esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project Overview
### Spatial Distribution Characteristics of MS Events  
The spatial distribution of MS events from 19 July 2018 to 31 October 2020 is shown in Figure 5. The MS events are represented by spheres. The color and size of the spheres correspond to the moment magnitude and energy of the MS events, respectively. The MS events are mostly caused by high stress concentrations and large stress differences. The area where the MS events are concentrated usually has potential structural planes or has experienced unreasonable construction in its vicinity. According to Figure 5a, the MS events are mainly distributed in the powerhouse's arch area and on the top of the mid-partition separating the powerhouse from the main transformer chamber. The main areas of damage are represented by zones I, II, and III in Figure 5b, where the MS events are concentrated. Zone I is located beneath the powerhouse between stakes \(0-060.00\) and \(0+030.00\). Zone II is located in the arch of the mid-partition, which separates the powerhouse from the main transformer chamber between stakes \(0+030.00\) and \(0+070.00\). Zone III is located between stakes \(0+090.00\) and \(0+140.00\). The result shows that multiple MS events are more likely to concentrate in underground caverns under high geostress, and it is noteworthy that zone II showed more MS events of high moment magnitude and significant energy. Analyses of the construction nodes indicate that the main transformer chamber was excavated for bench I while bench II of the powerhouse was being excavated. Many large blasting events occurred at this time, and the intensity of the construction and blasting was high. Therefore, the surrounding rock was disturbed and unloaded in both directions, resulting in MS events with large energy and high moment magnitude. Moreover, zone III had the greatest concentration of MS events. Analyses of the geological conditions show that this zone was near the SPD9-F1 secondary fault and lamorphyre dyke. The presence of structural planes caused more MS events to occur. The concentration zones of the MS events have a high risk of destabilization during the excavation of underground caverns. For example, on 17 May 2019, a rock collapse occurred in zone III between stakes \(0+120.00\) and \(0+135.00\) on the upstream spandrel of the main powerhouse. Site surveys revealed that a lamorphyre dyke was exposed at the site of the collapse [26].  
Figure 4: Temporal distribution of MS events from 19 July 2018 to 31 October 2020.
## 4 Case Study
### Machine-Learning Results
#### 4.1.3 Multi-Class Classification Model  
For the multi-class classification model, the results are shown in Figures 11-13 for the normal, half and double case, respectively. The best case is the normal case using Multi-class NNs algorithm with respect to accuracy of results and computational time. The double case's computational time is significantly high with almost triple the normal case time. The half case showed no significant change in computational time.  
Figure 11: Multi-class classification model results for normal case.  
Figure 9: Binary classification model results for half case.  
On the other hand, both the Multi-class NNs and the Two-Class NN showed similar results while the Multi-class Decision Family scored the worst results for all cases. It can be concluded that the best parameters to use to predict the machine failure time in weeks are the normal case parameters using Multi-class NNs algorithm.
## 4 Discussion
### Surface Roughness Parameter  
Previous AFM research and analysis have indicated that the surface roughness of coal is influenced by multiple factors, including maceral type, particle size, and metamorphic  
Figure 5: (**A**) is the SEM image of coal sample AH-1; (**B**) is the SEM image of coal sample HB-1; (**C**) is the SEM image of coal sample SX-1; (**D**) is the SEM image of coal sample SX-4.  
grade. [PERSON] et al. [15] found that as the average particle size of coal increases, its surface roughness also increases. [PERSON] et al. [18] discovered that the surface roughness of coal varies with different metamorphic grades, with higher metamorphic grade coal having a smaller surface roughness than lower metamorphic grade coal. [PERSON]. [46] found that different macroals within the same coal sample exhibit different surface roughnesses, with fusinite having a greater surface roughness than semifusinite. Additionally, the surface roughness of coal is also affected by changes in volatiles.  
