esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Communication Scheme Based on CMPS  
### Design of CMPS Controller  
Consider the following \(n\)-dimensional complex chaotic non-linear system as a response system:  
\[\dot{x}=f(x)+v+\epsilon_{1}, \tag{3}\]  
where \(x=(x_{1},x_{2},...,x_{n})^{T}\) is the complex state vector, \(x=x^{r}+jx^{i}\), and the superscripts \(r\) and \(i\) denote the real and imaginary parts of the complex state vector, respectively. Let \(x_{1}=x_{1}^{r}+jx_{1}^{i}\), \(x_{2}=x_{2}^{r}+jx_{2}^{i}\), \(...,x_{n}=x_{n}^{r}+jx_{n}^{i}\); then, \(x^{r}=(x_{1}^{r},x_{2}^{r},...,x_{n}^{r})^{T}\), \(x^{i}=(x_{1}^{i},x_{2}^{i},...,x_{n}^{i})^{T}\). \(f(x)\) is an \(n\times m\) complex matrix, with its elements being functions of complex state variables. \(f=(f_{1},f_{2},...,f_{n})^{T}\) is a non-linear complex function vector. The controller to be designed is \(v=v^{r}+jv^{i}\), where \(v^{r}=(v_{1}^{r},v_{2}^{r},...,v_{n}^{r})^{T}\) and \(v^{i}=(v_{1}^{i},v_{2}^{i},...,v_{n}^{i})^{T}\). \(\epsilon_{1}=(\epsilon_{11},\epsilon_{12},...,\epsilon_{1n})^{T}\) indicates external bounded interference, such that \(|\epsilon_{1I}|<\rho(l=1,2,...,n)\), where \(\rho_{1}\) is a positive constant (\(|\cdot|\) is the modulus of a complex number).  
Consider the drive chaotic complex system \(y=(y_{1},y_{2},...,y_{n})^{T}\) satisfying  
\[\dot{y}=g(y)+\epsilon_{2}, \tag{4}\]  
where \(y=(y_{1},y_{2},...,y_{n})^{T}\) is the complex state vector, \(y=y^{r}+jy^{i}\), with \(y^{r}=(y_{1}^{r},y_{2}^{r},...,y_{n}^{r})^{T}\) and \(y^{i}=(y_{1}^{i},y_{2}^{i},...,y_{n}^{i})^{T}\). \(g=(g_{1},g_{2},...,g_{n})^{T}\) is a non-linear complex function vector. \(\epsilon_{2}=(\epsilon_{21},\epsilon_{22},...,\epsilon_{2n})^{T}\) is bounded external interference, such that \(|\epsilon_{2I}|<\rho_{2}(l=1,2,...,n)\), where \(\rho_{2}\) is a positive constant.  
Figure 5: Bifurcation diagram of time delay complex Chen system.  
For the response System (3) and drive System (4), if there is a complex constant matrix \(H=diag\{h_{1},h_{2},...,h_{n}\}=diag\{h_{1}^{r}+jh_{1}^{i},h_{2}^{r}+jh_{2}^{i},..., h_{n}^{r}+jh_{n}^{i}\}\) satisfying  
\[\begin{split}\lim_{t\to\infty}\|e(t)\|^{2}&=\lim_{t \to\infty}\|x(t)-Hy(t)\|^{2}\\ &=\lim_{t\to\infty}(\|x^{r}(t)-H^{r}y^{r}(t)+H^{i}y^{i}(t))\|^{2 }\\ &\quad+\|x^{i}(t)-H^{r}y^{i}(t)-H^{i}y^{r}(t)\|^{2})\\ &=0,\end{split} \tag{5}\]  
where \(e(t)\) is an error vector, then the chaotic Systems (3) and (4) realize complex modified projection synchronization.  
