esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 4 Analysis Model
### System Model
: \(E[N_{T=t}^{k}]\) _is the expected number of devices having a trial number \(t\) that are located in zone \(k\) for \(t\in[1,N_{max}]\) and \(k\in[1,N_{max}]\). Then, \(\sum_{k}\sum_{t}E[N_{T=t}^{k}]=N\) holds._  
If \(E[N_{T=t}^{k}]\) for \(\forall t,k\) are given, we can define the distribution of the ramping number. **Definition 4**. : \(\mathbb{P}(R_{u}=r,Z_{u}=k\ |\ N)\) _is the probability that a device has a ramping number \(r\) and is located in zone \(k\); \(E[N_{R=r}^{k}]\) is the expected number of devices that have a trial number \(r\) and are located in zone \(k\) when \(N\) devices participate in the RA for \(r\in[1,N_{max}]\) and \(k\in[1,N_{max}]\)._  
Figure 1: Definition of ‘zone’. \(E[N_{R=r}^{k}]\) can be derived from \(E[N_{T=t}^{k}]\). Because the devices belonging to _zone_\(k\) can only increase their ramping numbers to \(k\), \(N_{R=r}^{k}=0\) for \(r>k\). Hence, \(E[N_{R=r}^{k}]\) can be defined using \(E[N_{T=t}^{k}]\) as follows:  
\[E[N_{R=r}^{k}]=\left\{ \begin{array}{ll}E[N_{T=r}^{k}],&1\leq r<k,\\ \sum_{t=k}^{N_{max}}E[N_{T=t}^{k}],&r=k,\\ 0,&k<r\leq N_{max}.\end{array} \right.,\quad r\in[1,N_{max}], \tag{9}\]  
Finally, the following two equations are derived. \[ \begin{split}\mathbb{P}(R_{u},Z_{u}=k|N)&=\frac{E[N_{R=r}^{k}] }{N}\\ \mathbb{P}(R_{u}|N)&=\sum_{k=1}^{N_{max}}\mathbb{P}(R_{u},Z_{u} =k|N)\\ &=\frac{E[n_{R=r}]}{N},\quad r\in[1,N_{max}],\end{split}  \tag{10}\]  
where \(\mathbb{P}(R_{u}|N)\) is the distribution of the ramping number when the number of accesses is \(N\).
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 3 QoS Guarantee within Internet of Things Environment
### QBAIoT: QoS Based Access for IoT
#### 3.4.1 IoT Gateway QoS Based Access Method  
The IoT LL-Gw of our architecture adopts the proposed QBAIoT mechanism as a QoS based access method enabling QoS guarantee for IoT objects according to an achieved iSLA. We specify in Figure 8 the finite state machine that illustrates the QBAIoT process within the LL-Gw.  
In state S0, the LL-Gw tests if there is a new configuration to be sent to the objects of its cluster. This new configuration is specified by the HL-Gw thanks to a QBAIoT self-configuring capability (cf. Section 3.5). Thus, if a new configuration exists, the LL-Gw updates the former beacon and sends it to the IoT nodes while reaching State S1. If there is no new configuration the beacon is sent directly to the IoT nodes at the beacon interval time and the LL-Gw switches to state S1. In State S1, the LL-Gw receives data from the different nodes according to their QoS CAPs. The LL-Gw counts the number of received packets during each QoS CAP enabling a self-optimizing capability that is out of the scope of this paper. At the end of each superframe, the number of received packet during the different QoS CAPs is communicated to the HL-Gw and the LL-Gw switches to state S0.  
The QBAIoT process within the LL-Gw is illustrated in Figure 9. In this context, the LL-Gw specifies the beacon configuration according to the received values. Then, the beacon is sent to the IoT nodes if there is at least one existing traffic in the IoT environment. The existence of traffic in IoT environment can be deducted from the existence of gSLAs on the HL-Gw for the corresponding LL-Gw. If there is no traffic in the IoT environment, there is no need to send the beacons to the nodes. After sending the beacon, the LL-Gw should receive data from different nodes depending on the corresponding QoS CAPs of each node. In case of reception of new configurations resulting from added, removed or modified iSLAs, the LL-Gw updates its configuration values and sends a new beacon to the IoT nodes.  
Figure 8: Finite state machine of a gateway QBAIoT process.
