esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
## 3 Proposed Model
### Steady-State Phase
```
1:Input: Clusters formed in Setup Phase
2:Output: Data Transmission to the IoT Cloud
3: N = total number of clusters
4: n = total nodes in a cluster
5: i = 0, j = 0, s = no of slots
6: Node detects a new event
7: If (distance to event > DE)
8: do not report the event
9: else
10: report event in its slot
11: end if
12: for i = 1 to s
13: if (CH receives data from its member in VP)
14: continue listening till the end of the slot
15: else
16: turn radio OFF
17: end if
18: end for
19: for i = 1 to s
20: if (Current slot = last time slot for intra-cluster communication)
21: wait to receive data from downstream CH in the inter-cluster communication period
22: else
23: listen to channel in VP of next slot
24: end if
25: end for
26: if (Data is received from downstream CH in VP)
27: Continue listening till the end of the slot
28: else
29: Turn off radio
30: end for
31: for i = 1 to N
32: CH sends aggregate data to its upstream CH /BS
33: end for
```  
**Algorithm 2**Pseudo-Code for Steady-State PhaseFigure 6: Functioning of the BS in a complete round.
###### Abstract  
To study the effect of a bird striking engine fan on the rotor system, a low-pressure rotor system dynamic model based on a real aero-engine structure was established. Dynamic equations were derived considering the case of the bird strike force which transferred to the rotor system. The bird strike force was obtained from the bird strike process simulation in LS-DYNA, where a smoothed particle hydrodynamics (SPH) malard model was constructed using a computed tomography (CT) scanner, and finite element method (FEM) was used to simulate the bird strike on an actual fan model. The dynamic equations were solved using the Newmark-\(\beta\) method. The effect of rotational speeds on the rotor system dynamics after bird strike was investigated and discussed. Results show that the maximum bird impact force can reach 104 kN at 3772 r/min. Impact time is only 0.06 s, but the bird strike on fan blades lead to a transient shock on the rotor system. Under the action of transient shocks, the rotor system displacement in the horizontal and vertical directions increase sharply, and the closer the mass point is to the fan, the more it is affected; the vibration amplitude at the fan will increase 15 times within 0.1 s of the bird strike and will gradually decrease with the effect of damping. The dynamics of the rotor system changes from a stable single periodic motion to a complex irregular quasi-periodic motion after a bird strike, and the strike force excites the first-order vibrational mode of the rotor system. This phenomenon occurs at all speeds when bird strikes occur. Bird strikes will cause resonance in the rotor system, which may cause damage to the engine. It was also seen that the bird strike force, and hence the effects on the rotor system, increases as the engine rotational speed increases; the peak force is larger and the number of peaks has increased. The impact force at 3772 r/min is 99.5 kN higher than at 836 r/min, and three additional peaks emerged. This effect is more reflected in the amplitude, and the overall vibration characteristics do not change. Combining the bird strike with the rotor dynamics calculation, the dynamic response of the aero-engine rotor system to bird strike is studied at different flight stages, which is of guiding significance for power evaluation of aero engines after bird strike.  
dynamic responses; bird strike; aero-engine rotor system; SPH +
Footnote †: journal: _Applied Sciences_
## 2 Materials and Methods
### Experimental Design  
A laptop (Lenovo ThinkPad T15 g Gen 1) with Intel Core i7-10750H, 16 GB DDR4 RAM, and NVIDIA RTX 2080 Super was used in the computer simulation. The script was implemented using Python 2.7 programming language and ROS Melodic Morenia in Ubuntu 18.04 LTS operating system.  
The simulation was performed at \(S=0.5\) m, based on the camera configuration of [PERSON] et al. (2019) [23]. Sensitivity analysis was performed at \(d_{l}\) values of 1, 10, 100, 1000, and 10,000 m. The literature review showed that 20,000 m was used in the study of [PERSON] et al., 2021 [26]. However, the basis for the \(d_{l}\) used in their study was not explained. Hence, sensitivity analysis was performed in this study to establish the sufficient \(d_{l}\) that would not affect \(r_{d,th}\) and \(r_{d,sim}\). The resulting values of \(r_{d,sim}\) were compared to \(r_{d,th}\). Inferencing speed of 2.4 _fps_ and travel velocity of 2.5 m/s were used for the sensitivity analysis to have an \(r_{o}<1\) at \(S=0.5\) m. If a faster inferencing speed or slower travel was used, \(r_{o}\) could be equal to or greater than 1. This result will fall into Case 1 or 2 and could not be used for sensitivity analysis.  