Therefore, on the basis of active control and [22; 24; 25], we designed the controller as  
\[v=Hg(y)-f(x)+Ke, \tag{6}\]  
where \(K=diag(k_{1},k_{2},...,k_{n})\) is the real control intensity matrix. The specific proof process can be found in [22; 24].
## 3 Results and Discussion  
### Sorption Kinetics  
The effect of shaking time (0-48 h) on the adsorption of Pb\({}^{2+}\), Cu\({}^{2+}\), or Zn\({}^{2+}\) (initial concentration, 20.0 mg-L\({}^{-1}\)) to PPG-nZVI beads (dosage, 5.0 g-L\({}^{-1}\)) was shown in Figure 2. Equilibrium achieved at about 10 h for Zn\({}^{2+}\), 6 h for Pb\({}^{2+}\), and 2.5 h for Cu\({}^{2+}\). The fitting parameters of kinetic curves are shown in Table 1. For most of the metals (i.e., Cu\({}^{2+}\) and Pb\({}^{2+}\)), the pseudo-second-order model fits better than pseudo-first-order model model (based on the larger value of \(r^{2}\)). For Zn\({}^{2+}\), there is not much of the difference between two models for describing sorption (\(r^{2}\) value for pseudo-first-order model and pseudo-second-order model is 0.99 and 0.98, respectively). Since pseudo-second-order model is the most commonly used model to describe sorption of heavy metals to heterogenous surfaces, it could better describe sorption of the heavy metals to beads than pseudo-first-order model in our case. Adsorption rate of each heavy metal ion by the PPG-nZVI beads is different, and the \(k_{2}\) decreased follows in the order of Cu\({}^{2+}\) > Pb\({}^{2+}\) > Zn\({}^{2+}\). [PERSON] et al. suggested that the adsorption mechanism of zero-valent iron towards metal ions is mainly dependent on the standard reduction potential and chemical speciation of the adsorbate under the operating pH [4]. For metal cations such as Zn\({}^{2+}\)/Zn  
Figure 1: Infiltration pool and water intake diagram 1–pool with pumice synthetized guar gum-nanoscale zerovalent iron beads (PPG-nZVI beads) layer; 2–pool without PPG-nZVI beads layer (not shown in the figure); 3–reservoir; 4–water pump; 5–flowmeter; 6–water pipes; 7–sand layer; 8–sawdust layer; 9–pumice layer; 10–fine gravel layer; 11–PPG-nZVI beads layer; 12–partition; 13–pebble support layer; 14–outlet; 15–geotextile.  
(\(-0.76\) V, \(298\) K), whose standard potential is negative or close to Fe\({}^{2+}\)/Fe\({}^{0}\) (\(-0.44\) V, \(298\) K). Electrostatic attraction and chemical adsorption, precipitation (with hydroxide ions) and co-precipitation within iron corrosion products are likely the primary mechanisms. While for both reduction potentials of Cu\({}^{2+}\)/Cu\({}^{0}\) (\(+0.34\) V, \(298\) K) and Pb\({}^{2+}\)/Pb\({}^{0}\) (\(-0.13\) V, \(298\) K) are well above of Fe\({}^{2+}\)/Fe\({}^{0}\) (\(-0.44\) V, \(298\) K), and the uptake of Cu\({}^{2+}\) and Pb\({}^{2+}\) ions would be expected to primarily take place via a redox mechanism.
## 3 Results
### Support Vector Machine (SVM)  
The accuracy of SVM models can be significantly impacted by various parameters, including the kernel function and the \(C\) and Gamma parameters. To obtain the optimal and most accurate SVM model, several models were trained and evaluated, and the best one was selected. The evaluation process was carried out by comparing the predicted damping ratio values against their actual counterparts from the testing dataset, as illustrated in Figure 17. The results indicate the high accuracy of the SVM model in predicting the damping ratio of sand.  