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
## 4 Experimental Results  
This section presents the experimental results to validate the proposed control algorithms for the aerial manipulator robot. Four experiments are described on a real aerial manipulator robot with three degrees of freedom: (i) Experiment 1: Kinematic controller implementation, (ii) Experiment 2: Inverse Dynamics controller implementation, (iii) Experiment 3: SMC controller implementation, and (iv) Experiment 4: LAMDA controller implementation.  
The tests consisted of the tracking of a lemiscate trajectory by the aerial manipulator robot, whose desired trajectory parameters were: \(\xi_{d}=\left[\xi_{xd}\ \xi_{yd}\ \xi_{zd}\right]\), \(\xi_{xd}=2.5\cos(0.02t)\) m, \(\xi_{yd}=2.5\sin(0.04t)\) m, \(\xi_{zd}=(\sin(0.1t)+0.6)\) m. The time derivative of the desired trajectory was: \(\dot{\xi}_{d}=\left[\dot{\xi}_{xd}\ \dot{\xi}_{yd}\ \dot{\xi}_{zd}\right]\), \(\dot{\xi}_{xd}=(-2.5*0.2\sin(0.02t))\) m/s, \(\dot{\xi}_{yd}=(2.5*0.04 cos(0.04t))\) m/s, \(\xi_{\
egzd}=(0.1 sin(0.1t)+12)\) m/s.  
The parameters of the aerial manipulator robot were length of joints 1, 2, and 3 defined by \(l_{1}=0.4\) m, \(l_{2}=0.26\) m, \(l_{3}=0.12\) m, and \(r=0.12\) m, which is the height of the hexacopter. \(q(t)=\left[\ u_{l}\ u_{m}\ u_{n}\ \omega\ \dot{q}_{1}\ \dot{q}_{2}\ \dot{q}_{3}\right]=\left[\ 0\ 0\ 0\ 0\ 0\ 0\ 0\ 0\ \right]\); \(\left[q_{1_{0}}\ q_{2_{0}}\ \dot{q}_{3_{0}}\right]=\left[0\ 0\ 0\ 0\right]^{\circ}\). The initial conditions of position of the end-effector were \(\xi=\left[-1.30-0.05\ 1.70\right]\) m and the initial conditions of position of the desired trajectory were \(\xi_{d0}\) [\(2.5\ 2\ 12\)] m.  
The following parameters were available in the kinematic controller: \(W=I_{3x3}\) identity matrix, \(K=I_{3x3}\) identity matrix, whereas the parameters defined for the Inverse Dynamics controller were: \(K_{D}=I_{7x7}\) identity matrix, \(Q_{D}=2I_{7x7}\) identity matrix. The SMC controller parameters were as follows: \(\lambda_{p}=1;\lambda_{d}=1;\lambda_{i}=1;\,k_{2}=1;\,\delta=0.6\). Finally, the parameters of the LAMDA controller were stated as: \(k_{c}=0.1I_{7x7}\), \(k_{d}=B_{7x7}\), a diagonal matrix whose diagonal elements are \(\left[10\ 10\ 40\ 20\ 20\ 20\ 20\right]\), \(\beta_{d}=0.5\), \(\beta_{vc}=0.1\), and \(\beta_{A}=20\).  
For the implementation of the four control algorithms, the experimental tests were carried out on the hexacopter-type aerial manipulator robot incorporating a three-degree-of-freedom robotic arm shown in Figure 6.  
The experiments were developed in the city of Ambato, province of Tungurahua, Ecuador, whose latitude and longitude coordinates are \(1^{\circ}16^{\prime}36.4^{\prime}\) S \(78^{\circ}35^{\prime}19.0^{\prime}\) W. The experiment started at 14:32 on 20 February 2021. Wind velocity at that time and location was 8.3 km/h, approximately, according to [29] as evidenced in Figure 7.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 5 Conclusions  
With the increasing demand for the use of biostimulants in agricultural crops in search of increasingly sustainable and less environmentally harmful agriculture, based on techniques in geospatial data processing, we propose a methodology to show the gain in maize yield after applying biostimulants. Three conclusions can be made.  
In conditions where it is not possible to evidence statistical differences between the yield of the control areas, based on the research methodology, it is possible to spatialize the yield gain following the application of _A. brasilense_. In conditions of mechanized harvesting with a high amount of georeferenced yield information, given the high accuracy and precision, we recommend using Kriging to generate the yield gain surfaces. Finally, under a few sample conditions, yield prediction models based on high spatial resolution images can be used to simulate yield gain surfaces with satisfactory precision and accuracy.  