The model was then simulated at different values of \(\overset{\sim}{v}\) and \(fps\), as shown in Table 1 to estimate the detection performance of combinations of CNN model, hardware, and \(\overset{\sim}{v}\). Forward walking speeds using a knapsack sprayer typically ranged from 0.1 to 1.78 m/s [35; 36; 37]. On the other hand, the travel velocities of boom sprayers ranged from 0.7 to 2.5 m/s [38; 39; 40; 41]. Solid fertilizer application using a tractor-mounted spreader operated at 0.89 to 1.68 m/s [13; 42]. Finally, a mechanical weeder with rotating mechanisms worked at 0.28-1.67 m/s [43; 44]. The literature review showed that 0.1 m/s was the slowest [41] and 2.5 m/s was the highest [40] forward velocities found. The mid-point velocity of 1.3 m/s estimated the typical walking speed using knapsack sprayers [35; 36; 37] and forward travel velocities of boom sprayers and fertilizer applicators [38; 39; 40; 41].  
In addition, 2.4 and 22 _fps_ were the inferencing speeds of YOLOv3 running on an NVIDIA TX2 embedded system and a laptop with NVIDIA 1070 TI discrete GPU as described in the study of [PERSON] et al. (2019) [11]. Finally, 12.2 _fps_ approximated the inferencing time of a custom CNN architecture or SSD MobileNetV1 CNN model optimized in TensorRT and implemented an embedded system [22; 23].  
The effect of increasing \(S\) using multiple camera modules in preventing missed detection was also performed on treatments falling under Case 3.
# Precision Analysis and Error Compensation of a Telescope Truss Structure Based on Robotics
## 4 Secondary Mirror Adjustment Experiment
### Experimental Results  
The field of view is converted by rotating the plane mirror and the interferometer synchronously, enabling the central field of view and the edge field of view of the system to be detected. The measured system aberrations of the three reference field points are shown in Figure 16.  
Figure 16: The detection results of each field of view in the maladjusted state: (a) F (0,0), 0.222\(\lambda\) (b) F (0,–0.1), 0.271\(\lambda\) and (c) F (0,0.1) 0.253\(\lambda\).  
Figure 15: Experimental diagram showing the detecting primary and secondary mirror (SM) system.  
The wavefront fitting Zernike fringe coefficients of the three reference field points were used to inform the established misalignment solution model, with the corresponding compensation amounts required for the SM listed in Table 7.  
It should be noted that the compensation amounts in Table 7 are ideal calculated values, not adjusted specific values. According to the sensitivity matrix mapping relationship, it can be known that the tolerance in micrometer level has almost no effect on the aberration of the system. The accuracy of the Stewart platform used in the experiment can meet the requirements of the system. The SM was adjusted as directed by the calculated misalignment, with the detection results of the wavefront error associated with the on-axis and edge fields of view relating to the adjusted system wave aberration shown in Figure 17.  
Owing to the limitations concerning the adjustment accuracy, it is impossible to adjust to the ideal wavefront state simultaneously. After the first adjustment, the compensation amount required for the SM is recalculated and adjusted. After multiple adjustments, the wave aberration RMS associated with the three fields of view is better than \(\lambda/15\), which satisfies the requirements of the system application. Detection results after multiple adjustments is shown in Figure 18. This result shows that the accuracy of the Stewart platform used in this paper can meet the requirements of system adjustment. Therefore, the robot arm with the same accuracy can also achieve the adjustment accuracy of the secondary mirror, which also shows the feasibility of the secondary mirror truss based on robotics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\Delta\)**x (mm)** & \(\Delta\)**y (mm)** & \(\Delta\)**z (mm)** & \(\Delta\)**\(\alpha\) (°)** & \(\Delta\)**\(\beta\) (°)** \\ \hline
0.395 & \(-\)0.423 & 0.036 & 0.02 & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Compensation amounts for the SM.  
Figure 17: Detection results for each field of view after adjustment. (**a**) F (0,0), 0.094\(\lambda\) (**b**) F (0,\(-\)0.1), 0.116\(\lambda\) and (**c**) F (0.0.1) 0.128\(\lambda\).  
Figure 18: Detection results after multiple adjustments. (**a**) F (0,0), 0.063\(\lambda\) (**b**) F (0,\(-\)0.1), 0.075\(\lambda\) and (**c**) F (0.0.1), 0.073\(\lambda\).
# Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model
## 4 Debugging and the Knowledge Base Management System
### Interactive Analysis for Informed Editing of the Synsets  
In tandem with the classification and clustering models, an interactive analysis module was developed to facilitate informed decisions regarding editing of the synsets by knowledge engineers. This module leverages techniques of reducing dimensionality and visual representations to provide a user-friendly exploration of the underlying semantic relationships within the data.  
1. **Visualizing semantic clusters**: The module offers a 2D scatter plot depicting the projections of the words' embedding vectors onto the first two principal components (PCs) derived from the principal component analysis applied to the training data. Projections of the testing data are overlaid for comparison. Users can interactively select data points or regions on the plot to retrieve the corresponding sentence examples. Additionally, the ability to overlay WordNet lexnames, synset labels, or hypernym labels further aids in comprehending whether specific data clusters align with these semantic categories. This visual exploration empowers knowledge engineers to identify potential refinements of the synsets by uncovering clusters that deviate from the expected semantic groupings.
2. **Reducing the dimensionality and neighborhood analysis**: While PCA provides a valuable 2D visualization for an initial analysis, neighborhood component analysis can be used to assess the information retained in higher dimensions. The NCA projection, presented as another interactive 2D graph, allows users to evaluate the feasibility of disambiguating the target word based on the inherent structure of the data. This visualization can also serve as a valuable tool for identifying potential annotation errors within the training data by highlighting points that deviate significantly from their expected semantic neighbors.
3. **3D exploration for enhanced insights**: For a more comprehensive perspective, the system offers the option to explore the data in a 3D scatter plot. Similar to the 2D view, users can select between PCA and NCA for reducing the dimensionality, allowing for in-depth examinations of the potential semantic relationships across multiple dimensions. This interactive 3D environment provides additional opportunities for knowledge engineers to refine their understanding of the data and make informed decisions regarding modifications of the synsets.  
Overall, the inclusion of the interactive analysis module empowers knowledge engineers with a comprehensive set of visual tools to analyze the semantic relationships within the data [28,29]. This facilitates a more informed approach to editing synsets, ultimately enhancing the effectiveness of the WSD system.
## 4 Conclusions  
This work aimed to investigate the influence of fillers, such as glass fibers, carbon fibers, and copper particles, on the grinding performance of additively manufactured resin-bonded grinding wheels using the DLP process. Since the most applicable resin bond grinding wheels in the industry are produced incorporating phenol-based resin, it was challenging to make this resin UV-curable. Therefore, UV-curable phenol resin was developed by Osnabruck University and utilized for the first time to print grinding wheels. Mechanical tests were conducted on tensile samples to identify a correlation between grinding performance and mechanical properties. The findings indicate that the mechanical properties can be significantly altered by the incorporation of additives, which notably impact the grinding performance. Some of the main achievements are listed below:  
1. Adding different additives makes a notable improvement to the mechanical properties, including the tensile strength and E-modulus of the grinding wheel compositions. This was more prominent for glass fiber with a 50% increase in the tensile strength and carbon fiber with a 16% increase in E-modulus compared to the phenol-SiC (TS1) composition;
2. Comparing the grinding forces showed a significant change (up to 25%) between the compositions when the highest depth of cut (100 \(\upmu\)m) was applied. The increase in the depth of cut led to notable growth in the force values of up to 50% of all examined grinding wheels. A higher normal force for GW-2 mainly originated from improved grain retention by adding glass fibers;
3. Reinforced grinding wheels with glass fiber represented the highest wear resistance compared to the other examined grinding wheels with up to 75% reduction in tool wear value;
4. For shallow grinding, the results indicate that GW with lower strength produces better surface quality mainly due to the higher tool wear and lower forces. However, with deep grinding, GW-2 represented improved surface quality compared to the other examined grinding wheels;
5. The surface morphology of the grinding wheels following the grinding operation indicates the presence of a worn bond, grain pull-out, and dull grains, with these effects being less significant for the grinding wheel reinforced with glass fiber.  
Conceptualization, M.B.; Methodology, A.H., M.B. and F.S.; validation, M.B. and F.S.; investigation, M.B. and F.S.; data curation, M.B. and F.S.; writing--original draft preparation, M.B. and F.S.; writing--review and editing, M.B. and F.S.; supervision, B.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Zentrales Innovationsprogramm Mittelstand (ZIM) under grant No. KK5080103 CD1.  
