esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 5 Comparison of The Three Types of Sensor Networks
### Data Quality  
_Ranking:_ SSN \(>\) VSN \(>\) CSN  
_Reasons:_  
Good data quality is essential for developing TNGAPMSs. The data quality of the low-cost portable ambient sensors used in TNGAPMSs is poorer than that of the high-end instruments used in the conventional air pollution monitoring systems. However, the low-cost portable ambient sensors still provide a fair enough accuracy and detection range [46], and flexibility in massive deployment.  
_Explanation:_  
In CSN systems, the constraints on weight, size and power consumption (usually powered by small capacity batteries) of the sensor nodes are extraordinary serious. These sensor nodes are typically light weight and small size, and impossible to equip with assisting instruments like temperature and humidity controllers. Moreover, the sensor nodes are not well maintained and may be put into bags or pockets that further lower the data quality. In VSN systems, these constraints are not as critical as that in CSN systems. Adding assisting tools to the sensor nodes is possible and the sensor nodes are well maintained by the professionals. However, the high mobility of the sensor nodes becomes a major factor affecting the accuracy of the sensor readings due to the varying air flow around the sensor head [10]. In SSN systems, limitations on the weight, size and power consumption (powered by power line or renewable energy source) of the sensor nodes are relaxed. The sensor nodes are able to equip with assisting equipment to ensure the data quality. The network connectivity and the sensor node's power supply are guaranteed and the reliability of the sensed data is improved due to stationary characteristic. The data quality of SSN systems is the highest among these three types of systems, followed by the data quality of VSN systems and CSN Systems.  
After we have an in-deep understanding of these six comparison properties described above, we graded these properties of SSN, CSN and VSN systems using the grading code defined as: '0' means 'None'; '1' means 'Low/Short/Inconvenient'; '2' means 'Medium' and '3' means'High/Long/Convenient'. The final grade of each comparison property of SSN, CSN and VSN systems following the descriptions above are shown in Figure 11.
## 3 Methodology
### Linguistics Processing  
Part-of-speech (POS) tagging is one of the most important tasks for language processing. Previously, many approaches have been used to parse tweets, for instance, [PERSON] et al. presented TweeboParser by exploiting the TurboParser [29]. Similarly, [PERSON] et al. analyzed tweets with universal dependencies (UD) by defining the extended guidelines for tweets tokenization [30]. [PERSON] [31] have comprehensively discussed the part-of-speech (POS) tagging and different tag-sets and the related approaches to adopting those tag-sets. The Stanford, Lancaster Oslo/Bergen (LOB), CLAWS, VOLSUNGA, POS Tagger, etc., are briefly introduced, and Stanford NER achieved an accuracy of 97.4%, among others [31]. Therefore, it was widely applied to various text types and for diverse entity types, such as blogs, tweets, newspapers, locations, fictions, persons, chemicals, etc.  
The list of all requirements with part-of-speech tagging to understand the structure and linguistics features of content includes the following.  
* Tokens: Tokenization of the tweet content.
* POS: Part-of-speech for tagging of each word.
* NER: Named entity recognition for entity identification.
* LEMMA: Lemmatization for getting the base word.
* DepRel: Relation dependency among words.  
The Stanford CoreNLP is a natural language processing (NLP) toolkit that includes various grammatical analysis tools, including a parser, named entity recognizer (NER), part-of-speech (POS) tagger, sentiment analysis, and open information extraction tools, etc. and its worth is well proven in the domain of natural language processing [32,33]. The accuracy of its NER and POS tagging is exploited by researchers of this domain with high accuracy of around 97.4%. It also has the edge over similar approaches in that it has a complete package of different grammar rules.
## 2 Methodology
### Quality Control Process
#### 3.1.2 PD Database  
The PD database was prepared to obtain the PoF based on defect density probability in elements. To this end, the damages on each RC element have been detected and registered, distinguishing those related to environmental conditions (e.g., carbonation, corrosion, concrete layer detachment) and structural reasons (e.g., cracks due to overload or fatigue) [47]. Therefore, it is necessary to analyze surface damages to assess defect exposure. Meanwhile, the durability feature directly links surface defects [12]. However, the traditional method relies on subjective interpretation based on visual inspection. Consequently, image-processing software monitors the rate of defect expansion without any human error disorder [14]. Through this tool, it is possible to assess structures in terms of quality in their lifetime based on quantitative measurements of the defect.  