To achieve greater precision and accuracy, future studies should consider two aspects. The first is the possibility of generating the surfaces of yield gain from Kriging techniques. In this scenario, the models should consider agricultural variables not correlated to yield, that is, measures of agronomic parameters different from the variables currently available in the output file of the harvesters, where only agricultural variables highly correlated between themselves are presented. Secondly, for models generated by images, we highlight the need for sensors with higher spatial, spectral, and radiometric resolutions.  
Conceptualization, G.D.M., L.C.M.X., C.A.M.d.A.J. and G.P.d.O.; methodology, G.D.M., L.C.M.X., C.A.M.d.A.J. and G.P.d.O.; validation, B.S.V. and D.J.M.; formal analysis, G.D.M.; investigation, G.D.M., L.C.M.X., C.A.M.d.A.J., F.V.d.S. and G.P.d.O.; data curation, M.d.L.B.T.G.; writing--original draft preparation, G.D.M. and G.P.d.O.; writing--review and editing, M.d.L.B.T.G.; visualization, L.C.M.X.; supervision, G.D.M. and G.P.d.O.; project administration, G.D.M. and C.A.M.d.A.J. All authors have read and agreed to the published version of the manuscript.  
This research received funding provided by Lallemand Plant Care.  
3 rd Party Data and Restrictions apply to the availability of these data.  
The authors declare no conflict of interest.
# Highway Tunnel Defect Detection Based on Mobile GPR Scanning
## 2 Materials  
### System Integration  
The tunnel defect detection system in this paper is called the deformable mobile detect system (DMDS). The DMDS integrates an adjustable manipulator and a dual-frequency GPR antenna. The hardware layout of the DMDS is shown in Figure 1.  
In contrast with the regular section outline of the subway tunnel, the outline of the highway tunnel is complex and multifarious with ancillary facilities. Therefore, the  
Figure 1: The DMDS has an attitude adjustment structure in different directions to adapt to the different shapes of the tunnel section.  
manipulator must have flexible attitude adjustability. The manipulator comprises a base, rotary table, lifting rod, telescopic rod, and antenna deflection device. The manipulator can rotate within \(\pm 90^{\circ}\) in the horizontal direction through the rotary table, rotate \(0\)-\(90^{\circ}\) in the vertical direction through the lifting rod, and expand and retract the manipulator through the telescopic rod. In order to ensure that the signal transmitted by the GPR antenna is vertically incident on the structural surface, the angle can be adjusted through the deflection facility at the top of the manipulator.  
Figure 2 depicts the details of the deflection facility. The deflection facility is installed on the manipulator through the base. The deflecting is realized by the motor. In order to avoid collision during detection, a range finder is installed on one side of the bracket to monitor the distance between the antenna and the tunnel surface. The recorded distance between the antenna and the structure surface each time the signal is transmitted would be used in the subsequent data processing.
# Imaging Flow Velocimetry with Laser Mie Scattering
## 3 Developed Measurement Techniques and Their Imaging Evolution
### Measurement Techniques Using the Time-Of-Flight Principle
#### 3.3.2 Space Measurement
As a result, the depth information is extracted from the particle image. In 1994 [PERSON] and [PERSON] proposed to discard the pinhole mask, to use monodisperse particles and to measure the diameter of the defocused particle image [131]. [PERSON] et al., enhanced this technique for polydisperse seeding particles in 2010 to astigmatism-PTV (A-PTV) [132]. A cylinder lens is used in the imaging system, so that the particle depth information is coded in the ratio of the major axis of the elliptical particle images. Since processing each single particle is computationally intensive, [PERSON] et al. proposed in 2010 a real-time image processing with a field programmable gate array (FPGA) [133; 134]. Furthermore, PTV allows a higher spatial resolution in comparison to PIV, but the data rate is lower due to the requirement of a lower seeding particle concentration that enables a reliable pairing of particle images. In order to increase the possible seeding particle concentration to what is typically used for PIV, [PERSON] et al. introduced a particle tracking based on more than two sequent images in 2013 [33]. In 2014, [PERSON] et al. demonstrated high-speed measurements with A-PTV [135]. The illumination is provided by a pulsed light-emitting diode (LED) with a pulse repetition rate of 250 kHz and a pulse width of 1 us. The imaging is performed with a single ultra-high-speed CCD camera with 312 \(\times\) 260 pixels, which is capable of up to 1 Mfps and an exposure time of 250 ns. As a result, PIV and PTV are well advanced measurement techniques for imaging flow velocities with high measurement rates. A comparison between the PIV and the DGV technique, which are the furthermost developed field measurement techniques based on the time-of-flight and the Doppler principle, respectively, is found in [136] with respect to their physical measurement capabilities. For instance, DGV is capable of coping with high seeding concentrations, and it is not necessary to resolve single particles or patterns of multiple particles in the image. Furthermore, a time-consuming image processing is not required for DGV, and each camera pixel can provide flow velocity information in each frame. Both features are essential for achieving high data rates. On the other hand, the laser requirements are lower for PIV, and the achievable accuracy is assumed to be higher. In order to understand the fundamental measurement capabilities, the fundamental measurement limits of the different flow measurement techniques are treated in Section 4.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)
#### 4.4.3 Interference in UAVs  
Interference with a UAV in flight may prove damaging to the UAV's mission success. The most serious types of interference are those that affect global navigation satellite system (GNSS) transmissions. This may force the UAV to compromise on the quality and accuracy of the data it stores. Once the data is analyzed, this may lead to re-fly the task again. Interference could lead to a complete loss of signal and UAV because it will lose tracking and positioning [157]. There are two main types of interference in UAVs. The first type is internal interference which represents the interference from other electronic devices on the UAV. Due to the compact size of electronic devices, certain GNSS antennas are located next to other electronic and electrical equipment. The second type is external interference which refers to the other interference sources that can come from the UAV itself, whether deliberate or not.  
