esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
## 3 Proposed Method
### Feature Extraction  
In this section, the hybrid multi-time scale neural network model that was used in this study to extract the characteristics of electricity consumption data is thoroughly described. The model's framework is depicted in Figure 15.  
LSTM, as a variant structure of RNN, can capture the hidden features inside the time series when processing electricity consumption data with temporal properties, and it can overcome the gradient disappearance problem of RNN [31]. For electricity consumption data with day as the time scale, inspired by previous research in the literature [18], Bi-LSTM was used to extract the global features of consumers' electricity consumption. Bi-LSTM is  
Figure 14: The Time GAN method utilizes PCA and t-SNE in the two-dimensional plane to visualize the results. (**a**) PCA; (**b**) t-SNE.  
Figure 15: Model structure framework of hybrid multi-time scale neural network.  
iterated in opposite directions to boost the whole learning of the sequence using forward and backward information, respectively. In this model, the input size was 1035, the hidden size was 128, and the number of layers was 2.  
Second, for electricity consumption data with week as the time scale, ResNet was used to extract the users' electricity consumption features. ResNet preserves the completeness of the input information by bypassing it directly to the output, where the whole network only requires learning the input, the part of the output difference, thus simplifying the learning objective and difficulty [32]. In this model, the input size was 7 \(\times\) 148, the number of layers was 3, the numbers of filters were 16, 8, and 4, respectively, the kernel size was 3, the move step stride was 1, and the activation was'relu'.  
Finally, for the electricity consumption data with month as the time scale, AlexNet was employed to extract the electricity consumption features of consumers. AlexNet has been widely used in the field of image recognition, and with increasing computational power, AlexNet has also gradually been used to extract time series data for feature extraction [33]. AlexNet has an 8-layer structure; the front 5 layers are convolutional layers, and the last 3 layers are fully connected layers. In this model, the input size was 30 \(\times\) 34, the numbers of filters were 8, 16, 32, 32, and 16, respectively, the kernel size was 3, the move step stride was 1, and the activation was'relu'.  
After several rounds of training, the size of each batch of data was finally selected as a batch size of 20, the learning rate was 1, and the loss function was binary cross-entropy.
## 2 Scientific Background and State of Knowledge
### Terrestrial C Cycling  
One of the crucial issues in the prognosis of future climate change is the global budget of atmospheric CO\({}_{2}\). The growth rate of atmospheric CO\({}_{2}\) is increasing rapidly. Three processes contribute to this rapid increase: fossil fuel emission, land use change (deforestation), and ocean and  
Figure 1: Schematic view of the components of the climate system, their processes and interactions [1].  
terrestrial uptake. As shown in Figure 2, terrestrial C budgets have large uncertainties and interannual variability.  
Terrestrial ecosystems mediate a large part of CO\({}_{2}\) flux between the Earth's surface and the atmosphere, with \(\sim\)120 Pg C yr\({}^{-1}\) taken up by photosynthesis and roughly the same amount released back to the atmosphere by respiration annually [1,15]. Imbalances between gross ecosystem photosynthesis or gross primary productivity (GPP) and ecosystem respiration (\(R_{e}\)) lead to land surfaces being either CO\({}_{2}\) sinks or sources. C sequestration by global terrestrial ecosystems is estimated to be about 1-2 Gt C yr\({}^{-1}\)[1,15]. A detailed understanding of the interactive relationships in atmosphere-biosphere exchange is relevant to ecosystem-scale analysis and is needed to improve our knowledge of the global C cycle [16]. The metabolism of terrestrial ecosystems is complex and highly dynamic because ecosystems consist of coupled, non-linear processes that possess many positive and negative feedbacks [17,18]. Complex features of ecosystem metabolism are relatively unknown and how C budget of major ecosystems will respond to changes in climate is not quantitatively well understood [19-23].
# Analysis of the Current State of Automation of Hazard Detection Processes in BIM in Slovakia
## 2 Materials and Methods
### Selection of a Data Collection Method  
Two methods of data collection were considered. The first was a mass survey in which an electronic questionnaire would be sent to a large number of construction companies and respondents belonging to several associations, unions or chambers of civil engineers. However, based on statistics from the survey of BIM association Slovakia from September 2020, 29.7% of 1727 respondents worked for construction companies and only 9% of them used BIM in their projects, 35% of them were interested in BIM and the rest did not use BIM [6]. Based on these data [6], such a questionnaire would probably lower the professional relevance of the answers to BIM, VDC (Virtual Design and Construction) and digitization. The second method was to collect more relevant and detailed data from a smaller sample of construction specialists (BIM managers, project managers, safety coordinators) from construction companies that already use BIM, since their level of knowledge about construction digitization is higher than would be found in a broad market sample. Data collected by the second method should be more applicable for the further research and BIM software development.  