To further investigate the relationship between coal surface roughness and volatile matter, a correlation analysis was conducted between the roughness (R\({}_{\text{a}}\), R\({}_{\text{q}}\)) of each sample and its volatile matter content (V\({}_{\text{daf}}\)). As shown in Figure 6, the surface roughness of the samples exhibited an identical upward trend with increasing volatile matter content. This is due to the variation in roughness caused by differences in volatile matter content among the coal samples. Coals with low volatile matter have a dense structure, lower porosity, and a smoother surface. In contrast, coals with high volatile matter have a loose structure and contain more micropores and fractures, leading to higher surface roughness. The reason for this phenomenon may be that as volatile matter increases, the unstable organic matter in coal also increases. This organic matter is prone to decomposition during oxidation or pyrolysis, escaping as gases. Coals with high volatile matter release larger volumes of gases more rapidly, exerting an impact on the coal surface and forming more porous structures, significantly increasing its surface roughness. Furthermore, coals with high volatile matter contain more interintite microcomponents, while coals with low volatile matter are dominated by vitrinite. The porous structure of interintite also contributes to increased surface roughness. In summary, an increase in volatile matter promotes pore development and alters the proportions of microcomponents, leading to an upward trend in surface roughness.
## 3 Absorption Characteristics
### Geometric Parameter Sweep  
A parametric analysis is required to understand the proposed MA's absorbance properties. The sweeping parameter plays an important role in determining the best possible design and utilization absorbance. The parameter of H1 represents the thickness of tungsten at the bottom plane. Figure 8a shows that the absorbance property is studied in the wavelength range of 30-110 nm for H\({}_{1}\). It offers a unique absorbance curve for greater thicknesses with H\({}_{1}\) = 100 nm. To maximize absorbance and prevent transmission, the MMA design used H\({}_{1}\) = 110 nm, and absorbance levels are also impacted by the dielectric thickness (SiO\({}_{2}\)). Figure 8b shows the absorbance at various dielectric thicknesses for the parameter of H\({}_{2}\). When we used H\({}_{2}\) = 30 nm, the lower optical wavelength is absorbed more than the upper visible wavelength range. Moreover, absorbance values in the upper visible wavelength range are greater for thicker materials than those at the lower optical wavelengths. The highest average absorbance takes place at H\({}_{2}\) = 60 nm. The MMA patch thickness is also studied for H\({}_{3}\). There is a constant amount of absorbance for different H\({}_{3}\) values, but there is a slight distortion in the upper optical range. The value of H\({}_{3}\) = 15 nm has been determined as the highest absorbance level. A consistent absorbance bandwidth is achieved, and very few deformations are observed in the higher wavelength, as shown in Figure 7c. The resonator of the MMA was also studied. The value of L\({}_{2}\) = 205, W\({}_{2}\) = 480, W\({}_{3}\) = 180.03, and W\({}_{4}\) = 50 nm has been determined as the highest absorbance level. The circular ring resonator (CRR) considers R\({}_{1}\) = 140 and R\({}_{2}\) = 160 nm, respectively.  
Figure 7: The absorbance compared with (**a**) several dielectric layers and (**b**) various types of metals.  
Figure 8: Parametric analysis of **(a)** the bottom tungsten layer thickness, **(b)** SiO\({}_{2}\) dielectric layer thickness, **(c)** resonating patch of tungsten layer thickness, **(d)** resonating patch’s wide (**e**) effect of the circle leg (**f**) effect of the circle radius.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 3 Experimental Datasets
### Zero-Degree Isotherm Height  
The _freezing level_ corresponds to the zero-degree isotherm height. Below such altitude, the temperature is above the freezing point and rainfall may occur.  
The knowledge of the freezing level is necessary for the estimation of the equivalent rain height \(h_{R}\), in turn used in any rain attenuation prediction model: the latter quantity represents the altitude of an equivalent rain layer, which is typically higher than the freezing level and it depends on the different precipitation type (stratiform and convective).  
