esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 On-Site Monitoring Arrangement
### Horizontal Lateral Displacement of Deep Soil  
Figure 6a shows the monitoring results of horizontal lateral deformation of CX1 at monitoring section I with different positions of the cutterhead. The distance between this inclinometer monitoring point and the jacking axis is 2.3 m. Figure 6b shows the results of  
Figure 5: Longitudinal surface settlement.  
three horizontal soil deformation monitoring points when the jacking distance is 66 m (i.e., \(L_{1}=6\) m)  
It can be seen from Figure 6a that: (1) The horizontal lateral deformation caused by the upper pipe jacking is more obvious, and all the deformation is toward the pipe jacking hole. The maximum deformation is located at the center of the pipe, and the maximum lateral displacement is about 3.3 times that of the lower pipe jacking, and the vertical distribution range is wider than the lower pipe. (2) Before and after the pipe jacking machine reaches an excavation face of 2 m, the soil deformation into the hole accounts for about 50% of the total deformation. The main reasons for soil deformation inward from the jacking hole are the diameter of the pipe being less than that of the jacking machine cutterhead, bentonite mud filling not being compact, and mud seepage loss, etc. Therefore, it is necessary to strictly control the construction gap of pipe jacking and to adopt a reasonable proportion of mud to fill the void in time. As can be seen from Figure 6b, (1) CX1 is closer to the pipe jacking axis, and the horizontal deformation of soil is larger during construction. As the distance away from the axis increases, the effect of construction on the horizontal deformation becomes less and less. (2) The maximum horizontal deformation occurs near the depth of the pipeline, but the depth of the maximum horizontal displacement during the construction of the lower pipe jacking is higher than the depth of the pipeline, because in most of the displaced rock layers of the lower pipe jacking, the disturbance of the rock layer is smaller during the construction.
# Rearing Conditions and Automated Feed Distribution Systems for Zebrafish (_Danio rerio_)
## 2 Standard Rearing Conditions/Parameters  
Wild zebrafish is a freshwater teleost fish with origins in South Asia, where it lives in a wide variety of natural habitats, including irrigation ditches and rice fields, man-made fish ponds, upper reaches of rivers, and even fast flowing hill streams [22; 23; 24; 25; 26]. Mimicking the zebrafish wild environment is a good approach to minimize sub-optimal rearing conditions, which result in increasing energy towards maintaining homeostasis, rather than on growth, gamete production, and immune function, thus leading to a decrease in growth performance, number, quality of offspring, and survival [27].  
On the basis of these considerations, in the laboratory, zebrafish is usually maintained in low moving and slightly acidic to slightly alkaline (pH 6.5-8.0) waters (in natural habitats pH can vary from 6 up to 10) [25; 26; 27] with relatively high clarity (\(\approx\)35 cm) [27], under 14:10 h light:dark cycle [27] and a temperature of 28.5 \({}^{\circ}\)C [28], which is the almost universally cited temperature for normal development. With respect to adults, zebrafish embryos and larvae are generally reared in a solution (E3 medium (also known as egg water)) containing all key ions (5 mmol/L NaCl, 0.17 mmol/L KCl, and 0.33 mmol/L MgSO4) at low salinity levels and methylene blue (0.5 mg/L) to reduce fungal infections [29]. With the development (and size increase), larval, juvenile, and adult zebrafish are gradually reared in tanks housedin designed culture systems (see Section 3. Designed culture systems) under the standard conditions stated above.  
For detailed information about zebrafish rearing procedures, please refer to, among others, the reviews by [PERSON] et al. (2017, 2018), [PERSON] et al. (2019), [PERSON] et al. (2019), and [PERSON] et al. (2020). The most relevant conditions and parameters reported in the cited reviews for zebrafish rearing are summarized in Table 1.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Effect of the Query Distance  
In this simulation, we varied the query distance of the range monitoring queries from 50 to 500 to examine how the query distance affects the performance of the proposed method. As shown in Figure 8a, the query distance does not affect the communication cost of CM because of the same reason mentioned in the first simulation. On the other hand, as the query distance is increased, the communication cost of DM\({}_{\text{(naive SSP-tree)}}\) and DM\({}_{\text{(SSP-tree)}}\) is increased. As the query distance becomes longer, excessive intersections among query segments occur. This increases the number of node splits of the naive SSP-tree and the SSP-tree, and thus accelerates the height growth of the naive SSP-tree and the SSP-tree. As a result, the servers in DM\({}_{\text{(naive SSP-tree)}}\) and DM\({}_{\text{(SSP-tree)}}\) are led to assign smaller vicinity regions to moving objects. Because the moving objects are assigned the vicinity regions of small size, they frequently leave these vicinity regions and send RequestVR messages to the server. However, DM\({}_{\text{(SSP-tree)}}\) performs better and is less sensitive to this parameter than DM\({}_{\text{(naive SSP-tree)}}\) because, again, of the first advantage of maintaining the full list at each node in the  
Figure 7: Effect of the number of queries on the communication cost and the server computation cost. (**a**) Total number of messages vs. the number of queries; (**b**) The amount of CPU-time vs. the number of queries.  