In 2020, the population of Slovakia was approximately 5,460,000 [66], and the number of construction companies at the time of writing was approximately 44,600 [67]. As already mentioned, BIM adoption in Slovakia is low [6], but there are companies where the level of BIM implementation is high. Despite these statistics, and the expected small survey sample, the second method was chosen. In addition to the electronic questionnaire, it was decided to conduct personal interviews in the form of a video call so that the topic, research goals and some questions could be explained better and to foster more detailed discussion and information. Misunderstandings, or knowledge gaps (for example of technical terms), were eliminated, which led to more accurate results. In addition, the data obtained from experts are comparatively more useful for research and BIM software development. Unfortunately, only a few companies and specialists in Slovakia have reached this level.  
Based on the decision to develop a questionnaire and syllabus for the interview, a process map of the research (Figure 1) consisting of five main steps was prepared: literature search, survey preparation, survey execution, analysis of results and use of results. These steps contained subprocesses that are described in the next sections.
## 3 Results and Discussion
### Physical Fiber Quality  
#### 3.6.1 Surface Morphology  
SEM images of retting-treated kenaf fiber surface analysis are shown in Figure 7. Figure 7a,b show uninoculated-treated fibers with FW and SW, respectively, indicating substantial gums are present on the fibers' surfaces after six days of the retting treatments. Both treatments kept the fibers intact and required additional retting to remove the inherent gums. Figure 7c shows the APPB1-inoculated treatments combined with seawater solvents, revealing comparably smooth surfaces, which may be due to maximum NCG removal. In e, f, similar fibers on the surfaces of APPB2 combined with SW and FW indicated some degree of gums on their surfaces, while being cleaner than those of the uninoculated kenaf fibers.  
The findings of the surface morphology revealed by the SEM technique after the retting treatments are consistent with the results obtained with the fiber composition analysis. The surfaces of the kenaf fibers retrieved from water retting did not show any mechanical damages, regardless of the presence and absence of adhered gums. These findings are consistent with the report by [63; 64] of retted kenaf fibers.  
Figure 7: SEM images of retted kenaf fibers at a 100 \(\upmu\)m magnification, showing the presence and absence of amounts of gummy substances on the fiber’s surfaces. (**a**) Uninoculated least retted fiber with FW. (**b**) Uninoculated with SW. (**c**) APPB1 with SW. (**d**) APPB1 with FW. (**e**) APPB2 with SW. (**f**) APPB2 with FW.
## 3 Results and Discussion
### Characterization of Volatile Compounds of FAA Tea in Four Harvest Times
#### Analysis of Volatile Compounds via HS-SPME/GC\(\times\)GC-TOFMS  
The chromatograms obtained from HS-SPME/GC\(\times\)GC-TOFMS analysis are shown in Figure S4. The chromatograms of FAA tea harvested at different times vary. Flavor compounds in FAA tea consist primarily of volatile compounds, such as hydrocarbons,  
Figure 6: (**a**) PCA score plots based on GC-IMS analysis; (**b**) FAA tea flavor fingerprint via GC-IMS analysis.  
aldehydes, esters, acids, ketones, alcohols, ethers, phenols, and heterocyclic compounds, which are generated through complex biochemical reactions involving flavor precursors during processing [46]. As shown in Figure 7a, FAA-6 exhibited the highest number of volatile compound species, while FAA-3 had the lowest. Figure 7b highlights that a total of 1742 volatile compounds were identified, with 443 shared across all four harvest periods. Among the unique compounds, FAA-3 contained 159, FAA-4 had 150, FAA-5 included 173, and FAA-6 had the highest number at 298. Figure 7c further illustrate variations in the relative content of volatile compounds across the four harvest periods. Notably, FAA-6 showed the highest abundance and relative content of ketones and esters, emphasizing its distinct flavor profile compared to earlier harvest periods. These results demonstrate that the harvest period significantly impacts the composition and relative abundance of flavor compounds in FAA tea.
# Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks
## 5 Simulation Results and Discussion
### MLMVN as a CNN in the Frequency Domain
Our results obtained using MLMVN as a frequency-domain CNN are comparable. A regular MLP with a single hidden layer containing 800 neurons was used in [81], and we employed MLMVN with a single hidden layer with various numbers of neurons there. However, an entire 28 \(\times\) 28 image was used as a network input in [81] (that is, 784 inputs were used), while we used Fourier coefficients corresponding to the first five to seven frequencies as an input. That is, we employed between 60 inputs (five frequencies) and 112 inputs (seven frequencies)--fewer inputs than in [81] by an order of magnitude. Our results for the Fashion MNIST dataset are also comparable to the results reported by other authors. In [80], 89.65% accuracy is reported for an ordinal feedforward network, but with an optimizer. A CNN with two convolutional layers containing 64 kernels each along with modified learning algorithm makes it possible to reach 92.76% accuracy [80]. To obtain better results, a CNN with multiple convolutional layers and/or data preprocessing should be used [82; 80]. The use of hierarchical CNNs [34] (that is, the use of a significantly more sophisticated technique) may also boost classification accuracy for the Fashion MNIST dataset [82]. Hence, we may conclude that our simulation results are comparable to those obtained by other authors. Moreover, our approach has potential that is yet to be discovered--if more sophisticated network topologies, various kinds of preprocessing, and adaptive modified learning techniques are used.
# Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt
## 3 Material and Methods
### CHIRPS Data  
The CHIRPS is a 30+ year quasi-global rainfall dataset. It spans from 50\({}^{\circ}\)S-50\({}^{\circ}\)N with a high spatial resolution of 0.05\({}^{\circ}\). CHIRPS data contain information only from continental areas and at multiple temporal resolutions. This dataset includes three principal components: global climatologies, satellite measurements (at infrared wavelengths) and in-situ observations [44]. Among the advantages of CHIRPS are their great geographical coverage, temporal uniformity, their validation processes and the quasi-real-time updates. Several studies have been conducted worldwide to validate this dataset [45; 46; 47]. Mexico is one of the principal collaborators in in-situ observations of CHIRPS [44], and recently, different investigations have been realized in the country using this dataset [48; 49]. For example, in 2017, an average of 335 meteorological stations per month were used in the CHIRPS dataset, only for the area of the TMVB. In this study, the 2.0 version of the CHIRPS dataset was used to assess the spatio-temporal distribution of precipitation and extreme events in the TMVB. Daily data [50] from 2008-2017 with a spatial resolution of 0.05\({}^{\circ}\) were processed. Although the CHIRPS dataset used includes the entire diurnal cycle, the significant contribution of the afternoon and early night precipitation in the study zone just before or even through the last MODIS observations [51; 52] helped us to understand the contribution of these convective clouds to the precipitation regime.
###### Abstract  
This study introduces a compact Raman spectrometer with a 1064 nm excitation laser coupled with a fiber probe and an inexpensive motorized stage, offering a promising alternative to widely used Raman imaging instruments with 785 nm excitation lasers. The benefits of 1064 nm excitation for biomedical applications include further suppression of fluorescence background and deeper tissue penetration. The performance of the 1064 nm instrument in detecting cancer in human bladder resectates is demonstrated. Raman images with 1064 nm excitation were collected ex vivo from 10 human tumor and non-tumor bladder specimens, and the results are compared to previously published Raman images with 785 nm excitation. K-Means cluster (KMC) analysis is used after pre-processing to identify Raman signatures of control, tumor, necrosis, and lipid-rich tissues. Hierarchical cluster analysis (HCA) groups the KMC centroids of all specimens as input. The tools for data processing and hyperspectral analysis were compiled in an open-source Python library called SpectralMap (SpMap). In spite of lower spectral resolution, the 1064 nm Raman instrument can differentiate between tumor and non-tumor bladder tissues in a similar way to 785 nm Raman spectroscopy. These findings hold promise for future clinical hyperspectral Raman imaging, in particular for specimens with intense fluorescence background, e.g., kidney stones that are discussed as another widespread urological application.  
