esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
In particular, the use of unmanned aerial vehicles (UAVs) for RS has become an important research direction [45; 46]. [PERSON] et al. [47] assessed the accuracy and application of UAV photogrammetry in measuring the geometry and volume of sanitary landfills, comparing it with traditional measurement methods to demonstrate the effectiveness of UAVs in routine landfill monitoring. Emerging research highlights the integration of 3S techniques and the application of new techniques in waste monitoring [48; 49; 50; 51; 52]. [PERSON] et al. [53] proposed a ranking algorithm using RS and GIS to identify and categorize suitable, moderately suitable, or unsuitable landfill sites in India. [PERSON] et al. [54] addressed the lack of methods for selecting appropriate landfills or recycling plants for construction and demolition waste in urban areas by combining compensatory and non-compensatory multi-criteria analysis with GIS. [PERSON] et al. [55] implemented a wireless personal area network and cloud-assisted architecture using the Internet of Things and Xbee communication protocol for real-time monitoring of solid waste in remote areas. [PERSON] et al. [56] emphasized the development of an optical fiber sensing method for large deformation measurement in construction solid waste landfills, improving risk assessment and mitigation capabilities through accurate field monitoring and effective early warning systems. [PERSON] et al. [57] proposed a method to optimize waste collection processes by leveraging digital technologies such as GIS, the Internet of Things, and data analytics, significantly reducing travel distance and time in waste collection and transportation. In summary, the potential of 3S techniques is increasingly recognized in monitoring olive oil mill SDAs, municipal open SDAs, mine SDAs, and hazardous SDAs. However, despite the critical importance of SDAs in HSRs, existing research has largely overlooked their monitoring. Additionally, more research emphasizes the application of a single technique rather than the integration of techniques. Moreover, existing research mainly focuses on the monitoring of sedimentation, temperature, and concentration. Existing research is not applicable to the monitoring focus of disturbance boundaries, soil and water conservation measures, and spoil volume changes in SDAs. Therefore, it is necessary to integrate 3S techniques to carry out comprehensive monitoring of SDAs during the construction stage of HSRs in order to support the recycling and reuse of construction spoil and contribute to the development of a circular economy in HSRs.
## 4 Grouting Design and Scheme  
### Grouting Penetration Distance  
The basic premise of an economical and effective grouting scheme is the reasonable value range of grout penetration distance. Although scholars proposed many theoretical equations based on the different considering of influence factors such as filtration effect and tortuous length etc., the columnar penetrate equation is recognized by most researchers, and its expression is  
\[R=\sqrt{\frac{2 kpt}{n\beta\ln\frac{R}{r_{0}}}} \tag{4}\]  
where \(R\) is the penetrate radius (centimeter), \(r_{0}\) is the radius of grouting hole (centimeter), \(p\) is the pumping pressure (centimeter head), \(k\) is the permeability (cm/s), \(t\) is the grouting time (s), \(n\) is the porosity, and \(\beta\) is the ratio of slurry viscosity to water viscosity. In this project, \(r_{0}\) can be taken as 5.4 cm, other parameters are: \(k\) = 4.8 \(\times\) 10\({}^{-4}\) cm/s, \(n\) = 0.10, \(\beta\) = 2.50, grouting time \(t\) are 900 s. The pumping pressures are assumed as 2.0 \(\times\) 10\({}^{4}\) cm head and 3.0 \(\times\) 10\({}^{4}\) cm head, the penetrate distances are 1.61 m and 1.91 m, respectively. The field grouting tests are conducted to obtain a reasonable grouting penetration distance. The results shows that when the concentration of suspension slurry is 0.5, grouting time is 900 s and the grouting pressure is 2.0 MPa. For the 50 mm and 108 mm grouting holes, the longest grouting penetration distances are 1.85 m and 2.15 m, respectively. By comparing the penetration distance results of different methods, 1.5-2.1 m is finally considered as the design of grouting hole arrangement in order to make the grouting fully diffuse in the stratum and bite each other. Assuming that the soil skeleton cannot be compressed, the void space in the grouted region is filled with slurry, the total slurry volume can be expressed as  
\[Q=\pi R^{2}\cdot n \tag{5}\]  
where \(R\) is the radius of grouted region. The grouted region is assumed as a columnar and the grouting volumes are 1.02 m\({}^{3}\)/m and 1.26 m\({}^{3}\)/m. In the actual construction process, the grouted region shows different properties due to the different position of injected media, the grouting time and pumping pressure should be adjusted according to the actual engineering condition.