The overall performance of the proposed SVM model to predict the damping ratio for both training and testing datasets is presented in Table 8. The SVM model achieved MAE values of 0.716 and 0.831 for the training and testing datasets, respectively. The corresponding MSE and RMSE values were 0.761 and 0.872 for training and 1.302 and 1.141 for testing, respectively. Moreover, the model's MSLE and RMSLE values were obtained as 0.006 and 0.079 for the training dataset and 0.003 and 0.057 for the testing dataset, respectively. In terms of correlation, the SVM model produced \(R^{2}\) = 0.973 and 0.892 for the training and testing datasets, respectively. Like the proposed ANN model, these results suggest that the SVM model performs well in predicting the damping ratio of sand based on the selected input parameters (the sand particle shape, vertical stress, number of loading cycles and CSR conditions).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Training Dataset** & **Testing Dataset** \\ \hline MAE (\%) & 0.887 & 0.551 \\ MSE (\%) & 1.056 & 0.460 \\ RMSE (\%) & 1.027 & 0.679 \\ MSLE (\%) & 0.007 & 0.001 \\ RMSLE (\%) & 0.084 & 0.037 \\ \(R^{2}\) (–) & 0.962 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Overall performance of the best ANN model to predict the damping ratio for both training and testing datasets.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 4 Experiments and Results
### Comparative Analysis with Image Captioning Models
Moreover, our experiments show that the proposed CoT-based reasoning achieves an average processing time of approximately 4-5 s per image using a standard GPU (RTX 3090). This represents a significant improvement over traditional image captioning models, which typically require 20-30 s per image. Although not yet strictly real-time, this processing speed greatly enhances the practical usability of the system in realistic navigation scenarios.
# A Framework for 3D Plant Simulation of Meal-Kit-Packaging Robot Automation System
###### Abstract  
A data-driven 3D simulation for the robotic automation of the most labor-intensive packaging process in meal kit production was developed using Tecnomatix plant simulation software. The workflow and environments of the existing manual process were analyzed. An existing production site was scanned using a 3D Lidar scanner to create 3D models and design the initial assembly layout. Two types of 3D simulation models, implemented with a single or double delta robot, were designed to determine the optimal robot-automated packaging process. Key performance indicators for simulation models of a manual and two robot automation systems were analyzed. The throughputs of the manual, single delta robot and double delta robot models were 2112, 1510, and 2568 ea/h, respectively. The single robot system achieved only 68.3% of the throughput of the manual process, which is attributed to a cycle time of 2.36 s for picking and placing all components. On the other hand, the cycle time of the double robot system was 1.66 times faster, and the throughput was 1.7 times greater compared to the single robot system. The developed 3D simulation model for the meal kit packaging system demonstrates the potential of robotic automation in addressing the labor shortage issue as well as improving production efficiency.  
meal kit packaging; robot automation; 3D simulation; delta robot; machine vision Article
## 3 Preliminaries and System Model
### System Model  
Referring to the overall architecture shown in Figure 1, the satellite network members for the SGIN in this article include the UE, regenerative LEO with gNB-DU (distributed unit), GS with gNB-CU (centralized unit), and NCC; the terrestrial network members include the UE, gNB, and 6 GC. Due to the heterogeneous 6G environment and different user needs, we put forward two different scenarios of satellite network access: (1) An Unlinkable Authentication Scenario, which requires higher anonymity and unlinkability, based on a short group signature [23]; (2) a Batch Authentication Scenario, which includes massive lightweight UE, where their requirement for anonymity is relatively low. We designed different access authentication protocols for the two scenarios. The UE can select corresponding authentication modes based on their requirements. In addition, the proposed scheme provides roaming authentication methods for users who need to roam across domains.  