Eventually, the geometry of all elements was considered by their approximate volume to estimate the defect density. To separate segments with and without defects, a value of 0 was given to intact RC material and for a defect segment the value was 1. Meanwhile, for segments with a high level of defects, a value of 2 was assigned. For this research case, according to Eurocode 2 and ACI 224R-01 [48], the limit state of crack width was considered to be 0.3 mm. Hence, 0.3 mm for cracks was the limit state for these RC elements. Moreover, the coefficient of these defects was 1 until the rebar was exposed through that type of crack, leading to a value of 2 if higher. This grading is summarized in Table 2.  
Figure 4: Data analysis process.  
The exposure factor is a subjective value that the inspector assessing risk must define based on Table 2 in pre-posterior analysis. After data gathering (PD and SI), the dataset was analyzed based on the following step by calculating two types of grades for bridge comparison and their quality index. These grades have been calculated in steps 2 and 3 based on the case study database.
## 3 Results and Discussion
### Characterization of the Nanoparticles Containing Zinc Complexes  
#### 3.2.1 SEM/EDX Analysis  
Qualitative EDX analysis is based on identifying the specific energies of X-rays emitted by the atoms of a certain chemical element. Quantitative analysis involves measuring the intensity of the spectral peaks corresponding to the preselected elements both for the studied samples and for standards, calculating the intensity ratios (k values) and converting these k values into percentage concentrations (%wt and %at). The EDX spectrum of the ZnNPs sample, the elemental chemical composition of the ZnNPs sample and the EDX elemental mappings of the Zn, O and C atoms are shown in Figure 1. In the EDX spectrum (Figure 1a), the characteristic peaks from 1.012 (L\(\alpha\)), 8.630 (K\(\alpha\)) and 9.572 (K\(\beta\)), corresponding to the photon energies of x-ray emission lines belonging to the principal K- and L spectral series, confirm the existence of the zinc atoms in the biosynthesized nanoparticles. The low-intensity peaks detected in the EDX spectrum indicate the presence of small amounts of Ca and Mg elements coming from the vine leaves. The EDX elemental mapping analysis of the ZnNPs sample revealed a homogeneous distribution of the Zn, O and C elements within the sample (Figure 1d-f).  
Figure 1: _Cont._  
Scanning electron microscopy was used to investigate the surface morphology of the ZnNP sample. From the SEM microphotographs recorded at 1000\(\times\) magnification degree (Figure 1b) and respectively at 2020\(\times\) magnification degree (Figure 1c), it can be noticed that the external surface of the sample looks like it would be makeup of agglomerations of nanoparticles that form a cluster-like structure. This agglomeration can be attributed to the fact that the NPs derived from plants possess a high surface area, and they strongly stick to each other [34].
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods  
### Continuous Coverage Constraint of Mega Constellation  
Considering the numerous orbital planes and satellites in one mega constellation, the continuous coverage constraint is redefined in this section. The configuration requirements for mega constellations that satisfy the coverage constraint are proposed. The intersatellite motion constraints during the lifetime are also analyzed.
## 4 Discussion
### Legacy Effect Comparison between DBFs and DNFs  
The DBFs not only showed higher temporal stability than DNFs, but they also demonstrated more similarity in the lengths of their legacy effects with droughs. This result was consistent with a previous study which found a longer legacy effect for DNFs than DBFs [19]. Our results based on the EVI and GPP might also be a bridge for previous research about drought legacy effects on forests based on different data sources. Some studies showed a legacy length less than 4 years [15; 19], while others showed a legacy no longer than 2 years [13]. The former research works were based on tree-ring data across Europe and America, which reflected the xylem accumulation or carbon partitioning strategy. The latter research was based on multi-source GPP data, which reflected ecosystem photosynthesis or carbon fixation. The diverse time lags implied that forests respond to water differently under various ecological processes. These differences call for more field measurements and more research about carbon partitioning strategies during droughs. Moreover, the relatively converging long time lag of DBFs suggests that different broadleaf forest communities have acclimated to droughs. This result provides a reference in studying the response of photosynthesis to droughs in different vegetation types.
