esa-sceva/satcom-chunk-collection huggingface dataset:
# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 2 Implementation of Path Planning Algorithms
### Rrt  
RRT grows a tree from the initial node by sampling the sequence of the random node and tries to make an edge between the nodes. For each iteration, one random sample node will be created from the closest vertex in the tree and this tree grows in the large unsearched areas randomly. The inputs to generate a tree based on RRT are initial configuration (\(q_{start}\)), number of vertices \((K)\), the incremental distance (\(\delta\)), configuration space (C), and the output to reach \(q_{goal}\) and construct a tree (\(T\)) based on RRT. For RRT, \(q_{start}\) represents the starting position of the random tree. Initially, a random node \(q_{rand}\) is created using \(RAND\_CONF\) in the obstacle-free configuration space. Then, the nearest node, \(q_{near}\), is obtained in the tree near \(q_{rand}\). From \(q_{near}\) to \(q_{rand}\) based on the sampling value (\(\delta\)), the sampled configurations are created, which are called \(q_{new}\). Now, the new configuration, i.e., \(q_{new}\), is placed in the line in-between \(q_{near}\) and \(q_{rand}\) with a sampling value of \(\delta\). Here, the value of \(\delta\) is fixed. After placing the \(q_{new}\) node, collision detection will be enabled between \(q_{near}\) and \(q_{new}\). If there is no collision between the \(q_{near}\) and \(q_{new}\) nodes, an edge will be created. This edge will be added to the tree, \(T\). If there is a collision found, the created \(q_{new}\) the node will be discarded and another \(q_{new}\) will be sampled in the obstacle-free space, and again, a collision check will be enabled. The above process will be iterated continuously until \(q_{new}\) reaches \(q_{goal}\). When \(q_{new}\) reaches \(q_{goal}\), the random tree (\(T\)) based on RRT for the entire configuration space is constructed. The collision-free random path from \(q_{start}\) to \(q_{goal}\) is created. Algorithm 1 shows captures of the implemented RRT algorithm.
## 2 Materials and Methods
### Chemical Analysis  
Aliquots of culture media were centrifuged at 10,000\(\times\)\(g\) for 5 min, and supernatants were stored at \(-\)20 \({}^{\circ}\)C until chemical analysis.  
Ethanol concentration, residual sugars, and pH were determined using FTIR spectroscopy (OenoFossTM type 4101, FOSS, Hilleroed, Denmark).  
Lactic acid concentration was analyzed with an L-Lactic Acid Enzymatic Assay Kit (BioSenTec, Portet-sur-Garonne, France).  
Volatile compounds were quantified using headspace-solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). The extraction of volatiles was carried out using a Combi-pal autosampler (CTC Analytics, Zwingen, Switzerland). A volume of 2 mL of sample was placed in a 10 mL vial that was then fitted with a polytetrafluoroethylene (PTFE)/silico septum and maintained under agitation at 40 \({}^{\circ}\)C for 10 min. After sample conditioning, an SPME fiber (divinylbenzene/carboxen/polydimethylsiloxane 50/30 \(\upmu\)m, 1 cm long (DVB/CAR/PDMS, Supelco (Bellefonte, PA, USA)) was exposed for 30 min to the sample headspace and immediately desorbed in the gas chromatograph injector. Volatile compounds were analyzed by gas chromatography coupled to quadrupolar mass selective spectrometry using an Agilent 5973 Network Detector (Agilent Technologies, Palo Alto, CA, USA). Analytes were separated on a Supelcowax-10 (Supelco) 60 m \(\times\) 0.25 mm I.D. with a 0.25 \(\upmu\)m film thickness. The column temperature was held at 4 \({}^{\circ}\)C for 10 min, increased to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), then to 250 \({}^{\circ}\)C at 15 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), and held for 5 min. The injector temperature was 260 \({}^{\circ}\)C, and the time of desorption of the fiber into the injection port was fixed at 5 min. The carrier gas was helium at a flow rate of 1.5 mL\(\cdot\)min\({}^{-1}\). The temperature of the ion source was 230 \({}^{\circ}\)C, and that of the transfer line was 280 \({}^{\circ}\)C. Electron impact mass spectra were recorded at 70 eV ionization energy, 5.1 scan\(\cdot\)s\({}^{-1}\). The GC-MS analysis was performed in the complete scanning mode (SCAN) in a mass range of 35-300 u.  
