esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Hop Cone Analysis
#### 3.9.2. Hop Cone Yield  
The cone yield per plant exhibits important differences among treatments. Treat 3 revealed the poorest yield performance, with 2.20 kg of fresh cones per plant (Table 6); Treat 2 instead, exhibited the best yield, staging a 1.35 kg per plant (i.e., +61%) yield increase over Treat 3. Hop yield seems to be related not only to N fertilization, that characterizes Treat 2, Treat 3 and Treat 4 at t1 (Table 1), but also to K, that was higher in Treat 2 and Treat 4 in t1, and finally, to the use of microelements at t2 (Treat 2). In trees, usually K and P promote fruit yields and quality [51]. From our study it does not appear clearly that some nutrients were in excess, and might have affected hop yield. However, it may not be excluded that high quantities of P and K in t2 (i.e., T3) could have reduced or inhibited cone production, or inhibited the intake of Zn or other microelements that are involved in cone formation. Moreover, Treat 4, characterized by enhanced content in Fe, Zn, Cu, had a higher cone yield than Treat 1, Treat 3 and Untreat, thus confirming in this case the contribution of Fe and Zn to cone growth [2].
## 2 Analytical Model
### Root Means Square (RMS) Pressure  
#### 2.3.1 RMS Pressure Field  
The RMS (Root Mean Square) pressure represents the time-integrated value of the noise field, or its acoustical energy. It is classically defined by:  
\[p_{RMS}(\mathbf{x})=\sqrt{\frac{1}{\mathrm{T}}\int_{0}^{T}\left|p(\mathbf{x},t)\right|^{ 2}}dt \tag{50}\]  
When developing the total field (\(p(\mathbf{x},t)=p_{inc}(\mathbf{x},t)+p_{ref}(\mathbf{x},t)\), cf. Equation (1)), one gets  
\[\left|p(\mathbf{x},t)\right|^{2}=\left|p_{inc}(\mathbf{x},t)\right|^{2}+\left|p_{ref}( \mathbf{x},t)\right|^{2}+2p_{inc}(\mathbf{x},t)p_{ref}(\mathbf{x},t) \tag{51}\]  
The first and second terms respectively correspond to the incident and reflected fields, whose interaction is given by the third term. By further developing the reflected field (\(p_{ref}(\mathbf{x},t)=p^{\prime}_{ref}(\mathbf{x},t)+p^{\prime\prime}_{ref}(\mathbf{x},t)\), cf. Equation (6)), one gets  
\[\left|p_{ref}(\mathbf{x},t)\right|^{2}=\left|p^{\prime}_{ref}(\mathbf{x},t)\right|^{2} +\left|p^{\prime\prime}_{ref}(\mathbf{x},t)\right|^{2}+2p^{\prime}_{ref}(\mathbf{x},t )p^{\prime\prime}_{ref}(\mathbf{x},t) \tag{52}\]  
Recasting the latter expression into Equation (51) leads to  
\[\begin{array}{c}\left|p(\mathbf{x},t)\right|^{2}=\left|p_{inc}(\mathbf{x},t)\right|^ {2}+\left|p^{\prime}_{ref}(\mathbf{x},t)\right|^{2}+\left|p^{\prime}_{ref}(\mathbf{x},t )\right|^{2}\\ +2\left[p_{inc}(\mathbf{x},t)p^{\prime}_{ref}(\mathbf{x},t)+p_{inc}(\mathbf{x},t)p^{\prime \prime}_{ref}(\mathbf{x},t)+p^{\prime}_{ref}(\mathbf{x},t)p^{\prime\prime}_{ref}(\mathbf{x },t)\right]\end{array} \tag{53}\]  
or, written in terms of the sole (real and virtual) sources' pressure field, \(p_{s}\) (cf. Equations (2), (7) and (15)),  
\[\begin{array}{c}\left|p(\mathbf{x},t)\right|^{2}=\left|p_{s}(r,\theta,t)\right|^ {2}+\left|p_{s}(r^{\prime},\theta^{\prime},t)\right|^{2}+\left|p_{s}(r^{ \prime\prime},\theta^{\prime\prime},t)\right|^{2}\\ +2[p_{s}(r,\theta,t)p_{s}(r^{\prime},\theta^{\prime},t)+p_{s}(r,\theta,t)p_{ s}(r^{\prime\prime},\theta^{\prime\prime},t)+p_{s}(r^{\prime},\theta^{\prime},t)p_{ s}(r^{\prime\prime},\theta^{\prime\prime},t)]\end{array} \tag{54}\]  
By integrating the latter quantity over one source period, one then readily obtains the RMS field in any point of the domain (see Equation (50)). Notably, although such an integration can be performed numerically, one can also derive it via analytical means, as is done hereafter for the present case of a harmonic dipolar source.  