## 3 C/N Penalty of RoF Link
### C/N Penalty of RoF Link with Irregular \(\theta\)
#### 3.3.1 Irregular \(\theta\) Values for Scheme A1, A2, A3 and A4  
Irregular \(\theta\) is the value chosen from 0\({}^{\circ}\) to 360\({}^{\circ}\) so that the RoF link produces the smallest DF. Irregular \(\theta\) values are different for different schemes. Irregular \(\theta\) values for scheme A1 are sought with the following stages:  
1. Calculate \(P_{rec}(L)\) using Equation (28) with \(r\) = 1 (4:4), \(\gamma\) = 1/4, \(m\) = 0.1, \(\theta\) = 0\({}^{\circ}\), \(P_{in}\) = 1 mw (0 dBm), \(\lambda_{c}\) = 1550 nm (\(D\) = 17 ps/(nm.km)), and \(f_{m}\) = 60 GHz at \(L\) = 0, 0.1, 0.2, \(\ldots\), 5 km.
2. Calculate the C/N penalty from the calculation results (a) using Equation (29).
3. Calculate DF of all calculation results of (b) using Equation (30).
4. Repeat steps (a) to (c) for \(\theta\) = 1\({}^{\circ}\), 2\({}^{\circ}\), 3\({}^{\circ}\), \(\ldots\), 360\({}^{\circ}\).
5. Find the \(\theta\) value in step d), which produces the smallest DF (DF\({}_{\text{min}}\)); this \(\theta\) is called irregular \(\theta\).
6. Repeat steps (a) to (e) for \(m\) = 0.2, 0.3, \(\ldots\), 4.
7. The result of the calculation is shown in Figure 5a-d.  
The examination result curve of irregular \(\theta\) for scheme A1 is shown in Figure 5a. The irregular \(\theta\) value on scheme A2 is searched by repeating steps (a) to (f) using \(r\) = 3/4, \(r\) = 1/2 for scheme A3 and \(r\) = 1/4 for scheme A4. The examination result curve of irregular \(\theta\) for schemes A2, A3, and A4 can be seen in Figure 5b-d.  
Figure 5 reveals that for each \(m\) used, there are two values of irregular \(\theta\): irregular \(\theta\) I (\(\theta_{\text{I}}\)) and irregular \(\theta\) II (\(\theta_{\text{II}}\)). The different \(m\) value and different schemes produce different irregular \(\theta\). The minimum DF for irregular \(\theta\) I (\(\theta_{\text{I}}\)) and irregular \(\theta\) II (\(\theta_{\text{II}}\)) is shown in Table 4.  
The values of DF for each irregular \(\theta\) at \(0.1\leq m\leq 4\) of schemes A1, A2, A3 and A4 are depicted in Figure 6. The DF values of the RoF link with an irregular \(\theta\) at \(m\) = 1 of schemes A1, A2, A3 and A4 are 0.089, 0.119, 0.113, and 0.070, respectively, which shows that at \(m\) = 1, irregular \(\theta\) with scheme A4 and overcome DPF most effectively on the RoF link using DD-MZM with CA, which is biased at \(\gamma\) = 1/4.
## 2 Background and Setting
### The CONGEST and CONGEST-CLIQUE Models of Distributed Computing  
In the standard CONGEST model, we consider a graph of \(n\) processor nodes whose edges represent communication channels. Initially, each node knows only its neighbors in the graph and associated edge weights. In rounds, each processor node executes computation locally and then communicates with its neighbors before executing further local computation. The congestion limitation restricts this communication, with each node able to send only one message of \(\mathcal{O}(\log(n))\) classical bits in each round to its neighbors, though the messages to each neighbor may differ. In the cCCM, we separate the communication graph from the problem's input graph by allowing all nodes to communicate with each other, though the same \(\mathcal{O}(\log(n))\) bits-per-message congestion limitation remains. Hence, a processor node could send \(n-1\) different messages to the other \(n-1\) nodes in the graph, with a single node distributing up to \(\mathcal{O}(n\cdot\log(n))\) bits of information in a single round. Taking advantage of this way of dispersing information to the network is paramount in many efficient CONGEST-CLIQUE algorithms. The efficiency of algorithms in these distributed models is commonly measured in terms of the _round complexity_, or the number of rounds of communication used in an algorithm to solve the problem in question. A good overview of these distributed models can be found in [15].