The protocol model of the SGIN is shown in Figure 2. The protocol involved in this paper is represented by orange lines. The dashed lines mean that the channel between two entities is insecure, and the solid lines are the contrary. The UE, connected to the satellite, can access the NCC through the two protocols designed in the following section. In addition, since regenerative satellites are equipped with gNBs, the UE can also access the 6G core network through regenerative satellites, transparent satellites, or gNBs, using the AKA protocol. When it is necessary to handover satellites due to their movement, the satellites plan the handover strategy independently. This process is transparent to the UE. When the UE needs to roam between networks of the same operator or roam across thenetworks of different operators, they have to use the protocols of the presented roaming authentication phase according to the situation. The protocol architecture aims to address the required access methods and performance for heterogeneous terminals.
## 4 Proposed Metrics
### Valid Routes
#### 4.2.3 Global Metric
ZTR does not always guarantee the route with fewer hops, and if a node is disconnected temporarily, all of its child nodes are cut off in the upward path, resulting in an increased number of retransmissions in the MAC layer. MPH always guarantees the shortest path, and thus, it produces smaller delays and fewer packet delivery errors, resulting in less energy consumption. The local energy consumption is presented in Figure 18. Local energy is an important metric to analyze how energy is distributed over the network. In Figure 18, we show the total energy in each node for the different protocols. For AODV and DSR, we see that the energy consumption is similar for all of the nodes in the same ring around the coordinator. As expected, the rings closer to the sink spend more energy, since they carry their own packets and transport packets from farther nodes. ZTR has some nodes with higher energy consumption than the other nodes in the same ring; this is because, if a node is temporarily disconnected, the child nodes have no route to send packets to the coordinator and generate more transmission retry packets. In the MPH protocol, the energy of nodes is distributed more uniformly, but still consuming the greatest energy in nodes belonging to rings near the sink node. In Figure 19, the consumed energies explained through Equations (4) to (10) are shown. Here, we implemented the energy model for the four studied protocols: AODV, DSR, ZTR and MPH. The packet rate contributed for each node is 20 packets/s. We see the cost of each protocol for each of the types of tested energies. Thus, we prove that the transmitting and receiving energies are the most influential in the total energy, and this situation becomes critical, because they provide the communication among nodes. Figure 18: Local energy consumption.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 3 Reliability of Tunnel Lighting System
### Reliability Evaluation of Tunnel Lightting System  
How can we evaluate the quality of tunnel lighting? Perhaps some enlightenment can be obtained from the perspective of the reliability requirements of modern control systems.  
Based on system reliability theory, no matter how many components the system contains or how the components are connected, the system reliability \(Ra\) can be deduced mathematically from Equations (5) and (6),  
\[Ra=\prod_{i=1}^{n}Ri\ \ \ \ \ \ (\mathrm{i=1,2,\ldots n}) \tag{5}\]  
\[Ra=1-\prod_{i=1}^{n}Fi=1-\prod_{i=1}^{n}(1-Ri)\ \ (i=1,2,\ldots n). \tag{6}\]  
Here, Equations (5) and (6) are for a series system and parallel system, respectively. Ra denotes the system reliability, \(Fi\) is the \(i\)th unit unreliability; \(Ri\) is the \(i\)th unit reliability.  
For a hybrid system, its reliability can be calculated via Equations (7) and (8), which are actually derived from Equations (5) and (6):  
* Hybrid series-parallel system: assume that the system is \(n\) level in series, and each level is \(j\) level in parallel, \[Ra=\prod_{j=1}^{n}\left[1-\prod_{i=1}^{mj}(1-Rij)\right].\] (7)
* Hybrid parallel-series system: assume that the system is m level in parallel, and each stage is \(j\) level in series, \[Ra=1-\prod_{i=1}^{m}\left(1-\prod_{j=1}^{ni}Rij\right).\] (8)  
In this case, the task of improving the reliability of TLCS is transformed into how to maximize \(Ra\).