SSP-tree. On average, DM\({}_{\text{SSP-tree}}\) incurs 12.1% and 51.4% of the communication cost, as compared to CM and DM\({}_{\text{(naive SSP-tree)}}\), respectively.  
Figure 8b shows the effect of the query distance on the server computation cost. In contrast to DM\({}_{\text{(naive SSP-tree)}}\) and DM\({}_{\text{(SSP-tree)}}\), the server computation cost of CM is nearly not affected by the query distance because an increase in the query distance does not increases the amount of CPU-time the server takes for checking moving objects if they affect the current results of queries. However, DM\({}_{\text{(SSP-tree)}}\) still performs best in all cases. As compared to CM and DM\({}_{\text{(naive SSP-tree)}}\), on average, DM\({}_{\text{(SSP-tree)}}\) takes 41.7% and 64.9%, respectively, of the amount of CPU-time.
## 1 Introduction
### Applications of UAV-Aided Data Collection for WSNs/IoT  
UAV-assisted WSN/IoT applications are abundant in the literature, and some are time-critical. Such applications can be categorized into monitoring, industrial, environmental, smart city, data gathering, security, health, agriculture, and disaster management. Figure 1 ((a)) [[https://www.flickr.com/photos/mikecogh/22339765443/](https://www.flickr.com/photos/mikecogh/22339765443/)]([https://www.flickr.com/photos/mikecogh/22339765443/](https://www.flickr.com/photos/mikecogh/22339765443/)) accessed on 18 January 2023; (b) Picture by DFID--UK Department for International Development (see [[https://www.flickr.com/photos/dfid/17313224411/](https://www.flickr.com/photos/dfid/17313224411/)]([https://www.flickr.com/photos/dfid/17313224411/](https://www.flickr.com/photos/dfid/17313224411/)) accessed on 18 January 2023); (c) [[https://www.rawpixel.com/image/5912255](https://www.rawpixel.com/image/5912255)]([https://www.rawpixel.com/image/5912255](https://www.rawpixel.com/image/5912255)) accessed on 18 January 2023; (d) Picture by [PERSON], [[https://commons.wikimedia.org/wiki/File:Md4-1000_microdrones_frank.jpg](https://commons.wikimedia.org/wiki/File:Md4-1000_microdrones_frank.jpg)]([https://commons.wikimedia.org/wiki/File:Md4-1000_microdrones_frank.jpg](https://commons.wikimedia.org/wiki/File:Md4-1000_microdrones_frank.jpg)) accessed on 18 January 2023; (e) [[https://commons.wikimedia.org/wiki/File:4X-UHJ_Agridrones_d.jpg](https://commons.wikimedia.org/wiki/File:4X-UHJ_Agridrones_d.jpg)]([https://commons.wikimedia.org/wiki/File:4X-UHJ_Agridrones_d.jpg](https://commons.wikimedia.org/wiki/File:4X-UHJ_Agridrones_d.jpg)) accessed on 18 January 2023) depicts deployments of UAVs in real-world scenarios. UAV-based monitoring applications have huge prospects. For instance, ref. [1] studied the use of IoT for monitoring physical phenomena (e.g., temperature, humidity, etc.) in a geographic area. Similarly, refs. [2; 3] considered an environmental monitoring application, while [4] is applicable in agriculture, health, safety, and industrial data monitoring. The work in [5] is targeted at a post-disaster early warning scenario, while environmental sensing is considered in [6; 7], and the monitoring of time-stamped status updates is studied in [8; 9]. The identification, exposition, and understanding of the technical design aspects and considerations for UAV-assisted IoT architecture targeted at these applications would help researchers and network designers to make informed decisions with respect to alternative architectures, trade-offs, and techniques to achieve target design objectives, thus enhancing the efficiency of data gathering in these applications.