## 2 Power Spectral Density of Selected Disturbances
Similar to Figures 12 and 13, the influence of gyro drift at high time constants may be noted (low frequencies). Also, noises for high frequencies are observable. Frequencies of ca. 100 Hz and higher, for the angle calculated by complementary filters with time constants \(T=10\) and \(T=100\), are significantly burdened with, e.g., process noises. In most solutions, these frequencies can be neglected and filtered. As shown in Figure 14, the best time constant within those presented is \(T=10\). For the complementary filter with time constant \(T=1\), significant noise is observed in the frequency range of 0.1 to 50 Hz (both for variable and constant operating temperature). Noises in this frequency range are much smaller for the complementary filter with T = 10. For the complementary filter with the time constant \(T=100\), a significant influence of the gyro drift is observed (frequencies 0.001-0.05 Hz); in comparison with the two other filters, the noise level is higher in the entire frequency range. The discussion above shows that it is possible to use the one-sided PSD method not only for a single sensor analysis but also for the synthesis of the complementary filter for AHRS. Figure 14: One-sided PSD for \(10^{6}\) samples of AHRS roll angle when the sensor temperature value was not stabilized (**top**) and at stabilized sensor temperature value (**bottom**).
## 3 Insulator Pollution Discharge Test  
### Experiment Settings  
Discharge of pollution insulators is a complicated process. The pollution insulator's surface may form the local arc in a humid environment [27; 28; 29]. An insulator surface with a higher pollution class absorbs water, and then the electrolyte in the pollution layer is dissolved and ionized, resulting in increased leakage current on the insulator surface with the increase in insulator surface conductance. The discharge magnitude of pollution insulators is greatly affected by air humidity, pollution class, and voltage [30; 31]. This paper fully considers the influencing factors of insulator discharge and designs an insulator pollution discharge magnitude detection experiment based on the UV imaging method. Figure 9 gives the schematic diagram of the pollution insulator discharge detection experiment system.  
This experiment selected a YDTW-2000 kVA/500 kV AC transformer (Jiangsu Liyu HIGH Voltage Equipment Co., Ltd., Yangzhou, China) to provide voltage. The laboratory console has a voltmeter and ammeter to collect the data on voltage and leakage current  
Figure 8: Color image segmentation result. (**a**) Color spot image after Gaussian color processing. (**b**) Image segmentation result. (**c**) Graph morphology processing result.  
Figure 9: Schematic diagram of experiment system.  
across the insulator. C0 and C1 are voltage-dividing capacitors. R0 and R1 are protection resistors. The artificial aerosol chamber has a length of 1.0 m, a width of 0.8 m, and a height of 0.75 m, which can accept a 27 kV insulator. A humidifier is installed in the aerosol chamber. The experimental pressure is standard atmospheric pressure. The ambient temperature is 22 \(\pm\) 2 \({}^{\circ}\)C. The insulator sample is placed in the artificial aerosol chamber, and the relative humidity of the environment is maintained at about 80%.  
The experiment uses an OFIL Superb UV imager (OFIL, Ness Ziona, Israel) (as shown in Figure 10 and Table 1) to take videos of the entire process of insulator sample discharge. The minimum discharge sensitivity of the UV imager is 1.3 pC (10 m). Its detection spectral range is 240-280 nm. Its viewing angle is 5\({}^{\circ}\)\(\times\) 3.75\({}^{\circ}\). Its minimum detection distance is 3 m, and its imaging resolution is 640 \(\times\) 480 pixels. The frame rate of UV video is 25 frames per second.  
The insulator sample in the experiment is the ZS-10/4 porcelain insulator, as shown in Figure 11. The specific creepage distance of the ZS-10/4 outdoor post insulator is 25 mm/kV. The detailed parameters of the ZS-10/4 porcelain insulator are shown in Table 2.
# Lift-Thrust Integrated Ducted-Grid Fusion Configuration Design for a Ducted Fan Tail-Sitter UAV
## 4 Analysis of the Influence of Fusion Configuration Design Parameters
### The Impact of the Number of Flow Grids  
When keeping the coverage range and number of cells of the circumferential grid unchanged, changing the flow direction of the grid cells actually divides the width of the grid channels, and the aerodynamic results obtained are shown in Figure 18. As the number of flow grid cells increases, the thrust increases while the torque decreases, resulting in an increase in additional lift. It can be seen that the number of flow elements has a more significant impact on the entire composite configuration compared to the number of circumferential grid elements.  