## 2 Model Establishment
### Measurement Model  
In practical applications, the measurement information of a maneuvering target is obtained by sensors such as radar, which is mostly based on spherical coordinates, including distance \(r\), azimuth \(b\), and elevation \(e\)[48].  
\[\left\{\begin{array}{l}h_{r}=r+v_{r}\\ h_{b}=b+v_{b}\\ h_{e}=e+v_{e}\end{array}\right., \tag{6}\]  
where \(v_{r},\,v_{b},\) and \(v_{e}\) are measurement noises of distance, azimuth and elevation, respectively; \(h_{r},\,h_{b}\) and \(h_{e}\) are noisy measurement information.  
Considering the strong nonlinearity in the spherical coordinate system, in order to facilitate partitioned sampling, the Cartesian coordinate system is selected as the reference system to process the measurement information, and the coordinate conversion for data measured by sensors is required. In this paper, we assume that the measurement model in the Cartesian coordinate system is as follows,  
\[Z_{k}=H_{k}X_{k}+v_{k},\]where \(Z_{k}=\left[x_{k},\ y_{k},\ z_{k}\right]^{\mathrm{T}}\) is the measurement vector at time \(k\), \(H_{k}\) is the measurement matrix, \(v_{k}\) is the measurement noise.  
Assuming that there is a coordinate transformation between the two coordinate systems \(\varphi=h^{-1}\), we can get measurements in the transformed Cartesian coordinate system.  
\[Z=\left[\begin{array}{c}x\\ y\\ z\end{array}\right]=\varphi\!\left(Z_{p}\right)=\left[\begin{array}{c}r\cos b \cos e\\ r\sin b\cos e\\ r\sin e\end{array}\right]. \tag{8}\]  
The actual measurements in the Cartesian coordinate system after conversion can be expressed as follows:  
\[\left\{\begin{array}{c}h_{x}=x+v_{x}\\ h_{y}=y+v_{y}\\ h_{z}=z+v_{z}\end{array}\right., \tag{9}\]  
where \(v_{x}\), \(v_{y}\), and \(v_{z}\) are the measurement noises after conversion with the variance matrix \(R=\mathrm{J}(Z_{p})\times R_{p}\times\mathrm{J}(Z_{p})\), \(R_{p}\) is the original noises variance matrix and \(\mathrm{J}(Z_{p})\) is the Jacobian determinant of measurements. After conversion process, the noise variances of various parties are coupled to each other, so the decoupling process needs to be performed later. The detail of variance decoupling of measurement noises is shown in [3].
# Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
###### Abstract  
Large-sized step shafts are important devices for supporting and transferring heavy parts, and online inspection equipment for runout errors is affected by the environment and is subject to coaxiality errors and center-position errors, leading to problems such as reduced measurement accuracy in imaging systems. In view of the above problems, this paper proposes an improved optical imaging system design for runout error detection based on the plane-mirror-group correction method. Zemax was used to optimize the structure and simulate the optical path of the optical imaging system. The total length of the structure was 50 mm, and the MTF function for each field of view was greater than 0.3 at the spatial level up to a frequency of 42 lp/mm. The system was applied to a test platform for runout error detection, achieving the detection of runout errors of a large size in the radial direction and at the end face with a diameter range of 500-700 mm. The measurement repeatability was less than 30 \(\upmu\)m, and the system corrected the coaxiality error of the stepped-shaft online inspection equipment considered in this paper.  
runout error detection; direct laser triangulation; inclination error correction; optical imaging system +
Footnote †: journal: Applied  
Article
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### The Iterative Positioning Algorithm
#### 3.3.2 The Iteration Termination Criteria for the Algorithm  
If the termination condition of the iterative positioning algorithm is set correctly, the higher positioning accuracy can be obtained within a small number of iterations and the algorithm is prevented from falling into an infinite loop. That is, the iteration termination condition is defined as  
\[P_{\hat{O}_{n}}-P_{\hat{O}_{n-1}}<\varepsilon, \tag{15}\]  
where \(P_{\hat{O}_{n}}\) is the RSSI value between the centroid of the \(n\)th iteration and the unknown target node, and \(\varepsilon\) is the set threshold.  
