esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Results and Discussion Regarding Hypothesis 2
Given the presence of numerous industries in and around Okhla, including mills and various scales of industrial operations, it is reasonable to infer that these factors contribute to groundwater contamination and pollution. The samples collected from the Nangloi district provide a telling picture that aligns with our analysis. Notably, the untreated water sample from the canal, A9, exhibited the lowest water quality index due to sewage discharge and industrial contamination. Conversely, A10, the treated sample from the treatment plant, rightfully displayed the highest water quality index. Meanwhile, A11 samples raised concerns with their marginal quality, and A12 showed good water quality but deserves further scrutiny. Authorities should take proactive measures to address the potential risks of TDS, nitrate, and fluoride poisoning, which can have severe health implications. It is important to acknowledge that there are 14 variables and 42 tests that can be considered responsible for the low water quality index observed in some cases. The data presented in Tables 7 and 8, along with the analysis of water quality using CCMEWQI, reveals variations in water quality across different locations within the Delhi Region. The CCMEWQI ratings indicate that some locations have excellent water quality (WQI values in the range of 95-100) while others fall into the good, fair, marginal, or poor categories. Figure 13 shows the pin water sample location of the CCME WQI map of the 3 study area. The Nangloi district presents a significant contrast, with sample A9 from the canal displaying poor water quality due to sewage discharge and industrial contamination. On the other hand, A10, the treated sample from the treatment plant, has excellent water quality. However, A11 raises concerns in terms of marginal quality, and A12, while considered good, requires further scrutiny. Figure 12: CCME WQI for different location (author’s creation). In the Okhla Industrial Area, samples A5, A7, and A8 are rated as fair quality, while A6 falls into the marginal category. This aligns with the expectation that the industrial activities in the area contribute to groundwater contamination and lower water quality. In the Karol Bagh residential area, samples A1 and A2 exhibit excellent water quality, but A3 and A4 show marginal and fair quality, respectively. This suggests that, even in residential areas, there can be variations in water quality due to household activities and the release of waste water directly or indirectly into the sewerage or nala, causing groundwater pollution and contamination, which may have implications for public health. Overall, these findings support Hypothesis 2, indicating that water quality in the Delhi Region varies significantly across different locations, with factors like household activities, industrial activities, and sewage discharge playing a crucial role in groundwater quality variations. Therefore, Hypothesis 2 is confirmed.
###### Abstract  
This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node's power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery's lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems.  
piezoelectricity; vibration; energy harvesting; microcontroller; sensor node; autonomous operation; Bluetooth low energy 202424338
# A Genomic Survey of Signalling in the Myxococcaceae
## 3 Results
### MyxococcaelGenomes Encode Similar Numbers and Types of Regulatory Proteins  
Regulatory proteins were then identified among the genome-encoded CDSs for each genome (Table 2, Supplementary Table S1). TCS proteins, OCSs, TRs and alternative sigma factors were identified and categorised using P2 RP [33], while Ser/Thr Kinases were identified using BLASTp, queried with Pkn8 and Pkn14 from _M. xanthus_ (MXAN_1710 and MXAN_5116), which both contain the pfam domain Pkinase, PF00069 [40]. To compare the variability in numbers of the different types of proteins between groups of genomes, Table 2 also presents the variability coefficient (standard deviation divided by mean) for each type of protein in each group of genomes, expressed as a percentage.  
The numbers of TCS proteins and Ser/Thr kinases identified closely match those few published previously [1; 2; 3], with most Myxococcael genomes encoding around 300 TCS proteins, 100 Ser/Thr kinases and 300 transcription factors. Within each set of genomes, the numbers of each type of signalling protein are broadly similar. For instance, among the set of ten _M. xanthus_ genomes, the variability coefficient was less than 10% for every type of protein except for TCS phosphotransfer proteins, which are typically present in very small numbers. For TCS and RRs, the variability coefficient was particularly low: less than 1%, compared to a variability coefficient of 1.16% for the number of CDS. A similar pattern of variability was seen within the set of ten _C. exigus_ genomes, with the number of encoded phosphotransfer proteins being highly variable, but with minimal variations in the numbers of HKs and RRs (Table 2). At a genus level, more variability was seen in the numbers of all classes of regulatory genes than when considering sets of strains within a species, with _Myxococcus_/_Pyxidicoccus_ spp. genomes exhibiting more variability in numbers of regulatory genes than those of _Cornallococus_ spp. It is also noteworthy that the average _M. xanthus_ genome encodes substantially fewer regulatory proteins of every type than typical for _Myxococcus_/_Pyxidicoccus_ spp. (Table 2), and that OCS numbers were particularly variable in each taxon.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**M. xanthus Strains (10)**} & \multicolumn{2}{c}{**Myvaccoccus/Pyadicoccus spp. (11)**} & \multicolumn{2}{c}{_Corallococcus spp. (10)_} & \multicolumn{2}{c}{_C. elegans_ Strains (10)_} \\ \cline{2-9}  & **(mean \(\pm\) sd)** & **
## 3 Data and Methods
### Establishment of the Basic Database
#### 3.3.5 Potential Evapotranspiration Data  
The potential evapotranspiration data were sourced from the National Earth System Science Data Center in China, covering the period from 1901 to 2023, monthly potential evapotranspiration data with 1 km accuracy. Given that the study's temporal framework spanned from 2010 to 2018, grid data for nine years were acquired by a grid calculator in GIS to analyze the information, and potential evapotranspiration data for the study area were removed using a mask. The raster data acquired post-projection were utilized to operate the InVEST model [32].