Not applicable.  
The authors declare no conflicts of interest.
# The Spatial Structure of Passively Simulated Atmospheric Boundary Layer Turbulence
## 4 Results and Discussion
### Vertical Spatial Structure
However, when the distance is small, the Davenport coherence model is significantly lower than the measured data, and when the distance is large, there is a deficiency of overestimating the low-frequency value. The undecided parameters for the modified vertical coherence model will vary with the vertical spacing, integration scale, average wind speed, and height above ground, but the fluctuation range is generally small. To make practical engineering applications easier, the fitting results can be approximated by an average value. For the vertical coherence function of the along-wind component, the modified von Karman model takes \(a_{1}=0.678\) and \(b_{1}=1.175\), and the modified Dryden model takes \(a_{2}=0.72\) and \(b_{2}=2.293\). The reference model has a simple structure and high fitting accuracy, and its undetermined coefficients can be obtained by fitting experimental data. model are \(a_{2}=0.329\) and \(b_{2}=2.205\). The reference model has a simple structure and clear physical meaning; it can sufficiently describe the \"turn around\" phenomenon of the vertical coherence function of the lateral fluctuation component in the low-frequency region, and its undetermined parameter fitting method has been presented in the relevant literature [37]. The Krenk model and Davenport model can roughly reflect the vertical coherence distribution under different vertical spacings, but the fitting accuracy has some flaws and needs to be improved further.
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 2 Thermal Modeling
### Radiometry  
To accurately recompute the temperature field, the distance from the object must be provided in order to be able to use the calibration curve of the thermal camera, usually given at a very short distance from the target [14,15].  
The sensor function can be modeled in various ways. From the literature, here we report two sensor functions and their respective proposed implementations.
## 2 Data and Signal Processing
After this step, the data was then subdivided into two parts to build the LDA model (60% for learning and 40% for testing) and three parts for building the two others models based on neural networks (60% for learning, 20% for validation and 20% for tests).
## 3 Proposed Method
### Data Embedding Procedure
```
1:procedureEmbed(\(x_{i}^{1},x_{i}^{2},m_{i}\)) \(\triangleright\) Input: Pixel pairs \((x_{i}^{1},x_{i}^{2})\) from \(I_{1}\) and \(I_{2}\), and 5-ary data \(m_{i}\). 2:\(y\leftarrow[x_{i}^{1},x_{i}^{2}]\); \(\triangleright\) Initialize \(y\) as a vector of the pixel pairs. 3:\(f\leftarrow(x_{i}^{1}\cdot 1+x_{i}^{2}\cdot 2)\ mod\ 5\); \(\triangleright\) Compute \(f\) for modulo operation. 4:\(s\gets m_{i}-f\); \(\triangleright\) Determine difference \(s\) for data embedding adjustment. 5:if\(s=0\)then
6: no change needed; \(\triangleright\) No adjustment required for \(s=0\). 7:elseif\(s=1\)then
8:\(y_{1}\gets y_{1}+1\); \(\triangleright\) Increment first pixel value for \(s=1\). 9:elseif\(s=2\)then
10:\(y_{2}\gets y_{2}+1\); \(\triangleright\) Increment second pixel value for \(s=2\). 11:elseif\(s=3\)then
12:\(y_{1}\gets y_{1}-1\); \(\triangleright\) Decrement first pixel to encode \(s=3\). 13:\(y_{2}\gets y_{2}+1\); \(\triangleright\) Increment second pixel to balance embedding. 14:elseif\(s=4\)then
15:\(y_{1}\gets y_{1}+1\); \(\triangleright\) Increment first pixel to encode \(s=4\). 16:\(y_{2}\gets y_{2}-1\); \(\triangleright\) Decrement second pixel to balance embedding. 17:endif
18:return\(y\)
```  
**Algorithm 1** Enhanced EMD with optimal LSB (OLSB) method
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 3 Basic System Design
### Skyline Definition with Metric-Based Searching  
Metric space \(M\) can be defined as a pair \(M=(D,\,d)\), where \(D\) is the domain of objects and \(d\) is the _distance function--d:_\(D\times D\rightarrow\)\(R\) satisfying the following constraints (Equation (1)) for all objects \(a,b,c\in\ D\):  
\[\begin{array}{ll}d\left(a,b\right)\geq 0&\left(\text{non}-\text{negativity} \right)\\ d\left(a,b\right)=0&\left(\text{identity}\right)\\ d(a,b)=d(b,a)&\left(\text{symmetry}\right)\\ d(a,c)\leq d(a,b)+d(b,c)&\left(\text{triangle inequality}\right)\end{array} \tag{1}\]  