Figure 3: Low frequency (\(St=0.294\)) noise radiation from the trailing edge of an airfoil set at relatively high incidence (\(\alpha=20^{\circ}\)). Instantaneous pressure fields, as recorded along the top (**a**) and bottom (**b**) walls. Incident field (\(p_{inc}(\mathbf{x},t)\)), long dashes with delta symbols), all reflected fields (\(p_{ref}(\mathbf{x},t)\), small dashes with circle symbols), total field (\(p(\mathbf{x},t)\), solid line with square symbols). For visualization purposes, the pressure fields are here normalized by \(A_{max}\).
###### Abstract  
The ultrasonic guided wave-based damage imaging methods are often limited in detection accuracy and resolution due to the dispersive characteristics of guided waves. Finding ways to extract more information from the guided wave field to improve imaging resolution has always been a hot topic in ultrasonic imaging. Based on the same set of guided wave field data obtained by numerical simulation and experiments, this paper compares the detection accuracy and resolution of the time-domain delay-and-sum (DAS) method, the frequency-domain DAS method, and the DORT-MUSIC method, which integrates time-reversal operator decomposition with multiple signal classification. The results show that, compared to the traditional time-domain imaging method, the frequency-domain method that incorporates dispersion relations exhibits significantly higher imaging accuracy. Additionally, the DORT-MUSIC method demonstrates a remarkable advantage in resolution, which can approach the diffraction limit. Related work in this paper provides a research basis for improving the imaging accuracy and resolution for ultrasonic guided waves in the practical application of structure damage detection.  
Keywords:ultrasonic guided waves; damage imaging method; DAS; DORT-MUSIC; accuracy and resolution +
Footnote †: journal: Applied sciences
# A Comparative Study for Control of Quadrotor UAVs
## 2 Materials and Methods
### System Control
#### 2.2.6 Outer Loop  
A simplified translational dynamical model is reported in state space form, with \(\mathbf{x}=(\mathrm{x_{1}},\mathrm{x_{2}},\mathrm{x_{3}},\mathrm{x_{4}}, \mathrm{x_{5}},\mathrm{x_{6}})=\big{(}\ \mathrm{X}^{\prime},\ \mathrm{Y}^{\prime},\ \mathrm{Z}^{\prime},\phi, \theta,\psi\big{)}\) and \(\mathrm{u}=\mathrm{U_{1}}\).  
\[\dot{\mathrm{x}_{1}}=-\frac{1}{\mathrm{m}}(\mathrm{s_{x_{6}}s_{x_{4}}}+\mathrm{ c_{x_{6}}s_{x_{5}}}\mathrm{c_{x_{4}}})\mathrm{u} \tag{53}\]  
\[\dot{\mathrm{x}_{2}}=-\frac{1}{\mathrm{m}}(\mathrm{s_{x_{5}}s_{x_{6}}}\mathrm{ c_{x_{4}}}-\mathrm{s_{x_{4}}}\mathrm{c_{x_{6}}})\mathrm{u} \tag{54}\]  
\[\dot{\mathrm{x}_{3}}=\mathrm{g}-\frac{1}{\mathrm{m}}\mathrm{c_{x_{5}}}\mathrm{ c_{x_{4}}}\mathrm{u} \tag{55}\]  
The aim of the outer loop controller is to control the quadcopter's altitude and to compute the reference Euler angles to be tracked by the inner loop controller. The outer loop controller is divided into two parts: the first part implements three PID controllers that compute the reference linear accelerations from the linear velocity errors, the second part performs a dynamics inversion of the system in equations from (53) to (55), with the assumption that \(\psi_{\mathrm{ref}}=0\), for the computation of the reference pitch and roll angles, \(\theta_{\mathrm{ref}}\) and \(\phi_{\mathrm{ref}}\), respectively, and the total lift force, \(\mathrm{u}\).  