After the increase in flow grid cells, the separated vortices in the original hysteresis region are divided into multiple small separated flows, which further expands the low-pressure area in the front and improves the flow field quality in the rear of the duct, as shown in Figure 19. When the combination form of the circumferential \(\times\) flow direction combination form is \(2\times 1\), a small separation area appears at the rear of the duct. However, when the combination form is \(2\times 4\), this separation phenomenon is improved. In addition, due to the increased flow density of the grid, the constraint on the blade tip airflow is strengthened, significantly improving the additional resistance generated, resulting in a decrease in torque.  
Figure 19: Pressure distribution cloud images of the fusion configuration with different numbers of flow grids (the number of flow grids is 1, the number of flow grids is 4).  
Figure 18: The influence of different numbers of flow grids on the aerodynamic characteristics of ducted propellers: (**a**) thrust; (**b**) torque; (**c**) lift.  
It should be noted that, in this study, the range of parameter variations selected during the grid parameter influence analysis is relatively small. This is related to the lift generation mechanism of the duct-grid composite configuration. The grid angle is fixed at 60\({}^{\circ}\) to create a stable \"vortex cushion\" within the grid channel. If the angle is too large, unstable separated flow occurs inside, leading to additional drag. Conversely, if the angle is too small, internal flow leakage occurs, weakening the lift effect and affecting the number of grids in the flow direction, which has similar considerations. The circumferential angle and the number of circumferential grids are determined based on the relative range of the ducted and grid sections. Excessive circumferential distribution reduces the lift effect and causes uneven loading on the duct due to slotting, which may compromise structural safety.
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 5 Results and Discussion
### Influence of Soft-NMS  
As shown in Table 2, due to the existence of multiple targets in the image and the small size of some targets, the combination of the ECANet attention mechanism and Soft-NMS algorithm has achieved good detection results. The mAP value reaches 97.1%, which is 1.07% higher than the YOLOV4-tiny + ECANet. The FPS after the introduction of the Soft-NMS algorithm is 1.59, which is lower than that of the YOLOV4-tiny + ECANet model. The decrease in FPS is due to the addition of an attention mechanism, but the decrease is not significant. This shows that after the introduction of this optimization strategy, the learning and training of small targets are more in-depth. The Soft-NMS is used to correct candidate boxes that are retained by using the non-zero feature of lower confidence candidate boxes, thus improving the detection effect of the model. As shown in Figure 9, the log-average miss rate of the traditional NMS algorithm is higher than that of the Soft-NMS algorithm, and there are more categories with false detection than that of the Soft-NMS algorithm, which proves the effectiveness of the improved model.
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 2 Fundamental Theory and Methodology  
### MEMS Sensing Mechanisms  
The stability of unstable rock masses gradually decreases with the deterioration of their main structural planes [19]. Under normal fretting conditions, the vibration characteristics of unstable rock masses differ from those of the bedrock to some extent, and these differences become more pronounced with increasing degrees of structural deterioration. MEMS sensing provides a novel technology for monitoring unstable rock mass collapse, owing to its high sensitivity, low power consumption, and compact dimensions. As shown in Figure 1a, vibration signals can be acquired by affixing MEMS sensors to the unstable rock mass and bedrock using gypsum bonding or bolting methods, combined with wired or wireless transmission systems. Figure 1b schematically represents the MEMS sensor as a cubic enclosure containing a miniature metallic sphere. When subjected to gravitational forces and micro-vibrations from unstable rock masses, this sphere undergoes inertial displacement, thereby inducing electrical signals of varying magnitudes through wall impacts. The system ultimately outputs triaxial acceleration, velocity, and displacement measurements.  
MEMS sensors acquire vibration data from rock masses through direct contact measurements. As illustrated in Equation (1), a conventional MEMS accelerometer continuously measures triaxial acceleration components, including gravitational acceleration, through its embedded micro-electro-mechanical structures.  
\[\left\{\begin{array}{l}\stackrel{{\rightarrow}}{{a}}(t)= \frac{\stackrel{{\rightarrow}}{{F}}(t)}{m}\\ \left|\stackrel{{\rightarrow}}{{a}}(t)\right|=\sqrt{a_{x}^{2}(t) +a_{y}^{2}(t)+a_{z}^{2}(t)}\end{array}\right. \tag{1}\]  
where \(\stackrel{{\rightarrow}}{{F}}(t)\) represents the impact force acting on a rock mass of mass m. \(\stackrel{{\rightarrow}}{{a}}(t)\) represents the triaxial acceleration measured by MEMS sensors, \(\stackrel{{\rightarrow}}{{a}}(t)=(a_{x}(t),a_{y}(t),a_{z}(t))\).