bladder tumor; Raman imaging; 1064 nm; cluster analysis; Python toolbox +
Footnote †: journal: Applied sciences
## 3 Test Network
### Impact of SSC of an Aggregated Network Supply  
Additional tests were performed for a phase-to-phase (Ph-Ph) fault with a fault resistance of 2 \(\Omega\) in the middle of the C-D line for different SSC values of an aggregated network supply. Tables 1 and 2 give the steady-state values during the fault for the generated power and line voltages. The obtained results show that an increase in the aggregated network SSC decreased the differences in generated power between both FFC concepts. The generated reactive power was not affected significantly by an aggregated network SSC when using the power-based FFC concept. Compared with the current-based concept, the power-based concept also generated more reactive power and less active power. Furthermore, the generated apparent power was approximately equal for both the discussed FFC concepts. Minor differences in voltage support by both FFC concepts were seen only for small values of an aggregated network SSC.  
Figure 5: Comparison in power generation assuming reference tracking of the power-based FFC (**a**), and current-based FFC (**b**).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SSC\({}_{\text{an}}\) [MVA]** & **IIDG** & \(P\) [MW]** & \(Q\) [MVAr] & \(S\) [MVA] & \(U_{\text{L1}}\) [pu] & \(U_{\text{L2}}\) [pu] & \(U_{\text{L3}}\) [pu] \\ \hline \multirow{3}{*}{50} & B & 1.9 & 7.4 & 7.6 & 1.0581 & 0.5204 & 0.5394 \\  & C & 1.8 & 7.3 & 7.5 & 1.0608 & 0.5306 & 0.5306 \\  & D & 2.7 & 7.5 & 8.0 & 1.0581 & 0.5251 & 0.5336 \\ \hline \multirow{3}{*}{100} & B & 2.6 & 7.6 & 8.0 & 1.1092 & 0.5537 & 0.5603 \\  & C & 2.3 & 7.5 & 7.8 & 1.1118 & 0.5607 & 0.5521 \\  & D & 2.0 & 7.2 & 7.5 & 1.1092 & 0.5570 & 0.5538 \\ \hline \multirow{3}{*}{1000} & B & 3.2 & 7.7 & 8.3 & 0.9643 & 0.5718 & 0.4984 \\  & C & 1.7 & 7.2 & 7.4 & 0.9670 & 0.5295 & 0.4531 \\ \cline{1-1}  & D & 0.5 & 6.2 & 6.2 & 0.9641 & 0.5378 & 0.4622 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Steady-state results during a Ph-Ph fault (L2-L3) in the middle of the C-D line, employing the power-based FFC.  
Figure 6: Comparison in power generation during a Ph-Ph fault (L2-L3) in the middle of the C-D line, employing the power-based FFC (**a**), and current-based FFC (**b**).  
Figure 7: Comparison of line voltages during a Ph-Ph fault (L2-L3) in the middle of the C-D line, employing the power-based FFC, current-based FFC, and when switching-off IIDGs.
# Review of Indoor Positioning: Radio Wave Technology
## 3 Radio Signals-Based Positioning
### Bluetooth Technology  
Bluetooth low energy (BLE) is mostly supported by smart devices today. It is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15 standard. The Bluetooth 4.0 protocol was distributed and it was announced in 2010 [105]. BLE signal is a kind of electromagnetic signal that works in the range from 2.4 GHz to 2.4835 GHz band in Industrial Scientific and Medical (ISM) [106]. In 2013, a new iBeacon technology was presented by Apple Inc. The iBeacon technology was created based on BLE technology that can send directly with smartphones and it is has lower power and a lower cost than conventional Bluetooth and Wi-Fi technologies [107; 108; 109]. In addition, the launch of Google's EddystoneTM open standard in 2015 produced new and better broadcast formats that have aided in the development of interest in the widespread use and embedding of Bluetooth beacon platforms [32].  
BLE is designed with very short ranged wireless transmissions. Hence, the estimated errors using Wi-Fi-based systems are normally much higher than those in BLE-based systems [111]. The sensing length of Bluetooth is at most 10 m, with great power cost and is only ideal for a small space [38]. Bluetooth devices are varied because of different productions, rated voltage, and energy, and therefore, the RSS can change as much as 20 dBm [112]. Moreover, in reality, Bluetooth broadcast power takes time-varying characteristics [108].  