Certain operations, such as inspecting bridges and other relevant structures, employ UAVs that are close to roads. In this situation, the probability of interference from in-car devices such as jammers increases. This type of device is illegal, inexpensive, and easily available [158].  
A UAV designer should consider utilizing receivers and antennas that are very precise to achieve very high accuracy and to get rid of interfering signals from third parties, thereby providing data with high reliability [159]. Interference in UAVs is depicted in Figure 15.
# Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review
## 4 Discussion
### Our Findings' Highlights  
#### 4.1.1 Prevalence of the Property Graph Model  
Our review of existing DSLs for algorithmic graph processing revealed a predominant reliance on the property graph model. This model is also notably preferred in industry (Amazon, DataStax, IBM, Microsoft, Neo4j, Oracle, and TigerGraph) [84], and it is the subject of recent ISO standards [85].
## 1 Introduction
Reference [18] suggests that changing the non-coherent integration to a coherent integration can increase the sensitivity of the ISL signal acquisition. A novel optimized model based on the shortest algorithm for an ISL navigation constellation is presented [19]. A new approach for determining the precise orbit and ephemeris of the GNSS using an ISL has been developed [20]. Reference [21] summarizes the ISL application method and analyzes the availability of space service for the ISL in navigation constellations, etc. However, the methods presented to date do not focus on the ranging consistency in the ISLs of a navigation constellation. Ranging consistency is affected by the onboard clock equipment, ISL payload device, space environment, and the source transmitter, etc. In this paper, we focus on the influence of the frequency and phase characteristics of the onboard clock equipment on the ranging consistency of an ISL and propose a phase compensation sensor design method for the phase offset of the onboard clock equipment. The next section describes the ISL ranging principle and analyzes the questions regarding the ranging consistency by deviations in the phase and frequency. Section 3 presents the design approach for the sensor. The sensor phase detection error is analyzed in Section 4 and the sensor compensation performance is simulated and verified in Section 5. The final section presents our conclusions.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 6 Conclusions  
Due to their micro- or nanoscale size, mono-dispersion distribution, high encapsulation efficiency, and controllable surface morphology, electrosprayed PCL particles or capsules have garnered research interest for their potential use and advantages in the biomedical field. In order to obtain the desired structure and morphology, the selection of suitable solvents used in electrospraying is one the main parameters and needs to be considered carefully. On the one hand, the formation of a Taylor cone, which is a prerequisite for obtaining tiny and uniform particles, is mainly determined by some solution properties, including electrical conductivity, viscosity, surface tension, and vapor pressure. Its formation is only obtained when the values of these solution properties reach specific ranges or requirements. On the other hand, these solution properties have an effect on the operating parameters, such as the applied voltage, the working distance, and the flow rate during the electrospraying process, to achieve the Taylor cone formation.  
Furthermore, the breakup, as well as the solidification process of electrosprayed droplets, are also affected by these solution properties and operating parameters. Therefore, the control of the solution properties and the operating parameters allows designing the structure and morphology of electrosprayed particles. Electrospraying is an effective route to design micro- or nanoscaled particles with narrow size distribution and various structures via electrostatic interactions. The particle formation mechanism and principles of electrospraying are still complicated and not well understood, as are the relationships between the properties of the electrosprayed solutions, the operating parameters, the process, and the properties of the resulted particles.  