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 2 System Model and Problem Formulation
### System Model
Also, the total power consumption of the system is represented as:  
\[\mathcal{P}_{total}=\zeta P+P_{BS}+KP_{UE}+MP_{m}(b), \tag{5}\]  
where \(P=\sum_{k\in\mathcal{K}}\sum_{n\in\mathcal{N}}p_{k,n}\), and \(\mathbf{P}=[p_{k,n}]_{K\times N}\) denotes any possible power allocation matrix and should be subject to:  
\[\mathbf{P}\in\mathcal{P}=\bigg{\{}[p_{k,n}]_{K\times N}\bigg{|}\ p_{k,n}\geq 0,\,\forall k\in\mathcal{K},\,\forall n\in\mathcal{N};\sum_{k\in\mathcal{K}} \sum_{n\in\mathcal{N}}p_{k,n}\leqslant P_{\max}\bigg{\}}. \tag{6}\]  
In this context, \(P_{\text{BS}}\), \(P_{UE}\), and \(P_{m}(b)\), respectively, represent the circuit power of the base station, the power consumption of users, and the power consumption of the \(m\)-th element of RIS. In addition, \(\zeta=\
u^{-1}\), and the parameter \(\
u\) is used to describe the drain efficiency of the power amplifier at the side of the transmitter.
## 3 Results and Discussion
### Oxides of Nitrogen Emission  
Figure 10 depicts the oxides of nitrogen (NOx) emissions recorded in several engine configurations, namely, diesel (D100), DP20, DP20H5 (with 5 lpm hydrogen), DP20H10 (with 10 lpm hydrogen), and DP20H15 (with 15 lpm hydrogen). In a diesel engine, the creation of oxides of nitrogen (NOx) is typically governed by parameters such as the high cylinder temperature, the oxygen concentration in the local burning zones, the residence length, the latent heat of evaporation, and the engine operating state [37; 38]. Every single gasoline sample that was examined exhibited the same trend of NOx generation with increasing engine load. The increasing temperature of the cylinder is the reason for the higher NOx emission consequences [39; 40]. Figure 9 displays the NOx emissions under different diesel (D100), 20% waste-plastic oil biofuel with diesel (DP20), 5 lpm hydrogen (DP20H5), 10 lpm hydrogen (DP20H10), and 15 lpm hydrogen (DP20H15) engine settings. As demonstrated by the graph, under all test circumstances, the NOx emission of the DP20H5, DP20H10, and DP20H15 engines is greater than that of the D100. The highest NOx emissions for HP20D15 have been reported to be 51.0 g/kWh, whereas the lowest NOx emissions for D100 sample is 6.9 g/kWh. The 10 lpm hydrogen-DP20 mix produced the second highest NOx emissions at full load conditions, which was around 61.3%. This demonstrates how the addition of hydrogen influences the variance in NOx emissions during combustion compared to D100 and DP20 mixes. Significant alterations are noticed in diesel fuel containing hydrogen. At different engine settings, the NOx emissions rise with the addition of hydrogen propellant and engine load [41; 42].
### Research Contributions  
In this paper, three contributions have been provided to develop more efficient and effective solutions:  
1. For proposing hybrid protocols by preparing a case study on selecting optimal protocols for replicating adaptive video streaming content over the edge networks' servers, it is important to consider the following factors: The network topology of the edge servers is a crucial factor in determining which protocol is the most suitable. For example, if the edge servers are located in different geographical regions, a protocol that can handle large distances and network congestion would be more appropriate. The protocol chosen should be able to handle adaptive streaming and provide high-quality video playback. The chosen protocol should be able to reduce latency as much as possible. The protocol should be able to efficiently use the available bandwidth to deliver video content to users. A thorough evaluation and testing of each protocol in the edge network environment are necessary to determine the optimal protocol.