Although the Bluetooth-based system needs further hardware devices in contrast with the Wi-Fi-based system, it can attain accuracies in the range of 1.2 m [97]. In addition, low power Bluetooth is chosen in indoor positioning systems and IoT applications because of advantages such as low cost, low power (0.367 mW average power consumption) [107], small size and easy deployment [113; 114; 115; 116]. It can be as extended as much as 100 m byadjusting the broadcast power, which creates the possibility of a wider range of indoor positioning using Bluetooth 4.0 [117]. The Bluetooth-based indoor location system mainly use proximity detection, trilateration, and fingerprinting. However, the positioning accuracy will be affected by the stability of the Bluetooth node and the indoor propagating environment. Several experiments with the Bluetooth scheme show that accurate positioning needs additional exploration [105]. Furthermore, Bluetooth technology obstructs Wi-Fi for the reason that they share the same frequency band [92].
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 3 Results
### Validation of Various PWV Products
#### 3.1.1 PWV Comparison of S2-L2A, MOD05, and ESA-L2A with GPS  
To make the measured spatial range consistent, this study compared the GPS PWV with the mean value of 17 \(\times\) 17 pixels on the Sentinel-2 image. Table 2 shows the PWV comparison of S2-L2A, MOD05, and ESA-L2A with GPS. S2-L2A estimates show a strong correlation with GPS retrievals (0.929) and low bias (RMSE = 0.114 cm and Bias = \(-\)0.005 cm). This result demonstrated that the S2-L2A PWV had high accuracy and could meet the requirements of quantitative remote sensing. A negative Bias value indicated that S2-L2A underestimated PWV. Although the bias values of MOD05 and ESA-L2A products were slightly larger (RMSE values were 0.343 cm and 0.346 cm, respectively; Bias values were 0.292 cm and \(-\)0.319 cm, respectively), they were still highly correlated with GPS-retrieved PWV (R were 0.947 and 0.908, respectively), indicating that these two PWV products could be used to evaluate the accuracy of S2-L2A PWV over a large range. In addition, compared with MOD05 and ESA-L2A, the PWV retrieved by S2-L2A was closest to the reference data and had the highest accuracy.
## Results and Discussion
### Purification of APs from MPTH
#### 3.2.4 Isolation of Peptides from MUA-4-B by RP-HPLC  
RP-HPLC is an effective technique applied to purify and quantify peptides in a mixture solution on their hydrophobic character [32; 34]. The retention time (RT) and the peak area can be used for qualitative and quantitative analysis of the isolated peptides [11]. As shown in Figure 4, MUA-4-B was finally purified using RP-HPLC system and thirteen major sub-fractions (MMP-1 to MMP-13) were collected separately through repeated chromatography isolation. As shown in Figure 5, thirteen fractions showed varying degrees of DPPH- and HO- scavenging activity, and the radical scavenging activities of MMP-4, MMP-7, and MMP-12 were confirmed to be higher than those of other ten peptides. Therefore, MMP-4, MMP-7, and MMP-12 with RT of 9.912 min, 13.354 min, 24.032 min, respectively, were collected and lyophilized for amino acid sequence identification and activity evaluation.  
Figure 3: Elution profile of MUA-4 in Sephadex G-25 chromatography (**A**) and radical scavenging activities of MUA-4 and its fractions (MUA-4-A and MUA-4-B) at 5.0 mg protein/mL concentration (**B**). The data are presented as the mean \(\pm\) SD (\(n=3\)). \({}^{a-c}\) or \({}^{A-C}\) Column wise values with the same superscripts of this type indicate no significant difference (\(p>0.05\)).
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 2 Study Area and Datasets
### AMSR2 Brightness Temperature  
The advanced microwave scanning radiometer on JAXA's GCM-W1 (AMSR2), launched on 18 May 2012, scanned the earth twice per day (ascending and descending orbits). AMSR2 is a multi-frequency dual-polarized sensor and provides brightness temperatures in seven frequencies (6.93 GHz, 7.3 GHz, 10.65 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) at both horizontal and vertical polarizations. The footprints of the data vary with frequency, the higher frequency with finer spatial resolution. In this study, the brightness temperatures at 36.5 GHz and 89.0 GHz for both polarizations in 2017 and 2018 are used to derive ice thickness in the Ross Sea based on the method in [19]. These brightness temperatures are retrieved from the \"AMSR-E/AMSR2 Unified L3 Daily 12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids V001\" [49], archived in NASA ([[https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)]([https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search))). In this dataset, the brightness temperatures with different footprints are resampled to a grid size of 12 km \(\times\) 12 km.