Conceptualization: S.Z. and F.S.; software: S.Z. and F.S.; validation: F.S. and C.C.; writing-original draft preparation: S.Z.; writing-review and editing: F.S. and C.C.; supervision, F.S. and C.C.  
This work was financially supported by the GEMTEX and the China Scholarship Council (CSC).
## 2 Bridge Damage Identification Methods
### Damage Identification Based on Continuous Wavelet Transform
#### 2.2.2 Selection of Wavelet Basis Functions and Wavelet Scales  
Different wavelet basis functions can lead to varying analytical results when using continuous wavelet transform to analyze acceleration time series signals. Choosing an appropriate wavelet basis function according to the specific processing goals can achieve optimal results. Commonly employed wavelet basis functions include the Haar, Morlet, Mexican hat, and Daubechies wavelets. This study preliminarily analyzed the effectiveness of different wavelet basis functions for damage identification. The results showed that the Mexican hat wavelet performed best among the selected wavelet basis functions and thus was chosen for subsequent analysis.  
The relationship between wavelet scale and pseudo-frequency can be expressed by the following equation:  
\[Scale=Fs\cdot Fc/fa \tag{6}\]  
where Scale represents the scale range for calculating wavelet coefficients, \(Fs\) is the signal sampling frequency, \(Fc\) is the wavelet center frequency, and \(f_{a}\) is the frequency corresponding to scale \(a\).
## 4 Simulation Setup, Result Analysis, and Discussion
### End-to-End Delay  
We analyze and discuss the end-to-end delay of the simulation result of the proposed SDAA protocols and compare it to the other standard secure MAC protocols, including SFSC, SDA, and EEST, for determining the network's end-to-end (E2E) delay. Thus, the E2E delay of the network is determined by the total time data takes to reach base station (BS). This includes encryption time at underwater sensor nodes (USN), aggregation time at CH, transmission time from CH to BS, and decryption time at BS. Figure 4 depicts the results of the end-to-end delay for all the approaches. When malicious attackers are increased from 1 to 18, as shown in Figure 4, the delay of the proposed SDAA protocol increases from 0.017 to 0.039. However, the delay of the SDA approach increases from 0.018 to 0.043, the delay of EEST increases from 0.018 to 0.047, and the delay of SEFSC increases from 0.018 to 0.048. Hence, the delay of the SDAA protocol is 18.2% lesser than other contending approaches.
###### Abstract  
Headspace solid microextraction (HS-SPME) and GC-MS were used to investigate volatile organic compounds (VOCs) from cabbage plants infested and uninfested with green peach aphid _Myzus persicae_. The HS-SPME combined with GC-MS analysis of the volatiles described the differences between the infested and uninfested cabbage. Overall, 28 compounds were detected in infested and uninfested cabbage. Some VOCs released from infested cabbage were greater than uninfested plants and increased the quantity of the composition from infested plants. According to the peak area from the GC-MS analysis, the VOCs from infested cabbage consisted of propane, 2-methoxy, alpha- and beta pinene, myrcene, 1-hexanone, 5-methyl-1-phenyl-, limonene, decane, gamma-terpinen and heptane, 2,4,4-trimethyl. All these volatiles were higher in the infested cabbage compared with their peak area in the uninfested cabbage. The results of the study using a Y-shape olfactometer revealed that the VOCs produced by infested cabbage attracted _Myzus persicae_ substantially more than uninfested plants or clean air. The percentage of aphid choice was 80% in favor of infested cabbage; 7% were attracted to the clean air choice and uninfested plants. A total of aphids 7% were attracted to clean air. Comparing between infested and uninfested cabbage plants, the aphid was attracted to 63% of the infested cabbage, versus 57% of the uninfested cabbage. The preferences of _Aphilus colemani_ and _Aphelinus abdominalis_ to the infested or uninfested plants with _M. persicae_ and compared with clean air indicated that parasitoids could discriminate the infested cabbage. Both parasitoids significantly responded to the plant odor and were attracted to 86.6% of the infested cabbage plants.  
green peach aphids; VOCs; parasitoids; _Aphilus colemani; Aphelinus abdominalis_; cabbage 2022, 27, 1522. [[https://doi.org/10.3390/molecules27051522](https://doi.org/10.3390/molecules27051522)]([https://doi.org/10.3390/molecules27051522](https://doi.org/10.3390/molecules27051522))