2. A redirection algorithm based on Cuckoo search can be proposed to provide an accurate redirecting process of selected edge servers to end-users. The basic idea behind this algorithm is to use the Cuckoo search optimization technique to determine the optimal location of the edge servers that will provide the best performance for the end-users. The Cuckoo search algorithm can be used to find the optimal solution by mimicking the behavior of cuckoos in the natural world. In this algorithm, the edge servers are seen as cuckoos and the optimal location of the edge servers is seen as the best nest. The algorithm uses a combination of random walk and Levy flight, which is inspired by the random searching behavior of cuckoos, to explore the solution space and find the optimal location of the edge servers.
3. The applied protocols mechanism and redirection algorithm can further improve the service of QoE for adaptive video streaming.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Assessment Indicators  
#### 3.2.1 Evaluation Metrics  
Prediction performance is evaluated by four prediction error metrics, namely, the RMSE, MAE, MAPE, and \(\rm D_{stat}\), where N is the number of observations in the test set, and the numbers in \(\rm y_{i}\) and \(\rm y_{i}^{\rm i}\) are the true and predicted values, which can only be predicted if \(\left(\rm y_{i+1}-\rm y_{i}\right)\)\(\left(\rm\dot{y_{i+1}}-\rm\dot{y_{i}}\right)\geq 0\) when \(\rm A_{i}=1\); otherwise, \(\rm A_{i}=0\).  
\[\rm RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(\rm y_{i}-\rm\dot{y_{i}}\right)^ {2}}, \tag{1}\]  
\[\rm MAE=\frac{1}{N}\sum_{i=1}^{N}|\rm y_{i}-\rm\dot{y_{i}}|, \tag{2}\]  
\[\rm MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|\rm\dot{y_{i}}-\rm\dot{y_{i}}|}{|\rm y _{i}|}, \tag{3}\]  
\[\rm D_{stat}=\frac{1}{N}\sum_{i=1}^{N}A_{i} \tag{4}\]
## 3 Analysis and Discussion of Test Results  
### Analysis of Apparent Morphology of Specim after Freeze-Thaw Cycle  
The apparent morphology of the recycled concrete changed considerably as the test progressed. Specimens (ordinary recycled concrete) without freeze-thaw and with 150 and 250 freeze-thaw cycles in clear water, chloride salt solution, and sulfate solution were selected for a comparison of the apparent morphology analysis, using the RAC group (ordinary recycled concrete) as the reference group.  
In Figure 5a, it can be seen that when the specimen is not frozen and thawed, the surface is relatively smooth, flat, and dense, and there are some regular bubble holes generated by the hydration reaction. In Figure 5b, it can be seen that the specimen surface added a small number of scattered holes, and most of them are still relatively smooth and dense. In Figure 5c, it can be seen that some of the mortar on the surface of the specimen has begun to expand, and there is a small amount of shedding; from Figure 5d, it can be seen that there are more holes on the surface of the specimen than during the water freezing and thawing cycle. As can be seen in Figure 5e, the surface of the specimen is uneven, part of the mortar has fallen off, and there are many irregular holes. In Figure 5f, it can be seen that a large amount of mortar was detached from the surface of the specimen, and the area of the holes became larger and the number of holes increased significantly, which was accompanied by the appearance of some cracks, but there was no coarse aggregate exposed. In Figure 5g, it can be seen that the mortar is widely dislodged, the surface is rough and uneven, only a few mortars attached to the concrete surface exist, and the number of holes is greater than the number of clear water freezing and thawing cycles. In Figure 5h, it can be seen that the mortar on the surface of the test specimen is almost completely dislodged, the cracks are extended and increased in number, and some of the coarse aggregates are exposed to the air. In Figure 5i, it can be seen that the mortar on the surface of the specimen shed more, there are many dense, small holes, similar to a honeycomb, and in Figure 5j, it  
Figure 4: Flow chart of capillary water absorption experimental device.  
can be seen that a large amount of mortar is shed on the surface, only a few mortars are left, and there is a small amount of exposed coarse aggregate and some minor cracks. It can be seen that the freeze-thaw damage of recycled concrete is a process that occurs from shallow to deep concrete.