Under different environmental conditions, as Equation (15) is used as the iterative termination condition, its computational complexity is very high, even higher than the complexity of the iterative positioning algorithm itself. In this way, the hardware complexity of the system increases significantly and the algorithm becomes almost infeasible.  
The purpose of iterative positioning is to make the positioning error converge to the expected value, as the convergence property and convergence velocity are essential issues of the iteration. In the process of iteration, with the increased number of iterations, the convergence processes in which the error converges to the expected value usually have the following cases: fast convergence, slow convergence, and periodic oscillation. In order to further study the convergence property of the above three cases, a series of simulations are conducted to analyze the relationship between the positioning error and the number of iterations in the paper. Through simulation analysis, it can be noted that the iterative error varies with the above different regular patterns when the number of iterations is between 0-10 since the iteration error no longer varies when the number of iterations is between 10-20. A flow chart of the iterative positioning algorithm is shown in Figure 3.
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 3 Proposed Algorithm for Content Protection in JPEG Images
### Generating the Visually Transformed JPEG Images
#### 3.1.5 Hiding Location Information in the Transformed Image  
In this phase, histogram modification is applied to AC coefficients with value 1 or \(-1\) for reversible data embedding (e.g., [15]). The other AC coefficients are modified by histogram shifting. The operation is to embed a bit value \(i\) into an original AC coefficient \(a_{j}\) by \[a_{j}^{\prime}=\left\{\begin{array}{cc}0,&a_{j}=0\\ -1,&a_{j}=-1,i=0\\ -2,&a_{j}=-1,i=1\\ 1,&a_{j}=1,i=0\\ 2,&a_{j}=1,i=1\\ a_{j}+1,&a_{j}>1\\ a_{j}-1,&a_{j}<-1\end{array}\right., \tag{8}\]  
where \(a_{j}^{{}^{\prime}}\) is the modified AC coefficient.
# Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of _Cannabis_ Species
## 3 Results and Discussion
### Antifungal Activity of Cannabis  
The continuous emerging empirical evidence regarding the antifungal properties of _Cannabis_ and its secondary metabolites has proven to present excellent results. A few available research studies suggest that, for a long time, _Cannabis_ has been regarded as possessing antifungal activity against a diverse range of pathogenic fungi [61]. Despite cannabinoids such as \(\Delta^{9}\)-THC and CBD being known to greatly contribute to antifungal activity, some studies have demonstrated that plant extracts or essential oils also present this activity [9,111]. So far, studies about the antifungal activity of _Cannabis_ extracts and their secondary metabolites are extremely limited as compared with research studies on antibacterial activities [112]. [PERSON] et al. [113] showed that the n-hexane fraction of _C. sativa_ effectively inhibits the growth and development of a life-threatening human pathogen _Cryptococcus neoformans_ which is responsible for lung infections in humans. The results revealed modest antifungal activity with an IC50 value of 33.15 \(\mathrm{\SIUnitSymbolMicro g/mL}\) against
## 4 Discussion
### Wind-Driven Rain (WDR) and Other Weather Data  
This paper showed an application of a simplified method of WDR calculation using hourly weather record data of two nearest weather stations, both starting from rain deposition on buildings for one year and both with a long period of 20 year. This method seems very useful in absence of weather stations directly established in a site.  
In our case, the South-East side resulted the least affected by WDR, with 157.99 kg/m\({}^{2}\cdot\)a, and the highest values are in North facing ones. The role of WDR, which significantly favors the biological colonization of vertical surfaces, as previously shown for several cases [1], reveals significant differences in the amount of precipitation received by the different sides of the structure. In detail, the North-West side is the most affected by WDR, receiving 315.60 kg/m\({}^{2}\cdot\)a, particularly during Winter, with a maximum of 150 h of rain and several single events of higher WDR amount. However, in parallel, such exposure is also ventilated (see Figure 12) and this can favor a certain vapor evaporation. The North-East side receives a lower quantity (231.47 kg/m\({}^{2}\cdot\)a of WDR), and overall, a significant concentration in March, accounting for 40% of the total annual WDR and a lower number of hours of WDR (100 h) compared with the other sides but a higher amount of WDR in single events, i.e., more intense WDR events. Such factors can explain the diffuse exfoliation in this exposure that might reduce the growth of lichens [16].  
