esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
An SMI technique for the early screening of heat stress during the growth of Chinese cabbage within the Vis/NIR region was investigated, and an SMI system and VI, PLS-DA, and LS-SVM models were constructed and analyzed. Then, the determinant accuracy of VIs and the two models was calculated with an increase in temperature (heat stress intensity) and compared with each other for the evaluation of their classification performance. As the temperature increased, classification performance increased. The accuracy of the VIs was much lower than that of the developed models because the PLS-DA and LS-SVM models used more wavelengths than the VI models for their model development. Among them, the LS-SVM showed the highest accuracy. Therefore, the distribution patterns of heat-stressed areas in Chinese cabbage samples were obtained using PLS-DA and LS-SVM models with an increase in temperature. Subsequently, they were compared with their RGB images for performance assessment. It was difficult to recognize the significant differences between the heat-stressed and sound parts in the RGB pixels with the naked eye. However, the dynamic changes in the heat-stressed area of the sample image were clearly distinguished using the distribution maps visualized using the PLS-DA and LS-SVM models. Hence, the developed SMI technique may constitute an alternative method for a rapid, accurate, and early screening system for real-time heat stress evaluation to prevent excessive heat stress in Chinese cabbage. This approach introduces a promising research avenue for the development of snapshot-based multispectral imaging systems for various cultivars of _Brassica oleracea_.  
[PERSON] conceived the overall contents and structure for this article. [PERSON] and [PERSON] wrote and reviewed successive drafts. [PERSON] and [PERSON] led the data analysis drafted tables and figures. [PERSON] and [PERSON] analyzed growth information of Chinese cabbage. All authors have read and agreed to the published version of the manuscript.  
This work was carried out with the support of \"Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ01501904)\" Rural Development Administration, Republic of Korea.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Dueling DQN-Based Routing Protocol
### The Proposed Routing Protocol  
The framework of our proposed routing protocol is shown in Figure 3. The core module was a dueling DQN-based geographical-routing decision system in the network layer. This module was responsible for learning each node's Q-value according to the input state information. The data link layer and physical layers were used to acquire the ETX and SNR metrics, which could be used for estimating the link quality. At the same time, each node needed to extract additional state information from the established neighboring table and execute a free route loop operation.  
According to the designed routing protocol architecture, the routing process was divided into two phases: the first phase was the neighboring table establishment and maintenance, and the second phase was the routing decision.
# A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor
## 4 Experimental Results
### Results  
The visual comparison between planned path and the path actually followed by the simulated quadrotor and double pendulum are shown in Figures 5 and 6 for the MPC-PD and the benchmarking PD-PD control systems, respectively. As can be seen, the MPC-PD system achieves highly precise path-following, while the path followed by the PD-PD control system is quite irregular. Figure 7 shows the swing angle of the bottom pendulum mass, \(\theta_{2}\), along the time of the path achieved by the MPC-PD control system. The maximum value is \(\theta_{2}=0.23\) rads. Figure 8 shows the evolution in time of \(\theta_{2}\) under the PD-PD control system. Its maximum value is \(\theta_{2}=0.61\) rads. Thus, we achieve a three fold reduction of the double pendulum swinging in the simulated trajectory conditions.  
Due to the lack of performance of PD-PD control configuration, in the following we focus on the MPC-PD controller. Figure 9 plots the variation in time of the vertical tension of the first component of the double pendulum, that is, \(T_{1Z}\), which represents the tension of the cable attached to the quadrotor and supporting the combined weight of the two  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline mass, \(m\) & 1 kg \\ arm length, \(l\) & 0.25 m \\ inertia moments, \(l_{xx}=l_{yy}\) & \(5\times 10^{-3}\) [Nms\({}^{2}\)] \\ inertia moment, \(l_{zz}\) & \(1\times 10^{-2}\) [Nms\({}^{2}\)] \\ propeller thrust coefficient, \(b\) & \(3\times 10^{-6}\) [Ns\({}^{2}\)] \\ drag, \(d\) & \(1\times 10^{-7}\) [Nms\({}^{2}\)] \\ \hline \end{tabular}
\end{table}
Table 1: Quadrotor dynamic parameters.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(m_{1},m_{2}\) & 0.3 kg \\ \(L_{1},L_{2}\) & 0.5 m \\ \hline \end{tabular}
\end{table}
Table 2: Driven double pendulum data.  
particle masses, \(m_{1}\) and \(m_{2}\). Finally, Figure 10 plots the orientation of the quadrotor \(\theta\) along the simulation time. As we can see, it falls within the maximum and minimum limits defined in Section 3. The quadrotor adopts a changing pitch angle orientation, so that it can compensate the swing of the double pendulum, converging to a stationary hover attitude at the end of the experiment.  