# Contribution of Atmospheric Fallout to the Soil-Root-Leaf Transfer of PAHs in Higher Plants
## 4 Discussion
### Atmosphere-to-Aerial Parts Pathway  
Considering the leaf-stem-root pathway (the above-ground parts were treated with the extract), the highest concentration was found in the leaves (333 \(\mu\)g/kg), a similarly high concentration in the stems (269 \(\mu\)g/kg), and a low concentration in the roots (61.3 \(\mu\)g/kg), showing a diminishing tendency in the leaves-stem-roots compartments.  
Assessing the atmospheric uptake of HMW PAHs (air-leaves-stem-root pathway), a gradual decrease in individual PAHs was experienced, similarly to the concentration of 19 PAHs. These decreases showed bigger differences in the case of HMW PAHs (Figure 3). Statistically significant differences were found between 4-ring PAH concentrations in leaves and roots (t = 4.5244, df = 3, \(p\)-value = 0.02019), as well as in stems and roots (t = 8.175, df = 3, \(p\)-value = 0.003829). When 5-ring PAH concentrations were compared, differences were significant between leaves and roots (t = 3.2911, df = 4, \(p\)-value = 0.03019) as well as between stems and roots (t = 8.242, df = 4, \(p\)-value = 0.001182).  
In the leaves, the 2-ring Nap showed the highest concentration, 234 \(\mu\)g/kg. Of 3-ring PAHs, Phen was the dominant species, and its concentration was 8.6 \(\mu\)g/kg. In the study by [PERSON] et al., it was the most abundant PAH in _Salix matsudana_ leaves, showing good consistency with atmospheric concentrations [67]. Comparable results were given by [PERSON] et al., reporting that Phen was the most abundant PAH in plants and atmospheric wet deposition, assessing the soil-plant systems in northern taiga bioconeogenesis [68].  
In the leaves and stem compartments, 6-ring PAHs were detected. The concentration of IP was 1 \(\mu\)g/kg in the leaves and 0.5 \(\mu\)g/kg in the stems, while the concentration of B(g,h,i),p was 2.1 \(\mu\)g/kg in the leaves and 1.3 \(\mu\)g/kg in the stems. They were not translocated to the roots, however. Similarly, 5-ring PAHs could be detected in the leaf and stem compartments (Figure 3), but except for B(b)f, 5-ring PAHs were not translocated to the roots. The concentration of B(b)f shows a clear diminishing tendency: 8.4 \(\mu\)g/kg in the leaves, 3.2 \(\mu\)g/kg in the stems, and 1.1 \(\mu\)g/kg in the roots. These results also show that bioaccumulation of HMW PAHs cannot be neglected. High PAH concentrations in internal leaf tissues can be attributed to the impaired translocation and relative persistence of these hydrophobic compounds [69; 70].
## 5 MPA-Optimized MVMD and Improved Wavelet Threshold Noise Reduction Method
### Improvement of Wavelet Threshold Noise Reduction  
The third noise reduction denoises the reconstructed PD signal via the improved wavelet threshold algorithm to obtain the final denoised PD signal. The improved wavelet threshold algorithm is shown as follows:  
(1) The selection of wavelet basis: Selecting the appropriate wavelet basis can effectively extract the characteristics of PD signals. According to paper [32], the threshold noise reduction method based on the db7 wavelet has a good noise reduction effect on the field data, and the db7 mother wavelet is very similar to the PD pulse current signal collected by the high-frequency current sensor. In addition, paper [33] gives the maximum NCC value and corresponding SNR of four PD simulation signals under dbN (N = 2, 3,...,12) noise reduction. The results show that db7 has better noise reduction performance for four PD simulation signals. Therefore, the db7 wavelet is selected as the wavelet basis function in this paper.  
(2) Selection of decomposition scale: The noisy PD signal from Figure 1c is used, and the SNR is set to 4 dB. The SNR value of the noise-reduced signal under different  
Figure 6: Optimization results of different optimal algorithms.  
According to Figure 8, the improved threshold function has the best noise reduction effect on the PD signal. The noise-reduced PD signal is most similar to the pure PD signal, with the smallest amplitude error and less residual noise. The PD signal obtained by the soft threshold function and hard threshold function has a large amplitude error and a certain degree of distortion.  