The zero-degree isotherm height time series \(h_{0}(t)\) for Milan are derived from ECMWF high-resolution vertical profiles of temperature. Specifically, the data are extracted from the ERA5 database, whose temporal resolution is one hour (from 00:00 to 23:00 UTC), with a spatial resolution of \(0.25^{\circ}\times 0.25^{\circ}\) in latitude and longitude. At each time instant \(t\), the temperature profile provides the temperature \(T(h)\) as a function of the altitude \(h\); \(h_{0}(t)\) is then derived by linearly interpolating the two \(h\) values closer to \(T(h)=0^{\circ}\)C.
## 2 Methodology
### Collecting SWT and Aesthetics Data from the HTML Source Code
#### 2.3.1 Semantic Web Technologies Adoption Variables  
With the coming of HTML5 in 2008, a series of new structural elements were added [20] with the intention of not only providing structural insight, as normal HTML elements do but also contextual insight on what the content inside these elements represents. [PERSON] et al. indicate that usage of these elements is mainly intended for browsers and accessibility devices, and that it is up to the content creators to select the proper element to convey the contents of each part of their website [21]. These the elements are <_article>_, <_aside>_, <_details>_, <_figcaption>_, <_figure>_, <_footer>_, <_cheader>_, <_main>_, <_mark>_, <_nav>_, <_section>_, <_summary_>, and <_time>_. The data extraction algorithm traverses the Document Object Model (DOM) of each website and identifies the use of any of these elements and records it into the variable _html_var_.  
The second variable concerning SWT adoption was \(og\) and it recorded whether a website made use of the Open Graph protocol to present itself in the form of a rich object. The protocol's intention is to make it possible for websites to be presented in a social graph and this is accomplished through a method compatible with W3 Cs Resource Description Framework in attributes (RDFa) recommendation [22].  
Another RDFFa compatible system specifically designed for Twitter is called \"Twitter Cards\" [23] and whether it existed in a website instance was recorded in the _twitter_ variable. Both the Open Graph and the Twitter Cards graphs create meta tag attributes that include information containing the Web page including a title, a short description, and a related image. Essentially, both Open Graph and Twitter Cards comprise Semantic Web applications that stem from the realm of Social Media as [PERSON] et al. explain [24] and this connection they have with Social Media has influenced their popularity and their importance to websites' Semantic Web integration.  
Although technically on the fence between Web 2.0 and Web 3.0, RDF Site Summary (RSS) feeds present one of the earliest attempts at Web syndication [25] and have hence been a long-time component of presenting Web pages and their content in a machine-readable manner. The variable _rss_ records the existence of such feeds in a website instance.  
Figure 5: Visual representation of the complete instance data collecting process.  
Finally, the last SWT-related variable is _sch_, which records the existence of schema.org data structures in the website instance. The data structure schemas of the schema.org community, which is supported by various big names in Web technologies such as Microsoft, Google, Yahoo, and Yandex, aim to make it easier for developers to integrate sections of machine-readable information in their creations [26]. Their usage provides the flexibility of choosing between three formats: RDFa like Open Graph and Twitter Cards, Microdata, and JSON-LD.  
Table 1 presents all SWT-related variables with a short description.
# Correction Compensation and Adaptive Cost Aggregation for Deep Laparoscopic Stereo Matching
## 3 Materials and Methods
### Adaptive Cost Aggregation Module
#### 3.3.3 Dense Connection Structure  
The DCS shares the same design idea as that in [30]. Specifically, at the cost aggregation stage, the feature information learned by every APAB is losslessly transmitted to subsequent modules. As shown in Figure 5, for a single APAB, its input features are transmitted to the output in an additive manner. In the case of multiple APABs, the input features of the first are transmitted to the last in a concatenated manner. The DCS is conducive to the propagation of information, strengthens the reuse of information, and avoids gradient descent, making it easier for the network to converge.