In this metric space, considering that smaller values are preferable to larger ones for a set of \(l\)-dimensional data, the dynamic skyline query result set can be defined as:  
Figure 3: (a) DBDCS Mapping; (b) inter-sector communication; and (c) intra-sector member node to head node communication  
_Skyline(q, r):_  
\[X=\left\{\begin{array}{l}\forall_{i}\in[1,I],a,b\in I|a(i)\leq b(i)\text{ \ and \ }\\ \exists_{i}\in[1,I],a(i)<b(i)\text{ \ and \ }\\ d\left(q,a\right)\leq r\end{array}\right\} \tag{2}\]  
In Equation (2), \(q\) denotes the query point, \(a(i)\) denotes the value of the \(ith\) attribute of an object and \(r\) defines the range of the region of interest. The data space is divided into \(\mathsf{S}\) sectors with a pivot point, denoted by \(P_{i}\), for each sector \(S_{i}\). The \(i\)_Distance_ key for an object \(x\in\ D\) can be defined as (Figure 4a, Equation (3)):  
\[iDist\left(x\right)=d\left(P_{i},x\right)+i\cdot c \tag{3}\]  
In Equation (3), \(c\) is the separating constant for individual sectors. Given \(q\in\ D\), the region of interest with radius \(r\) can be defined as (Figure 4b, Equation (4)):  
\[r=\left[d\left(P_{i},q\right)+i\cdot c-r,d\left(P_{i},q\right)+i\cdot c+r\right] \tag{4}\]
## 4 Experiments and Results
### Experimental Setup
#### 4.1.2 Data Pre-Processing  
In this part, pre-processing was carried out on the created knowledge-based QA dataset according to the pre-constructed Chinese stop words text that includes \"1\", \"#\", \"3\" and other common Chinese words. The paper uses the jieba.cut() function for word splitting. The stop-words.txt is shown as Table 4.  
Remove stop words: Jieba is a Chinese open-source word splitting tool with high performance, accuracy, scalability, and other features. In addition to word splitting, Jieba also can perform word annotation, keyword extraction, and word frequency statistics. Custom dictionaries: words that appear only once in the dataset are removed and stored in the text. The knowledge text and the sliced training text are used as the lexicon and corpus for subsequent operations.
## 6 Uses of UAVs in Agriculture Advancement
### Irrigation  
The benefit of using UAVs are twofold in irrigation applications. First, equipping drones with various cameras and sensors can support the identification of areas under water stress to quantify irrigation water requirements. Second, they can also be utilized to spray herbicides and pesticides accurately and water on crops, particularly in emergencies, which can save time [154; 155; 156]. Besides, UAVs can also be utilized to assess crop water stress [157]. Because of advanced UAVs, these can be applied as water-saving tools. UAVs use not only facilitate improving watering efficiency but also identify leaks in irrigation or possible water sinks, such as \"JT20L-606\" [158] and \"AGRASMG-1\" [159], which are specially developed UAVs that are currently being utilized to serve this purpose.
###### Abstract  
An efficient scheduling reduces the time required to process the jobs, and energy management decreases the service cost as well as increases the lifetime of a battery. A balanced trade-off between the energy consumed and processing time gives an ideal objective for scheduling jobs in data centers and battery based devices. An online multiprocessor scheduling multiprocessor with bounded speed (MBS) is proposed in this paper. The objective of MBS is to minimize the importance-based flow time plus energy (lbFt+E), wherein the jobs arrive over time and the job's sizes are known only at completion time. Every processor can execute at a different speed, to reduce the energy consumption. MBS is using the tradition power function and bounded speed model. The functioning of MBS is evaluated by utilizing potential function analysis against an offline adversary. For processors m \(\geq\) 2, MBS is O(1)-competitive. The working of a set of jobs is simulated to compare MBS with the best known non-clairvoyant scheduling. The comparative analysis shows that the MBS outperforms other algorithms. The competitiveness of MBS is the least to date.  
multiprocessor system; online non-clairvoyant scheduling; weighted flow time; potential analysis; energy efficiency +
Footnote †: journal: Applied Soft. 2020, 10, 2459