Figure 5: Path planned (green) and followed (blue) by the quadrotor in XZ plane under control of the MPC-PD control system.  
Figure 6: Path planned (green) and followed (blue) by the quadrotor in XZ plane under control of the PD-PD control system.  
Figure 8: Swing angle \(\theta_{2}\) of the second pendulum along time achieved by the PD-PD control system.  
Figure 7: Swing angle \(\theta_{2}\) of the second pendulum along time achieved by the MPC-PD control system.  
Figure 9: Vertical tension \(T_{1}\) of the first pendulum under MPC-PD control.
## 4 Receiver Configuration and Simulated Scenario Settings  
For the sake of simplicity, the GNSS receiver configuration (as defined in terms of the constellation and frequency bands to be used in the validation) is restricted to GPS L1 single frequency (GPS SF) and Galileo E1 single frequency (GALILEO SF). The RUT was first configured in factory settings mode and then all the necessary modifications, based on the requirements of each individual test case scenario, were applied. It must be noted, however, that the test procedure and set-up is also equally applicable to other receiver configurations.  
Since the RUT compensates for the atmospheric effects, atmospheric modelling capability is required in the testing activity. The GNSS simulator supports a number of models to simulate such effects and, in the tests, Klobuchar and Saastamoinen models were used for the ionosphere and the troposphere, respectively. The receiver was configured in static stand-alone mode. Table 1 provides an overview of the simulated scenario settings, including also the start time, the duration, the GNSS signal power, and the interference power levels for the different test methodologies.  
Figure 3: Sensitivity test profile.  
When performing the tests, an elevation mask and a C/N\({}_{0}\) mask are applied for the receiver's PVT computation. Satellites with elevations lower than 5\({}^{\circ}\) as well as satellites whose C/N\({}_{0}\) are lower than 25 dB-Hz are excluded from position computation, as reported in Table 2. At the beginning of every test, the RUT is in cold start mode.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 2 Design of Staggered PCD End Mill
### Design of Flute and Insert Pocket  
The flute is designed for rapid chip removal in CFRP milling. The size and shape of flute directly affect the tool's stiffness. A large-space flute makes for easy chip removal while decreasing the tool's stiffness, which restricts high-speed and large-feed machining as well as improves machining efficiency. However, a small-space flute results in decreased smoothness and fast tool wear. Therefore, a large-space flute should be designed considering the tool's stiffness. CFRP has a powdered chip and a large feed can be used, so chip removal is easy. Considering tool fabrication, the flute shown in Figure 2 is designed.  
The insert pocket determines the geometric angles after insert welding, including rake angle, relief angle and cutting-edge angle. As the PCD welding process is used to fabricate the tool in this work, the tool's geometric angles after welding and the shape of PCD composite film after cutting should be considered when designing the insert pocket. According to actual conditions, the insert pocket shown in Figure 2 is designed, which is good for positioning during welding and can reduce fabrication cost.
# Infrastructure Elements for Smart Campuses: A Bibliometric Analysis
## 4 Implications of Findings
### Implications of Findings on Procurement of Smart Campus Infrastructure
#### 4.2.1 Implications on Smart Campus Building Construction or Repurposing  
[PERSON] and [PERSON] [1] reported the role of smart cities in the development of smart campuses from the viewpoint of people, prosperity, governance and propagation, which should be in line with the community, infrastructure sustainability, administration and replicability on campuses. [PERSON] [138] also identified the inclusion and repurposing of teaching, scientific and management services infrastructures in constructing a digital campus. The construction of smart campus buildings depends on the available technology and the existence of a smart city. If the campus is situated within a smart city, [PERSON] and [PERSON]'s [1] approach to creating a smart campus can be adopted along with the findings of this study which is inclusive of energy management systems in smart buildings. Inversely, the chances of repurposing or retrofitting existing campus buildings with the IoTs is another feasible methodology for the provision of smart buildings. What makes a building smart is the energy consumption and management approach, sustainability, IoTs capabilities, smart meters, water management and interconnectivity to harness data.  
Construction or repurposing activities to deliver smart buildings will have to consider pertinent stakeholders, procurement method, contract documentation and arrangement, construction method statement, and information management. Standard procurement and contractual approaches such as traditional procurement and New Engineering Contracts (NEC) may suit smart building construction. For the repurposing of buildings, the Joint Contract Tribunal (JCT) for minor works can be applicable. BIM and GIS have been suggested as good approaches to mapping and constructing campuses [14; 53]. Theapplication of BIM and GIS in smart building construction will lay a foundation for easier facilities management using the Construction Operations Building Information Exchange (COBie) data.
# Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
###### Abstract  
Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world's biodiversity hotspots, Madagascar's lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model's ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with \(r^{2}\) values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m\({}^{2}\)/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and \(-1\) t Cha\({}^{-1}\) year\({}^{-1}\), consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests.  
forest modeling; tropical rainforest; Madagascar; forest productivity; individual-based model +
Footnote †: journal: Physics Letters B  
_Article_  
Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 4 Proposed Solution
### Vehicle Low Traffic Density at Low Speed  
The second case of the proposed scheme involves low vehicle speed under low-traffic conditions. In this case, the virtual mix crowd method is used. The virtual crowd method scans the road environment for neighborhood vehicles in the surroundings, and based on the context of the vehicle neighbor's, the virtual pseudonym exchange process is executed. The target vehicle randomly selects one of the neighboring vehicles to exchange real and virtual pseudonyms with, as shown in Algorithm 4. Recall the beacon message include \(BM(P_{ID},T_{x},V,NeighCount,D,DTresh,PUplate)\) and is used to sense the vehicle traffic environment. In the virtual pseudonym exchange scheme, the vehicle randomly selects one of the transmission range neighbors to exchange pseudonyms and update its _PUplate_ attribute. We discuss the virtual pseudonym exchange process with the help of an example. If vehicle \(V_{1}\) wants to change the pseudonym and has \(V_{2}\), \(V_{3}\), \(V_{4}\), and \(V_{5}\) in its neighbor list. \(V_{1}\) will randomly select one of the vehicles and start an exchange of pseudonyms with \(V_{4}\). After completing the exchange process, both vehicles update their status of \(PUplate\) and publish it to the vehicle's crowd. Each vehicle that exchanges pseudonyms must report to the CA the pseudonyms exchanged. This process will reduce the liability issue in the network. The vehicle generates an image of its real pseudonym and exchanges it with its neighboring vehicle. The receiving vehicle verifies both of these pseudonyms, accepts the real one, and rejects the fake pseudonym. The vehicle generates such an image of a pseudonym that it is difficult for an adversary to identify it. Similarly, other vehicles such as \(V_{2}\), \(V_{3}\), and \(V_{5}\) also apply the virtual pseudonym exchange process in the vicinity. As many vehicles take part in the virtual pseudonym exchange process, this produces a virtual crowd of vehicles in the network, which mixes each vehicle identity and location in the crowd. The pseudonyms exchange process is shown in Figure 7.
## 4 Mean Squared Performance of Individual MISO APA Filters
### Steady-State Performance for MISO Filters  
We want to derive the expressions of individual EMSE at steady state so, taking into account the definition of the a priori estimation error signal for the \(j^{\text{th}}\) MISO filter (14) and Assumption 2, and neglecting the dependency of \(\widetilde{\mathbf{w}}_{n-1}^{(j)}\) on past noise samples, we can approximate the variance relation (36) as:  
\[\mu_{j}\mathrm{E}\left\{\mathbf{e}_{an}^{(j),T}\mathbf{A}_{n}^{(j)}\mathbf{e} _{an}^{(j)}\right\}+\mu_{j}\mathrm{E}\left\{\mathbf{v}_{n}^{(j),T}\mathbf{A}_ {n}^{(j)}\mathbf{v}_{n}^{(j)}\right\}=2\mathrm{E}\left\{\mathbf{e}_{an}^{(j), T}\mathbf{B}_{n}^{(j)}\mathbf{e}_{a}^{(j)}\right\}. \tag{37}\]  
We use this expression to evaluate the EMSE for \(n\to\infty\). However, for this purpose, we need a further assumption [42]. **Assumption 4**. : _At steady state, the input signal \(\mathbf{x}_{n}\) is statistically independent of the a priori estimation error signals \(\mathbf{e}_{an}^{(j)}\), \(j=1,2\), and moreover,_  
\[\mathrm{E}\left\{\mathbf{e}_{an}^{(j)}\mathbf{e}_{an}^{(j),T}\right\}=\mathrm{ E}\left\{\left|\mathbf{e}_{a}^{(j)}\left[n\right]\right|^{2}\right\}\cdot\mathbf{S}^{(j)} \tag{38}\]_where \(e_{a}^{(j)}\left[n\right]\) is the a priori estimation error sample for the first projection (i.e., \(k=0\); we omit the projection order subscript for the simplicity of notation), and the matrix \(\mathbf{S}^{(j)}\in\mathbb{R}^{K_{j}\times K_{j}}\) assumes the following values:_  
\[\mathbf{S}^{(j)}\approx\left\{ \begin{array}{ll}\mathbf{I},&\text{ for small values of }\mu_{j}\\ \mathbf{F},&\text{ for large values of }\mu_{j}\end{array} \right. \tag{39}\]  