The meteorological data regarding the predominant wind direction over an extended period are comparable with previous research conducted in a similar context at Uzumlu Church (September 2014-May 2015), recorded by a set of environmental monitoring stations _in situ_(September 2014). However, there are notable differences in the daily wind patterns between Uzumlu Church and Saint Simeon Church. At the considered weather stations, the data indicate that the prevailing wind direction is generally from the North. In contrast, at Uzumlu Church, the wind direction was reported to be from the North during the nighttime and from the South during the daytime. The difference in daily wind patterns between these two sites can likely be attributed to the setting where Uzumlu Church is located. Specifically, the surface temperature changing on the slope behind (North of) the church plays a crucial role in influencing wind patterns. During the day, the solar heating of the slope creates a thermal updraft (September 2014). This updraft near the back slope can generate a southward wind flow, affecting the wind direction observed at the church site. Due to the microclimatic data not being acquired _in situ_, we cannot definitively exclude the possibility of partial similar behavior in wind patterns at St. Simeon Church.
## 4 Results and Discussion
A chaos detection method was developed for AD by employing the FOAM for the GT's NFTV; a chaos detection method was developed for AD's NFTV. For the graphs illustrated in Figures 13-15, the excitation frequency has a logarithmic balayage for the GB's NFTV in transition through APPR \(\eta_{3}\simeq 5180\) Hz, considering the worst case for the GB \(\beta_{2}=47^{\circ}\), \(\mu_{3}=0.0065\), \(\overline{\Omega}_{3}=16273.5\) rad/s, \(\chi_{\rm GBn}=0.0492\), \(\Gamma_{3}=-9.9638\times 10^{4}\) s\({}^{-2}\)rad\({}^{-2}\), \(\Gamma_{5}=-1.9928\times 10^{5}\) s\({}^{-2}\)rad\({}^{-4}\), n = 0.00001 [octaves/min], and the damping ratio \(\xi=0.0016\ldots 0.006\). MATLAB software was developed based on DSOE (22) for nonstationary NFTV of the GB to calculate \(\Phi_{3}(\eta_{3})\), \(\frac{\mathrm{d}\Phi_{3}}{\mathrm{d}t}(\eta_{3})\). For the graphs illustrated in Figures 13-15, the excitation frequency has a logarithmic balayage for NFTV of the GB in transition through APRR. In contrast, the characteristics of internal and external excitations \(\mu_{3},\chi_{\text{GBn}},\Gamma_{3},\Gamma_{5}\) are constants. In Figures 13 and 14, it is remarked that the nonstationary SA and SVA \(\Phi_{3}(\eta_{3}),\frac{4\Phi_{3}}{\text{d}t}(\eta_{3})\) for NFTV of GB manifest the \"beatings\" and a rapid decrease of the extreme values with the damping's increases in the range \((1.6\dots\dots 6.0)\times 10^{-3}\). The greatest value of the nonstationary SA and SVA for NFTV of the GB is reached when \(\eta_{3}=5180\) Hz. It is remarked that the \"pictures\" in Figure 15 are like those specific to a strange attractor. In this way, the aim of using FOAM for NFTV of the GB was reached, FOAM being thus a detection method for chaos manifestation of NFTV for the elements of AD. Comparing Figures 10-12 with Figures 13-15 shows the same trend for the global tuip and the global bowl regarding the NFTV of AD's elements in transition through APRR. A possible chaotic dynamic behavior for these elements was detected using the FOAM to investigate NFTV for AD's elements.
## 3. Freestanding Position-Based Routing Protocol
### Location Service
#### 3.4.1 Destination Discovery  
Three classes of nodes are considered in the destination discovery procedure: source nodes, relay nodes, and destination nodes. The source nodes are vehicles with data to send. The relay nodes are vehicles located along the dissemination path. This nodes relay destination discovery packets. The destination nodes are the vehicles towards which the data packets are sent.  