In Table 6, the signal obtained via the improved threshold function has the largest SNR and NCC value and the smallest RMSE value. Obviously, the improved threshold function is superior to the other two threshold functions.
## 2 Affecting Factors
### Other Factors  
Several studies have concentrated on the influence of the configuration and calibration of field spectroradiometers on PRI variation. Most spectroradiometers are designed for periodic use, but continuous measurements and the validation of remote-sensing data are needed to improve PRI accuracy and application in assessing carbon uptake [137; 138]. Regular calibration and the correction of data based on the effect of nonlinearity were therefore recommended to ensure data quality [85; 137; 139]. Radiometric and spectral stability have high impacts on the uncertainty of PRI. The accurate measurement of near-surface spectral data can provide an important groundwork for the calibration and validation of satellite measurements [85].
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 3 Position Control Strategy of PAM
### Experimental Platform  
The experimental object is the McKibben-type PAM made in the laboratory. To illuminate the hysteresis relationship between displacement and pressure, an experimental device, as shown in Figure 4, was constructed. The cylinder (SC standard cylinder 32 \(\times\) 250) acts as a load to reduce accidental errors caused by hanging heavy objects or weights. During the experiment, the pneumatic muscles are placed horizontally, with one end fixed by a force sensor (MEACON MIK-LCSI) to the experimental platform and the other end linked with a cylinder by installing one laser displacement sensor (Panasonic HG-C1100) baffle. The internal pressure of the pneumatic muscles and cylinder pressure are controlled by a proportional valve (NITV 2050-312L). As shown in Figure 5, the computer host communicates with dSPACE and sends an analog signal to the proportion valve. Then, such a valve converts voltage signals into pressure signals. In addition, this charges and deflates the cylinders and PAMs that rely on the air pressure provided by the air compressor at the air source. In addition to the tension-compression sensor, the laser displacement sensor converts the tension and displacement changes of the PAM into voltage signals and outputs them to dSPACE to return to the computer host. Next, the output signals mentioned are changed into tension and displacement by the Simulink program. The main data recorded are the input voltage of the pneumatic muscle, the cylinder input voltage, the tension-compression sensor output force, and the laser displacement sensor output displacement. This paper mainly studies the hysteresis pertinence between pressure and displacement, with the readings of the force sensor not used.  
Figure 4: Experimental platform.  
Figure 5: Experimental schematic diagram.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 3 Methods
### Dataset and Data Cleaning
#### 3.1.2 Data Cleaning  
In our study, data cleaning was an essential preprocessing step, conducted using Python 3.8's regular expressions and the Natural Language Toolkit (NLTK) library 3.8.1. Raw social media data often contain various irrelevant elements, such as stop words, short tokens, URLs, excessive whitespace, HTML markup, punctuation, and other non-alphanumeric characters, which can hinder the model's ability to learn meaningful patterns. To address this, the text was tokenized into individual words and subjected to several preprocessing steps: converting all text to lowercase, lemmatizing words to their base forms, and removing all irrelevant elements. This process ensures that the model focuses on the most informative components of the text.  
The overall quality of the dataset plays a crucial role in determining the performance of any downstream model. Poor-quality data, such as mislabeled posts, irrelevant content, or noise, can negatively impact the model's learning process, leading to reduced accuracy and reliability in real-world applications. Therefore, thorough data cleaning and preprocessing are fundamental for enhancing model robustness and ensuring more accurate classification outcomes in disaster response scenarios.
## 5 Conclusions  
This research incorporates the EMFF system into the port-Hamiltonian framework, facilitating the analysis of satellite EMFF relative motion dynamics. By adopting \(\mathbf{q}_{a}\) as generalized coordinates within the Lagrangian framework, we successfully formulate the equations governing the non-linear relative motion dynamics under the port-Hamiltonian structure, along with the system's Hamiltonian function. For satellites under disturbance, a disturbance-reaction passivity-based controller is designed, combining a feedback control law based on tIDA-PBC with an error observer. This controller achieves trajectory tracking by adjusting the strength of the satellite's magnetic dipole, thereby modifying the electromagnetic forces exerted on it. Numerical simulations validate the effectiveness of the designed controller on a predefined circular formation reference trajectory. The results demonstrate that this method effectively controls the relative motion of EMFF spacecraft, exhibiting superiority in both convergence speed and control accuracy. Given the applicability of EMFF in the port-Hamiltonian framework and the potential for future research, this paper holds considerable practical significance.  