_being \(\mathbf{F}\in\mathbb{R}^{K_{j}\times K_{j}}=\text{diag}\left\{ \begin{array}[ ]{ccc}1&0&\ldots&0\end{array} \right\}\)._  
The derivation of the approximation in Assumption 4 can be found in Appendix A. Using Assumption 4, we can rewrite (37). In particular, the first term on the left-hand side is equivalently written as:  
\[ \begin{split}\mathrm{E}\left\{\mathbf{e}_{a}^{(j),T}\mathbf{A}_{ n}^{(j)}\mathbf{e}_{a}^{(j)}\right\}&=\mu_{j}\mathrm{Tr}\left(\mathrm{E} \left\{e_{a}^{(j)}e_{a}^{(j),T}\mathbf{A}_{n}^{(j)}\right\}\right)\\ &=\mu_{j}\mathrm{E}\left\{\left|e_{a}^{(j)}\left[n\right]\right| ^{2}\right\}\mathrm{Tr}\left(\mathbf{S}^{(j)}\mathrm{E}\left\{\mathbf{A}_{n}^ {(j)}\right\}\right).\end{split}
# Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy
###### Abstract  
Aiming to address the A* algorithm's issues of traversing a large number of nodes, long search times, and large turning angles in path planning, a strategy for multiple improvements to the A* algorithm is proposed. Firstly, the calculation of the heuristic function is refined by utilizing the Octile distance instead of traditional distance, which more accurately predicts the optimal path length. Additionally, environmental constraints are introduced to adaptively adjust the weight of the heuristic function, balancing the trade-off between search speed and path length. Secondly, the bidirectional jump point search method is integrated, allowing simultaneous path searches from both directions. This significantly reduces path search times and the number of nodes traversed. Finally, the path undergoes two rounds of smoothing using a path smoothing strategy until the final path is generated. To validate the effectiveness of the improved A* algorithm, simulations are conducted on ten types of grid maps. Results demonstrate that the improved A* algorithm markedly decreases path search times while maintaining path length, with greater speed improvements observed as the map size increases. Furthermore, the improved algorithm is applied in experiments with mobile robots, achieving significant reductions in average path search times of 79.04% and 37.41% compared to the traditional A* algorithm and the JPS algorithm, respectively. This enhancement effectively meets the requirements for rapid path planning in mobile robotics applications.  
robot; path planning; A* algorithm; heuristic function; jump point search +
Footnote †: journal: Applied sciences
# An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
## 4 Case Studies
### Pareto Performance Evaluation Metrics  
In contrast to single-objective optimization algorithms, it is not possible to evaluate the performance of multi-objective optimization results in terms of only one metric for each optimization algorithm. In this subsection, we choose two common evaluation metrics [15], which are the inverted generational distance as well as hypervolume, as a way to quantify the performance of the algorithms and to compare the performance of other different methods:  
1. Inverted generational distance IGD [16] is an average minimum distance between the solution set obtained from the search for the best and the true solution set. The decrease in the value of IGD means that the algorithm converges more effectively and is more closely related to the real Pareto front. The mathematical expression is as follows: \[\text{IGD}=\frac{\sum_{x\in P^{*}}\min\text{dis}(x,P)}{|P^{*}|}\] (12) in which \(P^{*}\) is the ideal Pareto front, \(P\) is the collection of optimized solutions acquired from the algorithm, \(\min\leavevmode\
obreak\ dis(x,P)\) is the minimum Euclidean distance between the solution \(x\) of true Pareto and the found Pareto solution \(P\) obtained by the algorithm, and \(|P^{*}|\) is the number of solutions in the solution set \(P^{*}\).
2. Hypervolume The hypervolume [17] indicator refers to the volume of the area surrounded by the optimal set of solutions obtained by the algorithm and the selected reference points in the target space. With a larger value of HV, it means that the comprehensive performance of the algorithm is better. The mathematical expression is as follows: \[\text{HV}(X,P)=\bigcup_{x\in X}^{X}v(x,P)\] (13) The HV metric allows assessing the convergence and diversity of the dominant solution set. In addition, the HV metric is obtained without real solution sets, so it can be widely used for engineering problems with unknown results in the real world.