If the geographic position of a destination node is unknown and a source node wants to send data packets to that vehicle, then a location request packet (LREQ) is broadcasted. This packet is used to find the geographic position of the destination node. Before broadcasting the LREQ, a transmitter (_i.e._, the source node or a relay node) selects a set of vehicles as relay nodes through FPBR's selection algorithm (see Section 3.3.2). If the vehicle is the source node, then the set of relay nodes in the LREQ packet must include at least one vehicle for each direction. The same applies for relay nodes located at intersections. On the other hand, relay nodes not located at intersections select vehicles located towards the dissemination direction before broadcasting the LREQ packet.  
When the LREQ packet arrives at the destination node, it waits for twice the propagation time, \(\tau\), before sending a location reply packet (LREP). This waiting time is introduced in order to avoid possible packet collisions with LREQ packets that could arrive from different paths. Thus, after \(2\tau\) s the destination node generates the LREP containing its geographic position and speed. The LREP packet is disseminated towards the source node position using the same algorithm as the LREQ. When the LREP packet reaches the source, the data packet is forwarded towards the geographic position of the destination node by means of the MFWAR mechanism. In order to avoid collisions between LS  
Figure 1: Use of the dynamic factors \(\beta_{s}\). and \(\beta_{c}\) to adjust the nominal radio range, \(R_{n}\), for vehicles belonging to the \(V_{s}\) and \(V_{c}\) sets. (**a**) Nominal radio coverage \(R_{n}\); (**b**) Adjusted \(\beta_{s}*R_{n}\) radio range for vehicles traveling in opposite direction and moving away (\(V_{s}\) set). (**c**) Adjusted \(\beta_{c}*R_{n}\) radio range for vehicles traveling in the same direction (\(V_{c}\) set).  
packets and hello beacons, every node that hears a LS packet delays its beacon period by \(\tau\). Vehicles along the discovery messages (LREP and LREQ) path are aware of these packets since they are broadcast packets.
# Impacts of Material Orthotropy on Mechanical Behaviors of Asphal Pavements
## 4 Analysis Results  
### Impact of Anisotropy on Surface Deflection  
Surface deflection has been deemed to be an indicator of the pavement capacity performance, and an increased surface deflection corresponds to a lower capacity [5]. The distributions of surface deflection in the transverse direction under vertical and longitudinal loads are plotted in Figure 8.  
The results of the ANOVA of pavement surface deflection are shown in Table 5. As noted, the significance \(p\)-value was less than 0.05, and the value of F was 12.401, which is larger than the critical value of 1.999, indicating that the material anisotropy had a significant effect on the road surface deflection.  
As noted, the surface deflection increased first and then decreased in the transverse direction. The peak deflection occurred at \(x\) = 0.15 m, which corresponds to the center of the wheel loads. In Figure 8a, the surface deflection was the largest when considering the all-layer orthotropy, showing a peak value of 640 \(\upmu\)m and a changing rate of 21.2%. Figure 8b,c implies that the surface deflection was slightly affected by the anisotropy in the HMA and base layers. Figure 8d demonstrates that the surface deflection for orthotropy in the subgrade was the largest, along with a value and changing rate of 605 \(\upmu\)m and 9.0%, respectively, while those for transverse isotropy changed slightly.  
Figure 7: Change rate of the surface defection between the field data and the FE model: (**a**) Structure A. (**b**) Structure B.  
We found that the material anisotropy increased the surface deflection to an extent, which was in good agreement with the results of [PERSON] et al. (2019). Moreover, it should be noted that the subgrade orthotropy made the most significant contribution to the surface deflection under all of the investigated scenarios.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Network Structure-Based Routing
#### 4.1.1 Flat Routing
The UAV collects data automatically, following an optimal route that covers all the sensors in the network. A vehicle routing problem (VRP) is formulated and subsequently solved to determine the optimal route. The routes of the UAV were pre-planned in Reference [38]. For this reason, the sensor nodes in the network could turn on the radio exactly when the UAV was close by. This phenomenon was responsible for the high energy efficiency in the network. The avoidance of multi-hop routing facilitated the energy efficiency as well.