Conceptualization, J.W. and Q.Z.; methodology, J.W.; software, J.W.; validation, J.W., Q.Z. and W.Z.; formal analysis, J.W., Q.Z., J.S. and W.Z.; investigation, J.W. and Q.Z.; resources, Q.Z.; data curation, Q.Z.; writing--original draft preparation, J.W.; writing--review and editing, Q.Z. and J.S.; visualization, Q.Z.; supervision, Q.Z. and W.Z.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key R&D Program of China (Grant No. 2018 YFA0703800), State Key Program of National Natural Science Foundation of China (Grant No. U23B2039 & U21B2008) and Civil Aerospace Research Project of China (Grant No. D030312).  
Not applicable.  
**Data Availability Statement:** All necessary and relevant data are included in this paper.  
**Acknowledgments:** The authors thank the editor and reviewers for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 6: Monte Carlo simulation.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Vehicle Monitoring by BackCom  
National Aeronautics and Space Administration (NASA) aims to use passive WSN for a vehicle health monitoring system (VHMS) that can ensure the safety of crew and vehicles. Figure 47 illustrates the concept of the VHMS. BackCom has many applications in aerospace vehicles to obtain benefits from ubiquitous passive sensor nodes. [PERSON] et al. highlighted the need for a passive sensor node communication network in aerospace vehicles [PERSON] et al. (2011). In local traffic management, smart signboards use backscatter tags to communicate with FM radio receivers in cars [PERSON] et al. (2011). Figure 48 shows the concept of vehicle safety using BackCom. This type of communication can be used to reduce the number of accidents.
## 4 Calibration of the Camera Array
### Self-Calibrating Baselines
\[o_{Ck}\in O_{k}\text{ such that }\forall_{k\in\{1,2,3,4\}}Q_{k}(o_{0})=o_{Ck} \tag{10}\]  
In this paper, only these keypoints \(o_{Ck}\) are considered in calculations of array rectification transformations \(M_{k}\). Thus, in every considered stereo camera, the set of the same points from a raw central image \(I_{R0}\) is a base for analyzing differences in disparities provided by different cameras. The subsequent step in calculating transformation \(M_{k}\) with the use of the SURF algorithm is calculating disparities \(d_{ok}\) between keypoints identified in images \(I_{Ck}\) and matching keypoints in images \(I_{Sk}\). Those keypoints for which \(d_{ok}\) is not within the range of disparities used for obtaining disparity maps by a stereo matching algorithm are excluded from further calculations. Excluding these values adjusts self-calibration to parameters of stereo matching algorithms. Thus, conditions \(d_{min}\leq d_{ok}\leq d_{max}\) are fulfilled. Then, an average value \(d_{ok,avg}\) of the the remaining disparities is calculated for each image pair \(I_{Ck}\) and \(I_{Sk}\). The average value of disparities between matching keypoints indicates which pair of images should be rectified by \(M_{k}\) so that disparities become larger and which pairs should provide lower values of disparities. Disparities for different image pairs are being adjusted to the pair \(I_{C1}\) and \(I_{S1}\) obtained from the central image and the right one taken by EBCA, so \(m_{1}=0\). Therefore, the extent of shifts \(m_{2}\), \(m_{3}\) and \(m_{4}\) that transformations \(M_{k}\) should implement are given by Equation (11). \[ \begin{split} m_{2}&=d_{01,avg}-d_{02,avg}\\ m_{3}&=d_{01,avg}-d_{03,avg}\\ m_{4}&=d_{01,avg}-d_{04,avg}\\ \end{split}  \tag{11}\]  
Thus, transformations \(M_{k}\) are equal to the matrix presented in Equation (12). \[M_{k}= \begin{bmatrix}1&0&-m_{k}\\ 0&1&0\\ 0&0&1\end{bmatrix}  \tag{12}\]  
Equation (4) used for calibrating cameras in EBCA takes the following form:  
\[sp=M_{k}W_{k}A[R|t]P_{w} \tag{13}\]  
Section 6.1 shows results of experiments with using transformations \(M_{k}\) to improve the quality of disparity maps obtained from EBCA.