## 1 Related Studies
### Semisupervised Feature Extraction  
A Wi-Fi RSSI dataset consists of RSSI values corresponding to a user's position, obtained from Wi-Fi access points (APs). The dimension of a raw Wi-Fi RSSI dataset is defined as the number of APs scanned in the entire area of interest in a building. Typically, the dimensionality is so high that it is impractical to perform real-time operations. Additionally, many of the elements in a raw RSSI database are usually empty because APs cannot cover a wide area. Therefore, reconstructing the data of a raw database is of paramount importance. Deep learning approaches [25; 26; 27; 28] have been applied to the RSSI indoor localization. The high accuracy reported is due to its capability for feature extraction by the deep neural network structure, which learns complex pattern of the RSSI observations. The feature extraction in the standard deep learning except the autoencoder method [29] is computed among the hidden neural network layers, and then the hidden layers are connected to the latest localization layer. Thus, they cannot be used for our purpose to obtain the low-dimensional feature vector separately.  
This paper combines two feature extraction methods: Fisher discriminant analysis (FDA) and principal component analysis (PCA). FDA is an a supervised dimensionality reduction method, and PCA is an unsupervised dimensionality reduction method. Supervised FDA tends to find embedded spaces overfitted to labeled samples. Therefore, it is effective to add unlabeled data, which can be collected easily and in large volumes. By using both labeled and unlabeled data, the combination of FDA and PCA can produce better accuracy compared to that achieved by solely using supervised FDA or unsupervised PCA. In this study, the role of semisupervised discriminant analysis is two-fold: dimension reduction and detection of floor level and landmarks.
## 3 Feature Extraction
### Cellular Network Environment Related Features  
The cellular base stations to which the mobile phone connects during a minute are logged. From the logged data, the following statistics are used as features:  
* The number of unique Cell IDs (base transceiver station identifiers).
* The number of unique location area codes (LAC).
* The number of Cell ID changes per minute.
* The number of LAC changes per minute.
* The standard deviation of the strength of the signal to the transceiver station the mobile phone is connected.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 3 Different Channel Models
### Graph-Based Models
#### 3.2.2 Algorithm for Searching Paths  
The shortest path problem is well known, and many efficient algorithms are available in the literature for solving this problem, e.g., [PERSON]'s algorithm [40], A* search algorithm [41] and Floyd-Warshall algorithm [42]. Due to the nature of the graph and the paths that we need to search for, we base our implementation on [PERSON]'s algorithm [40]. Algorithm A1 in Appendix A presents the pseudocode of our pathfinding method. Algorithm A1 includes two sub-procedures: Algorithms A2 and A3. The pseudocode and further details of these algorithms are provided in Appendix A as well. We use Algorithm A1 to search for a path from each AP location \(l\in\mathcal{L}\) to location \(i\in\mathcal{R}\) that gives the lowest value for \(\ell_{il}\). Our hypothesis behind the algorithm is that when sending a radio wave from a location \(l\) to a location \(i\), the propagation path loss during will increase with (1) increased propagation distance between the two locations, (2) reflections, scattering  
Figure 6: An example of the graph for capturing environment information on the map. The nodes in the graph are one meter apart and directly connected to their neighbor nodes via edges. The figure on the right zooms in on the graph. The highlighted lines illustrate the eight edges that connect a node with all of its neighbor nodes. The dashed line in the left figure indicates the coverage region. Each node within the coverage region is associated with a generated location mentioned in Section 2.  
and diffraction due to obstacles, and (3) when there is a mismatch between the angle of departure and the broadside direction of the AP at location \(l\). We try to incorporate these three effects into parameter \(\ell_{il}\). A smaller \(\ell_{il}\) corresponds with a higher average received signal power at location \(i\).