## 4 Discussion
Although still the minority in literature for ocular applications, recent studies have begun to focus on the valuable volumetric information OCTA imaging provides, and it could be that the tendency in upcoming years will keep building on these recent studies and that the usage of only flattened 2D OCTA images may eventually become obsolete. This is not to say that valuable information cannot be extracted from 2D en face images, but rather that a 3D analysis enrichens the information and can provide a more comprehensive analysis of healthy and pathological situations. As mentioned in the previous paragraph, open databases of OCTA images are starting to become more available; due to this, it is likely that segmentation tasks in OCTA imaging will gradually see less and less studies that apply only traditional methods, such as thresholding, and that there will be an increase in the application of deep learning methods. The actual segmentation step of OCTA images may also become less common, as deep learning methods can also directly classify images without computing any hand-crafted features. Still, the 3D visualization and quantitative analysis of vasculature is bound to keep its importance, especially in fields where the non-invasive analysis of neovascularization and vascular network complexity are of fundamental importance, such as cancer [104]. In the case of direct classification of images using deep learning methods, recently there has been a significant increase of also employing \"explainability\" methods, such as Grad-CAM [105], that can highlight what part of the image is the most influential for the final classification decision. Future studies focusing on the classification of OCTA images need to continue this trend, as it is fundamental for comparing and evaluating developed methods.
## 2 Materials and Methods
### 3D Acquisition and CAD Modelling  
A classic reverse-engineering approach [23; 24; 25] was used to digitalize the locking plate and the tibia. The length and thickness of the plate were 237 mm and 3.5 mm, respectively. The diameter of screws was 3.5 mm and their length ranged from 30 to 65 mm. The plate and the screws were digitally acquired (Figure 3) through a triangulation-based 3D laser scanner by Hexagon metrology (Hexagon Manufacturing Intelligence, Cobham, Great Britain). This scanner has a high level of accuracy (0.013 mm) which allows it to acquire point clouds at high speed (150,000 points/second). The acquired plate and screw point clouds were post-processed and converted into NURBS surfaces using Polyworks 2015 software (InnovMetric, Quebec, Canada, 2015). Finally, the NURBS surfaces were converted into CAD solid models (Figure 3). The reconstructed CAD models differ by less than 0.035 mm from the point clouds.  
Figure 2: Schatzker classification for tibial plateau fractures [2].  
The CAD model of the tibia, in contrast, was reconstructed from DICOM images of a medium-sized 1.76-m-tall man. To simulate the fracture on the 3D model, reference was made to similar clinical cases. All parts were assembled using 3D parametric CAD software (Figure 4).  
To better understand if the screw positioning affects the stability of proximal tibial fractures treated with LCP, the orientation of four screws was changed, keeping the remaining ones fixed. Four different configurations were investigated by modifying the angle of screws 4-7 (Figure 5).  
Figure 4: The exploded view of bone, plate and screws.  
Figure 3: Hexagon metrology 3D laser scanner HP-L-20.8 (**left**) and reconstructed CAD model of the plate (**right**).  
After analysing some commercial tibial plates, it emerged that the angle of the screws mainly varied from about \(-7^{\circ}\) to \(10^{\circ}\). For this reason, the authors decided to study different configurations by varying the angle of the screws from \(-5^{\circ}\) to \(10^{\circ}\). Considering that the main goal of the study was to investigate if the screw orientation affects the implant stability, this assumption did not affect the reliability of the results. Figure 6 shows the values of the angles for each configuration analysed.
# CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)
## 3 Structure of Chinese Character  
Character-Sets and Strokes ([[https://en.wikipedia.org/wiki/Chinese_characters](https://en.wikipedia.org/wiki/Chinese_characters)]([https://en.wikipedia.org/wiki/Chinese_characters](https://en.wikipedia.org/wiki/Chinese_characters)), (accessed on 1 June 2022))  
The exact number of Chinese characters is unknown because anyone can create characters by combining sounds and meanings. In record, there are 47,035 characters in the 1716 Kangxi Zidian () dictionary. The 1989 Hanyu Da Zidian () dictionary has 54,678 characters, the 1994 Zhonghua Zhai () has 85,568, and the 2004 Yitizi Zidian () contains 106,230 characters. The Japanese 2003 Dai Kan-Wa Jiten (), which uses Chinese characters, contains 50,305 characters, and Korea's 2008 Han-Han Dae Sajeon () contains 53,667 characters.  
Owing to the complexity of Chinese characters, it is difficult not only to understand all Chinese characters but also to write them; therefore, SC characters have been used recently. SC is a simplified version of the number of strokes and shapes compared to the original TC.  
GB2312-1980, published in 1980 by the Chinese government, contains 6763 SC Hanzi (), currently used in mainland China and Singapore. In 1995, this was extended to GBK, including TC (20,914 characters), GBK21003 (21,003 characters), GBK26634 (26,634 characters), and GB18030 (70,244 characters), which were combined with GB2312. In 2013, the Chinese government classified the number of Chinese characters used into three grades according to the difficulty of Chinese characters and published 8105 standard characters including SC (Tongyong Guifan Hanzi Biao, ). There are 3500 characters in Level 1, 3000 characters in Level 2, and Level 3 has 1605 characters. Furthermore, Levels 1 and 2 are regulated by common Chinese characters.  
Chinese characters can be divided into strokes and their components (radicals). A stroke is the smallest unit comprising a character. There are six types of basic strokes, as shown in Figure 3, and a total of 41 types of strokes.  
The strokes form part of a character but have no meaning. The strokes are useful for writing and identifying characters, but they are too small to be used as elements to form an image of a character. Figure 4 shows examples of a large number of strokes and the complexity of Chinese characters. The characters with the most strokes currently in use are the 58 characters for BiangBiang noodles, a type of noodle from the Chinese province of Shaanxi. There are also characters with higher strokes (64, 84, etc.) to denote the complexity of Chinese characters, but they are seldom used.  
Figure 3: Basic six strokes of Chinese characters.
## 4 Implementation
### Spectral Analysis  
In the spectral analysis section, the vibration and current signals from the DC motor are put through a full frequency domain analysis to find fault signatures. The Fast Fourier Transform (FFT) algorithm is applied to convert the time-domain signals into frequency-domain representations, enabling the identification of specific frequency components associated with various fault conditions. By analyzing the amplitude and frequency characteristics within distinct frequency ranges, the spectral analysis provides valuable insights into the nature and severity of faults in the DC motor system. In addition, the spectral features that are taken from the vibration and current signals are used as inputs for later machine learning algorithms. This makes fault diagnosis and prediction more accurate. The Fast Fourier Transform (FFT) is utilized to convert the time-domain signals \(x(t)\) into their corresponding frequency-domain [43] representations \(X(f)\), defined in Equation (23):  
\[X(f)=\int_{-\infty}^{\infty}x(t)e^{-j2\pi ft}dt \tag{23}\]  
where \(f\) represents frequency in Hz, \(t\) denotes time in seconds, and \(j\) denotes the imaginary unit. The FFT result is often plotted with logarithmic scaling to emphasize frequency components that might be hidden in linear plots. The logarithmic scaling of the FFT result \(|X(f)|\) is given by Equation (24):  
\[\text{Log}_{10}|X(f)|=10\cdot\text{log}_{10}(|X(f)|) \tag{24}\]  
Figure 2: Current signatures of starter DC motor operation with healthy and faulty conditions.  
This transformation enhances the visualization of frequency components, particularly those with lower amplitudes, aiding in identifying fault-related signatures. The analysis is shown in Figure 3.
###### Abstract  
In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.  
