 'generated' images.  
Many variations of this framework have been adapted and utilized for biomedical image segmentation. One such example is the MS-GAN [20]. It is a conditional GAN which uses MRIs such as FLAIR and T1 contrast images in place of noise vectors. Another example is the Medical Anomaly Detection GAN (MADGAN) [21], which also takes advantage of the aforementioned attention mechanism. Although this model is used for classification, a recent study has shown its very pertinent applications in both MRI reconstruction and lesion detection [21].
## 1 Introduction  
### Astronomical Point Sources and X-Ray Telescopes  
Astronomical systems as observed by modern telescopes are divided into the following two types: point-like sources and extended sources. The former are unresolved and present images whose sizes and shapes are dictated purely by the combined point spread function (PSF) of the telescope optics and the detector; whereas extended sources and crowded fields (containing many closely spaced point sources) present images that represent the true brightness patterns in the sky convolved with the imaging PSF [1]. If a reliable estimate of the PSF can be made, then in principle, mathematical deconvolution can reduce the instrumental blurring and provide a sharper image.  
Most observations from space-based X-ray telescopes are used to study distant galactic and extragalactic point sources. Examples of such point sources are X-ray binaries [2], novae [3], and active galactic nuclei [4], while extended sources are found in supernova remnants [5], galaxy clusters [6], star-forming regions [7], and galactic halos [8]. The goal is to always obtain more detailed images of the X-ray source, regardless of its nature. In extended sources, the finer the image, the more detailed the structure of the source. In the case of point sources, higher-resolution images can be crucial in distinguishing between two closely spaced sources (actually, their 2D projections in the sky).  
Among presently operating X-ray observatories, Chandra [9] has the best spatial resolution, capable of distinguishing sources projected to 0.5\({}^{\prime\prime}\) apart; whereas _NuSTAR_[10] has a broader energy coverage (viz. 3-79 keV versus 0.5-8 keV for Chandra), but also (suffers from) a broader PSF (a half power diameter (HPD) of 58\({}^{\prime\prime}\)). The _NuSTAR_ PSF also deteriorates considerably for off-axis sources (observed far out from the image center). This can create a grave issue in distinguishing between point sources located within the full radius of the PSF when there are no simultaneous observations from either the Chandra or the XMM-Newton [11] telescope. Image deconvolution can then help resolve the problem to a certain extent. For instance, [PERSON] et al. [12] applied a Richardson-Lucy image deconvolution technique to a high-resolution Chandra image of the supernova remnant Cas A to bring out more detailed features in the low-flux regions. A non-iterative deconvolution technique has also been used on Suzaku-XIS [13] images, improving the resolution from a 110\({}^{\prime\prime}\) HPD to \(\sim\)20\({}^{\prime\prime}\)[14].
## 2 M2M Service Networks
### M2M System
However, there are usually dependencies with the domain specific application, information, M2M service platform and M2M connectivity levels in the M2M asset devices. This may lead to a need to make changes into various levels of the M2M system, if, e.g., an M2M asset device manufacturer is changed. Therefore, application of standard based technologies in M2M service networks is very important. In addition, the system needs to scale, be interoperable, flexible and extensible during the complete life-cycle [3; 19]. Last but not the least is security and management of the complete M2M system. They are seen to be cross issues, which mean that these features shall be built-in to the levels of the system applying widely approved standards. In addition, the needs of ensuring services provided by industrial companies needs to be taken into account.
# Review of Power System Resilience Concept, Assessment, and Enhancement Measures
## 3 Understanding Power System Resilience
### Definition of Resilience
[67] indicated that the ability of absorption refers to the extent to which a system is able to automatically absorb the impacts of system perturbations and minimize the consequences as much as possible. The ability of adaptation is the extent to which a system can self-organize to restore system performance levels. The ability of recovery, as mentioned earlier, is the ability of a system to easily repair itself, with these repairs being considered dynamic. However, the power grid may be affected by extreme events, and although the probability of such accidents is low, the risks involved are high. Therefore, in the field of power systems, a new concept of resilience has been introduced to reflect the survivability of the power system when facing extreme events. The definition of resilience may vary depending on the organization or association. Table 2 summarizes the characteristics mentioned in each definition. The characteristics of a resilient system include anticipation, resistance, withstanding, absorption, adaptation, recovery, and learning. A brief description of each characteristic is given below [9; 14; 64; 67; 68]:  
* Anticipation: Before the disruptions, predicting the occurrence of events, assessing their potential harm, and taking preventive measures to reduce the impact on power grid performance. * Resistance: During the disruptions and before it expands, resisting and mitigating the harm of the events to ensure the continuity of the operations. * Withstanding: Ensuring the system maintains basic functionality at an acceptable level under disruptions. * Absorption: The system is capable of absorbing the impacts of disruptive events, avoiding potential cascading effects, and minimizing the system damage. * Adaptation: Adjusting, reorganizing, or modifying system configurations in an attempt to overcome a disruption. * Recovery: After the disruption, a system repairs or restores the damage from a disruption. * Learning: Learning from past events and improving measures to reduce risks, enhancing the flexibility of the system against future disruptions. It should be noted that the capability to withstand is related to long-term exposure to disruption, while the capacity for absorption is demonstrated only in the short term. The main characteristics that a strong and resilient power system should possess are shown in Table 3. To better understand the concept of resilience, Figure 2 presents a whole picture of power system resilience, illustrates the characteristics of a resilient system before and after extreme events and the measures for enhancing resilience, and indicates the flexibility and survivability that a resilient system should possess.