\[\dot{\mathrm{x}_{\mathrm{ref}}}^{\mathrm{P}}(t)=-\mathrm{K_{P}^{p}}\Big{(} \mathrm{x}^{\mathrm{P}}(t)-\mathrm{x}^{\mathrm{P}}_{\mathrm{ref}}(t)\Big{)}- \mathrm{K_{i}^{p}}\int_{0}^{\mathrm{T}}\!\Big{(}\mathrm{x}^{\mathrm{P}}(t)- \mathrm{x}^{\mathrm{P}}_{\mathrm{ref}}(t)\Big{)}\mathrm{dt}-\mathrm{K_{d}^{p}} \Big{(}\mathrm{x}^{\mathrm{P}}(t)-\mathrm{x}^{\mathrm{P}}_{\mathrm{ref}}(t) \Big{)}\,\ \mathrm{p}=1,2,3 \tag{56}\]  
\[\beta=-\mathrm{sign}\Big{(}\ \dot{\mathrm{x}_{\mathrm{ref}}}^{1}\Big{)}\Bigg{(}1+ \Bigg{(}\frac{\mathrm{g}-\dot{\mathrm{x}_{\mathrm{ref}}}^{3}}{\mathrm{x_{ \mathrm{ref}}}^{1}}\Bigg{)}^{2}\Bigg{)}^{-\frac{1}{2}} \tag{57}\]\[x_{\text{ref}}^{4}=\arcsin\left(\frac{\text{Bm}}{\text{u}}\right) \tag{58}\] \[x_{\text{ref}}^{5}=\arcsin(\beta)\] (59) \[x_{\text{ref}}^{6}=0\] (60) \[u=m\sqrt{\frac{\left(\dot{x}_{\text{ref}}^{1}\right)^{2}}{\beta^{2}}+ \left(\dot{x}_{\text{ref}}\right)^{2}} \tag{61}\]  
Equations from (56) to (61) express the full outer loop trajectory controller. The control parameters (\(\text{K}_{\text{p}}^{i}\),\(\text{K}_{\text{i}}^{i}\),\(\text{K}_{\text{d}}^{i}\)) can be set in order to ensure satisfactory dynamics for different 3D complex trajectories of reference, expressed by the signals \(x_{\text{ref}}^{i}(t)\). In order to avoid numerical problems, \(x_{\text{ref}}^{1}\) is forced to have a lower bound different from zero in absolute value. A similar approach is proposed in [11].
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation  
### Multi-Robot Formation Control Simulation Experiment  
The simulation experiment in this article was performed using the MATLAB simulation platform under the Windows operating system. In MATLAB, assuming that the robot was a first-order dynamic model, the size of the designated area was \(10\times 10\) m\({}^{2}\), and it was planned to control four robots in this area to complete square, diamond and linear formations. A total of 1000 \(\times\) 1000 virtual sensors were evenly placed in the area, and the values of the sensors were fitted to the density function of the CVT algorithm. By modifying the density function, the robots were controlled to form the desired formation. It can be seen from Figure 4 that the initialization positions of the four robots were all near the origin.