## 4 Experimental Results
Upgraded image packages with sizes ranging from 10 to 900 bytes were selected, and images with adjacent sizes were divided into nine groups to perform an OTA difference effect comparison experiment using the BsDiff and SAIS-BsDiff algorithms. The BsDiff and SAIS-BsDiff algorithms were used 50 times for each group of experiments and then averaged to obtain the difference in time between the two algorithms, as shown in Figure 10. The results show that both the BsDiff and SAIS-BsDiff algorithms can correctly complete the difference and reduction tasks. As the upgraded file size increases, the differential time \begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Model** & **Equipment Requirements** \\ \hline \multirow{4}{*}{Server} & Thinkpad T14 (Lenovo, Hong & \\  & Kong, China) & \\  & Intel CORE IF 7 (Intel, Santa & \\  & Clara, CA, USA) & \\ \hline \multirow{2}{*}{Router} & USR-G810 (USR IOT, Jinan, & \\  & China) & \\ \hline \multirow{4}{*}{Industrial Personal Computer} & MIC-770 (Intel, Santa Clara, & \\  & CA, USA) & Ubuntu20.04 \(\times\)64 \\  & Intel.\({}^{\bigtriangleup}\) Q370/H310 (Intel, & \\  & Santa Clara, CA, USA) & \\ \hline \multirow{4}{*}{STM32 Development board} & STM32F407 ZGT6 & \\  & (STMicroelectronics, Geneva, & \\ \cline{1-1}  & Switzerland) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test equipment configuration. increases. However, compared to the BsDiff algorithm, the proposed SAIS-BsDiff algorithm consumes less time. Taking a 900-byte file as an example, the BsDiff algorithm requires approximately 610 ms, whereas the SAIS-BsDiff algorithm requires less than 400 ms, saving 39% of the original consumption time. Figure 8: BsDiff’s difference reduction results. Figure 10: Differential time contrast. Figure 9: SAIS–BsDiff’s difference reduction results. Table 3 shows the optimization effects of the BsDiff and SAIS-BsDiff algorithms. With an increase in the upgraded file size, the time difference before and after optimization also increased. Compared to the BsDiff algorithm, the difference in time of the proposed SAIS-BsDiff algorithm improved by more than 35.86%. Through the aforementioned test platform analysis, it was verified that the proposed SAIS-BsDiff algorithm can improve the efficiency of differential compression and reduce time consumption in the OTA upgrading process.
###### Abstract  
The problem of voltage sag and swell is one of the major reasons for low-quality power in the distribution system. Normally, it results from the system's faults, including line-to-ground and line-to-line, non-linear characteristics of loads and sources. Its effect is very serious for the critical loads as their performance is very sensitive to the variation in voltage. The stabilization of voltage is a mandatory requirement in such a situation. The correction of such problems requires the addition and subtraction of the voltage once the line voltage is decreased and increased. This behavior of the correcting voltage is ensured by the use of voltage controllers that can convert a constant input voltage into a non-inverted and inverted variable form. Their voltage gains depend on the depth level of the problem. The voltage buck and boost capabilities of the AC voltage stabilizers can tackle the problems having any depth level. The smartness of such a system depends on the number of electronic devices as they are the key elements in the power conversion system. Therefore, this research proposes a new AC voltage controller with fewer solid-state devices. Its overall impact is low volume and cost. The validation of the introduced approach is ensured with the help of simulation modeling and results gained from the practical setup.  
AC voltage controller; buck and boost; critical loads; electronic devices; inverted and non-inverted outputs; power quality; power conversion; voltage sag and swell +
Footnote †: journal: Applied Sciences
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 3 Design and Implementation
### Network Types for Origination and Termination  
LPWAN gateways may transmit the calls to cellular or other networks as needed and as applicable. For example, some areas may not be covered by mobile networks but are covered by circuit switched networks. In the case of a disaster situation, some or all of the networks may not be available, and the LPWAN may connect to peer networks such as the IoT network. In any case, the destination network where the disaster centre is connected to is likely to be in a carrier network such as 3G mobile or PSTN numbers, depending on what number has been dialled by the gateway. Figure 2 shows emergency procedures of a gateway and sensors in an LPWAN.