Internet of Things; COVID-19 sensors; remote healthcare; telemedicine; quarantine monitoring; IoT wearable device +
Footnote †: journal: Sensors
# Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
## 4 Project Architecture
### Virtual Environment
#### 4.2.2 Trigger Logic  
The requirement for this project is that the manufacturing system must transport a specific number of parts within a defined timeframe. The conveyor operates in a fixed and cyclical path, with each transport cycle carrying four parts. Based on this information, the transport speed and the number of cycles is determined to meet the production plan.  
Figure 5: Example of OPC application.  
Given that the system operates in a closed circuit, two control points were implemented. The first control point is located at the unloading station, where the transported parts are counted, and the remaining time to complete all deliveries is tracked. At the end of each cycle, real-time data is transmitted to the simulation model. In the event of a failure, this information is forwarded to the optimization block, where the optimal speed to correct or minimize the issue is calculated. A signal is then transmitted to adjust the working speed accordingly.  
The second control point is positioned at the starting point, where speed adjustments are applied. Speed reprogramming is carried out using a signal emitted by the simulation model through the same data input port. Since the conveyor is designed to respond to this signal by adjusting its operational speed, the system continuously operates at the optimal speed required to fulfill the production plan.
## 2 Materials and Methods
### Environmental Covariates
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indices** & **Description** & **Spatial Resolution** \\ \hline Channel Network Distance (CND) & The distance from a specific point on the terrain to the nearest stream or river in the channel network. & 30 m \\ Channel Network Base Level (CNBL) & A hierarchical classification of the channel network, typically based on flow volume, catchment area, and channel width. & 30 m \\ Convergence Index (CI) & Indicates the tendency of water flow to converge on the terrain surface. & 30 m \\ Profile Curvature (PrCu) & Measures the convexity or concavity of the terrain surface along the direction of the steepest slope. & 30 m \\ Plan Curvature (PICu) & Measures the curvature of the terrain surface in the direction perpendicular to the steepest slope. & 30 m \\ Slope (Slo) & The overall steepness of the terrain at a given point. & 30 m \\ Total Catchment Area (TCaAr) & The area from which all surface water flows to a common outlet point (e.g., a river mouth). & 30 m \\ Topographic Wetness Index (TWI) & An index used to assess the influence of topography on soil moisture distribution. & 30 m \\ Relative Slope Position (RSP) & Describes the relative position of a point within specific terrain features (e.g., valley or ridge). & 30 m \\ LS-Factor (LSF) & Evaluates the potential impact of topography on soil erosion. & 30 m \\ Valley Depth (VD) & The vertical distance from the valley bottom to the highest points on either side. & 30 m \\ Terrain Undulation (TU) & The elevation difference between the highest and lowest points within a defined area (e.g., a \(11\times 11\) window). & 30 m \\ Topographic Position Index (TPI) & Assesses the relative position of a point compared to the average elevation of its surroundings. & 30 m \\ Terrain Ruggedness Index (TRI) & Quantifies the ruggedness or roughness of the terrain surface. & 30 m \\ Elevation (Ele) & The vertical distance of a point on the terrain surface relative to a reference level. & 30 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c} \hline \hline
**Indices** & **Description** & **Spatial Resolution** \\ \hline BIO1 & Annual Mean Temperature & 30 m \\ BIO2 & Mean Diurnal Range (Mean of monthly (max temp - min temp)) & 30 m \\ BIO3 & Isothermality (BIO2/BIO7) (\(\times 100\)) & 30 m \\ BIO4 & Temperature Seasonality (standard deviation \(\times 100\)) & 30 m \\ BIO5 & Max Temperature of Warmest Month & 30 m \\ BIO6 & Min Temperature of Coldest Month & 30 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of bioclimatic covariates.
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 5 Experiments and Results
### Three-Dimensional Modeling  
Upon acquiring the dataset for the modeling area, the creation of the 3D geological model can be swiftly accomplished by opting for different depth sampling accuracies. In this investigation, we have employed PyVista, an open-source Python-based extension package, to formulate the 3D geological model with depth sampling accuracies of 5 m and 1 m. Figure 6 illustrates the 3D geological model under these varying sampling accuracy conditions. When utilizing lower sampling accuracy, the model adeptly captures the overarching geological trends over expansive areas but may compromise precision and local detail. Conversely, higher sampling accuracy is capable of delineating local geological variations, yet it may introduce localized errors. Consequently, for research and presentations focusing on extensive geological changes, opting for lower sampling accuracy  
Figure 5: Comparison of raw data with predicted data.  
is recommended, whereas higher sampling accuracy is more appropriate for investigating or depicting localized geological conditions.