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 5 Experimental Results
### Medium-Scale Dataset Experiments
When compared to the BKS of these datasets, the IACA finds better solutions on four datasets. Although IACA was unable to find better solutions on more datasets, the gap between IACA's solution and BKS is modest. The average difference between the solution of IACA and the BKS of 14 datasets is 1.24%. The running time comparison of different algorithms is shown in Table 10. Additionally, to visualize the differences more clearly, a line chart based on the table data is shown in Figure 5.  
The overall running time of the IACA is shorter than that of the traditional ant colony algorithm, and when the number of customer points is the same, the greater the overall customer demand is, the more time is saved. Comparing the running times of the IACA with those of the other three modified ant colony optimization algorithms reveals the following: the running time of the IACA is less than that of the ASGCT and is similar to those of the ACGCT and MMGCT. However, the running time of the IACA is slightly longer than that of the ACGCT and MMGCT on the datasets of 100 customer points. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **TACA/s** & **ASGCT/s** & **ACGCT/s** & **MMGCT/s** & **IACA/s** \\ \hline S51D1 & 22.28 & 21.84 & 19.20 & 19.06 & 19.59 \\ S51D2 & 24.25 & 22.58 & 20.13 & 20.05 & 20.09 \\ S51D3 & 26.31 & 23.19 & 21.63 & 20.87 & 20.86 \\ S51D4 & 30.77 & 24.41 & 22.23 & 21.90 & 22.50 \\ S51D5 & 29.43 & 23.62 & 21.84 & 21.01 & 21.53 \\ S51D6 & 35.78 & 25.87 & 24.15 & 23.21 & 24.22 \\ S76D1 & 45.50 & 43.13 & 38.05 & 37.76 & 39.59 \\ S76D2 & 47.87 & 44.11 & 39.36 & 41.42 & 40.67 \\ S76D3 & 51.13 & 45.81 & 41.08 & 42.47 & 41.19 \\ S76D4 & 56.60 & 46.92 & 42.44 & 44.58 & 43.10 \\ S101D1 & 73.53 & 72.02 & 65.24 & 66.15 & 66.95 \\ S101D2 & 80.54 & 73.81 & 65.36 & 68.58 & 67.93 \\ S101D3 & 85.66 & 75.20 & 67.11 & 67.49 & 69.53 \\ S101D5 & 94.52 & 76.83 & 69.51 & 69.93 & 71.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Average running time of algorithms on medium-scale datasets. Figure 4: Reduction rate compared to the best solution of the TACA. Comparing the quality and running time of the solutions verifies that the comprehensive improvement method proposed in this paper is effective on medium-scale datasets.
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae
#### 3.3.6 Scalability Considerations  
In addition to demonstrating the effectiveness of PBR-1 and PBR-2 in treating raw whey wastewater, it is important to consider their scalability for industrial applications. The photobioreactor system used in this study was designed for indoor operation, using low-cost LED lighting and multi-tiered cultivation systems inspired by innovative indoor farming techniques [10; 11]. Recent advancements in stackable horizontal planar waveguide modules further enhance the scalability of our approach by optimizing space usage and light distribution--critical factors in industrial operations [21]. This controlled environment enables consistent operation regardless of external seasonal variations, ensuring stable light availability and temperature, which are critical for process efficiency and scalability.  
However, for outdoor applications where the light source is solar, the stacked photobioreactor system used in this study would be inefficient because of limited light penetration. In such scenarios, open-pond systems or alternative designs that optimize sunlight exposure would be more appropriate for duckweed cultivation. It is important to note that in outdoor systems, seasonal variations would indeed impact treatment efficiency due to fluctuations in irradiance and temperature. These variations could affect the growth rates of both duckweed and microalgae unless additional measures, such as a thermostat system, are implemented.  
Overall, while this study serves as proof of concept, the design and technology used are translatable to larger scales. This does not exclude other designs reported in the literature for the cultivation of duckweed and microalgae that could also be effectively coupled with sequential culture systems [21; 74]. Future research should focus on pilot-scale experiments to validate these findings and investigate operational efficiency in an industrial context. If outdoor conditions are preferable, the design and performance of alternative systems should be explored in varying seasonal conditions to assess their viability for large-scale implementation.
## 3 Results and Discussion
### Narrative Review
#### 3.1.1 Customized 3D-Printed Cage Implant Design Aspects
Firstly, 4 WEB Medical is the primary provider mentioned in the majority of the studies, supplying a wide range of implant designs, including spherical, polyhedral, and wedge-shaped configurations. The company's implants are known for their open architecture, truss structure, and porous surface features (Figure 2a) to enhance osseointegration. Restor3D uses SLM to produce custom gyroid lattice implants, which are reported in the patent as having a stiffness modulus of 3-14 GPa and compression strength of 50-230 MPa, depending on the porosity values (55-85%) and wall thickness [24]. Shenshi Medical Device was noted for producing spherical implants in one paper [21], with a central hole to accommodate the intramedullary fixation nail. The dimensions of 3D-printed implants for foot arthrodis are customized to match the anatomical requirements of each patient. For example, in the study by [PERSON] et al. [18], the implant was designed with a height of 30.5 mm, a base width of 46 mm, and a depth of  
Figure 2: Images of patented designs: (**a**) truss implant [20]; (**b**) gyroid lattice unit cell [24]. 48 mm, with a 14 mm hole for retrograde nail fixation, allowing for proper stability and integration with the patient's anatomy (cages). Similarly, in the study by [PERSON] et al. [29], spherical implants were produced with diameters ranging from 42 mm to 50 mm, ensuring that they conformed to the dimensions of the bone defect and provided adequate structural support. In cases of large bone loss, such as in [PERSON] et al. 's study [31], where an 85 mm segment of the distal tibia was replaced, the implant design not only had to account for the missing bone, but also included features like mesh structures and spikes to promote osseointegration. In [31], the implant was designed with 4-mm-thick side walls and a 2 mm mesh for bone ingrowth, with additional 2 mm spikes to enhance the stability. These dimensional considerations are critical in ensuring that the implant can withstand mechanical forces while facilitating biological integration. In another example from [PERSON] et al. [21], spherical-shaped implants were created with central channels to accommodate retrograde interlocking nails, with two different sizes available, 30 mm and 34 mm in diameter (cages)--with the last one being implanted. This design ensures that the implants are both structurally sound and capable of promoting bone growth and long-term fusion. In [PERSON] et al. [32], three polyhedral implants were made available in the operating room for the surgeon to choose from, each differing by 2.5 mm in height increments. In [PERSON] et al. [43], pill-shaped cage implants were presented for cases in which, due to the large void left by a failed TAR, the diameter of a spherical implant would have been too large for the ankle.
# A Novel Security Architecture for WSN-Based Applications in Smart Grid
## 2 Security in WSN-Based SG  
### Attacks  
WSNs suffer from many types of attacks on various network layers [11]. The current work focus on the following four types:  
* DoS Due to the WSNs' limited computational power and capacity of memory, it is vulnerable to Dos attack. Where the network resources will be unavailable. In the context of the PKI, a DoS attack could be conducted against the certification authority which may lead to the failure of the certification process [5].
* Sinkhole Attack The attacker either brings a fake node in the sensor network or gets an agreement of a real sensor node. The attack occurs when a fake node captures the traffic of the network. Once the attack is successful, the fake node can perform a variety of malfunctions, including deleting or altering data. To avoid this kind of attack a strong authentication mechanism is required [5].
* Sybil AttackTo execute this attack, the malicious sensor node occupies several identities. One of most major goals of this attack is to disorder the routing process and communication in WSNs. This scenario may be avoided using a strong identity management mechanism [5].
* Traffic analysis Attack  
To perform this attack, the adversaries analyze the traffic pattern by eavesdropping on wireless communication. Additionally, the adversaries could acquire a lot of information about the network topology and infer the location of important nodes by watching traffic volume and pattern, then launch an active attack, such as DoS. A strong asymmetric encryption scheme could be the first line of defense against eavesdropping [11].
## 3 Calibration System and Improved Speckle Statistics  
### System Errors Sources  
Systematic errors of the IM-CW IPDA lidar may arise for different reasons: (i) uncertainty in temperature and pressure measurements, (ii) frequency stability of the on wavelength and the linewidth of laser, (iii) influence of gases other than CO\({}_{2}\), and (iv) the drift of optical component characteristics. For (i), the HITRAN16 database was used to compute the absorption cross-sections, and some spectral line parameters dependent on temperature and pressure. In the first trial, the temperature and pressure were obtained by the sensors of a CO\({}_{2}\)/H\({}_{2}\)O analyzer (Instrumentation for Biological Sciences Ltd., Li-7500A). The measurement accuracy of the temperature and pressure are 0.5 \({}^{\circ}\)C and \(\pm\) 0.1%, respectively. We assumed that the background concentration of CO\({}_{2}\) was 410 ppm. The uncertainty of the DAOD is about 0.047% when the temperature uncertainty is 0.5 \({}^{\circ}\)C, which contributes less than 0.95 ppm to the error in the CO\({}_{2}\) concentration. The uncertainty of the DAOD is about 0.0012% and the corresponding CO\({}_{2}\) concentration is 0.01 ppm when the pressure uncertainty is 1 hPa. For (ii), the stability of the on wavelength was 0.05 pm after wavelength locking; the influence of this stability on the CO\({}_{2}\) measurement was lower than 0.001% and could therefore be neglected. The linewidth of the on and off radiation sources was less than 1 MHz, and their contribution to the error in the CO\({}_{2}\) concentration could also be ignored. For (iii), the on and off laser wavelengths were carefully selected considering the influence of other gases, especially water vapor. Therefore, (i), (ii) and (iii) were not the source of the large fluctuation shown in Figure 2c. For (iv), the characteristics of some optical components will drift with the ambient temperature. In the preliminary experiments, the optical components in this research were exposed to a changing temperature environment, which led to high uncertainties in the system calibration. Thus, the impact of factor (iv) is discussed below.
# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
###### Abstract
Therefore, comprehensive investigations specifically targeting high-temperature stress and its effects on garlic plants using these imaging techniques are necessary. Such studies can provide valuable insights into the physiological responses of garlic plants to high-temperature stress and contribute to the development of effective management strategies. Various models, including the vegetation index, partial least-squares discriminant analysis (PLS-DA), least-squares support-vector machines (LS-SVMs), deep neural networks (DNNs), and convolutional neural networks (CNNs), have been employed to detect high-temperature stress in snapshot data. The vegetation index quantifies crop vegetation using visible light and near-infrared rays. PLS-DA and LS-SVM are machine learning methods, while DNN and CNN are deep learning methods widely used in various classification tasks [34; 35; 36; 37; 38; 39; 40; 41; 42]. In this study, we extract spectral data through image preprocessing to detect high-temperature stress in southern-type garlic, using and evaluating the models of vegetation index, PLS-DA, LS-SVM, DNN, and CNN. The objective of this study is to assess the impact of high-temperature stress on garlic crop growth using a snapshot-based hyperspectral imaging and multispectral imaging system operating in the visible and near-infrared regions. We will analyze the spectral data acquired from garlic plants subjected to different temperature treatments and evaluate the potential of hyperspectral imaging and multispectral imaging as non-destructive and real-time monitoring tools for high-temperature stress. The findings of this research will enhance our understanding of the physiological changes occurring in garlic plants under high-temperature stress and contribute to the development of strategies for sustainable garlic crop management. The specifics of the study are as follows: (1) establish a snapshot-based visible/near-infrared multispectral imaging system to acquire multispectral images of garlic crops, (2) develop an optimal model capable of discriminating garlic crop growth under high-temperature stress using the acquired multispectral images, and (3) analyze the spatial changes of garlic crops caused by high temperature using the developed optimal model images.