The compounds were identified by comparison of their mass spectra and retention times with those of standard compounds or with those available in the Wiley 6 mass spectrum library and in the literature, respectively. The response factors of volatile compounds were calculated using a calibration curve obtained by analyzing a hydroalcoholic solution(ethanol 10%, \(v/v\)) with different concentrations of reference compounds (ethanol 96%, 1-propanol, 3-methylbutyl acetate, 3-methylbutanol, ethyl octanoate, ethyl decanoate, 1-hexanol, 2-phenylethyll alcohol, and octanoic acid purchased from SigmaTM (Merck KGaA, Darmstadt, Germany).
## 5 Numerical Results
\begin{array}{llll}\alpha=\{0.065997633200364677...,&0.131994143490292748...,& 0.197981670725993839...,\\ 0.263938884538034848...,&0.329824274254574844...,&0.395569509201723100...,\\ 0.461072959646730428...,&0.526193524526372529...,&0.590744978992414345...,\\ 0.654491128910354268...,&0.71742134576548678...,&0.778352432953974123...,\\ 0.837720734425024994...,&0.894792581480763658...,&0.949065916629282713... \}^{T}.\end{array} \]  
**Test Example 5** (TE5): The following huge nonlinear system is considered  
\[\left\{ \begin{array}{ll}y_{i}y_{i+1}-1=0,&i=1,2,3,...15,\\ y_{15}y_{1}-1=0.\end{array} \right.\]  
The solution is \(\alpha=(1,1,1,...,1)^{T}\). Choosing the initial vector as \(y^{(0)}=(1.5,1.5,1.5,...,1.5)^{T}\), we obtain the following Jacobian matrix. \[\Phi^{\prime}(y)=\left( \begin{array}{cccccccc}y_{2}&y_{1}&0&0&...&0&0\\ 0&y_{3}&y_{2}&0&...&0&0\\.&&&&&&&\\.&&&&&&&\\ 0&0&0&0&...&y_{15}&y_{14}\\ y_{15}&0&0&0&...&0&y_{1}\end{array} \right).\]  
The numerical results are displayed in Table 2 for the test examples TE1-TE5 by which we arrive at \(8^{th}PJ\) algorithm is the most efficient one with least residual error and less CPU time. Also, it is observed from the results, the proposed algorithms require fewer iterations than the compared methods for the test example TE3. However, when comparing number of functional evaluations \((n_{total})\) and inverse evaluations \((n_{inv})\), \(8 thPJ\) algorithm is the best among the compared methods.
## 2 Materials and Methods
### Reverse Transcription Polymerase Chain Reaction (RT-PCR)  
The expression levels of collagen and _MMP-1_ in UVB-irradiated human skin fibroblasts were measured using RT-PCR. First, total RNA was extracted from 5.0 \(\times\) 10\({}^{4}\) CCD986 cells/well treated with SPWB, and 0.1 ng/mL TGF-81 or 0.1% (_w/v_) retinol using the TRIzol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA). cDNA was prepared from 1 mg of RNA using the reverse transcriptase provided with the cDNA kit (First Strand cDNA Synthesis Kit, Ferentas, Hanover, MD, USA) at 37 \({}^{\circ}\)C for one hour. Then, 1-2 mL of cDNA template and a ReddyMixTM PCR master mix (Abgene, Surrey, UK) were mixed, and PCR was performed on a thermocycler (XP Thermal Cycler, TC-XP, BIOER Tech. Co., Hangzhou, China). The optimal conditions for obtaining PCR products of collagen and _MMP-1_ genes were 298 and 35 amplification cycles of 95 \({}^{\circ}\)C for one minute, 60 \({}^{\circ}\)C for one minute, and 73 \({}^{\circ}\)C for one minute. The sequences of the oligonucleotide primers for collagen type I \(\alpha\)1 (_ColA1_), _MMP-1_, and _GAPDH_ (a housekeeping gene) were as follows (Biogenia, Seoul, Republic of Korea): _ColA1_ (forward: primer 5\({}^{\prime}\)-CTC GAG GTG GAC ACC CT-3\({}^{\prime}\); reverse: 5\({}^{\prime}\)-CAG CTG GAT GGC CAC ATC GG-3\({}^{\prime}\)), _MMP-1_ (forward: 5\({}^{\prime}\)-TGG TCT GAA AGC AGT TTG AA-3\({}^{\prime}\); reverse: 5\({}^{\prime}\)-CCA CTC GTC GAC TGC TGA GT-3\({}^{\prime}\)), and _GAPDH_ (forward, 5\({}^{\prime}\)-ATT GTT GCC ATC AAT GAC CC-3\({}^{\prime}\); reverse, 5\({}^{\prime}\)-AGT AGA GGC AGG GAT GAT GT-3\({}^{\prime}\)). PCR products were electrophoresed on 2% agarose gels and stained with ethidium bromide. The intensity of the bands was quantified using a densitometry program (Bio ID; [PERSON], [PERSON], France).
# Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences
## 3 Methodology
### Neutral Language Model
Model Training: * Transfer Learning: The SAAS model leverages pre-trained weights from GPT-3, BERT, and T5, utilizing their language understanding capabilities. * Fine-Tuning: The model is fine-tuned on the normalized shopping dataset. Fine-tuning involves adjusting the model's weights to minimize a loss function (e.g., cross-entropy loss for classification tasks). * Hyperparameter Optimization: Parameters such as learning rate, batch size, and number of epochs are optimized to enhance model performance. 3. Evaluation: * Performance Metrics: The model is evaluated using metrics such as accuracy, precision, sensitivity, specificity, F1 score, and runtime performance. * Validation: The performance on the validation set guides further tuning and adjustments. * Testing: The final model is tested on a separate test set to assess its generalization capabilities. 4. Fine-Tuning Process:  
Figure 4: The sequence prediction process used by the T5 Model. * Data Preprocessing: Normalize the dataset to ensure consistency in the input data. * Model Training: Use the preprocessed data to train the SAAS model, adjusting weights and biases to minimize the error function. * Evaluation: Test the model on a validation set and adjust parameters to improve performance. Figure 5 depicts the all-encompassing structure of the SAAS model, demonstrating the progression of user input through multiple stages to produce tailored shopping recommendations. The procedure commences with the User Embedding Layer, Product Embedding Layer, and Text Embedding Layer, which transform their respective inputs into compact vectors. The embeddings are subsequently fed into the Embedding Layer, where they are consolidated and included into distinct models: GPT-3, BERT, and T5. The results generated by these models are merged in the Concatenation Layer and subsequently subjected to ReLU Activation, Dense Layers, and Dropout Layers for further processing. Ultimately, the data are processed by the Output Layer, utilizing a SoftMax function, and then undergo Fine-Tuning on purchasing Data to generate precise and tailored purchasing recommendations. Following this, the outputs from these models are combined in the Concatenation Layer. The concatenated embeddings undergo ReLU Activation and are processed through several Dense Layers to learn complex patterns. The Dropout Layers help prevent overfitting by randomly dropping neurons during training. The processed data are then passed through the Output Layer, which uses a SoftMax function to generate a probability distribution over possible suggestions. Finally, the model undergoes Fine-Tuning on Shopping Data to optimize its performance specifically for shopping-related tasks. This flowchart highlights the intricate steps involved in transforming raw user input into actionable, personalized shopping recommendations through the SAAS model.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 2 Methods
### Back-End
#### 2.2.5 Forecast of Rice Blast Disease
The following equation counts the number of hours \(W_{i}^{\text{eff}}\) in which leaf wetness occurs, resetting the count if the condition does not occur anymore in the latest four hours (in fact, if the leaf dries, the infective process stops):  
\[W_{i}^{\text{eff}}= \begin{cases}W_{i-1}^{\text{eff}}+\text{LW}_{i},&\text{if }\sum_{j=i-3}^{i}\text{LW}_{j}>0\\ 0,&\text{otherwise}\end{cases}  \tag{7}\]  
By combining the above variables, the infection condition \(\text{INF}_{i}\) for hour \(i\) is achieved when  
\[\text{INF}_{i}= \begin{cases}1,&\text{if }W_{i}^{\text{eff}}>W(T_{i})>0\\ 0,&\text{otherwise}\end{cases}  \tag{8}\]  
Then, for each day \(d\in\{D-1,D,D+1\}\), the risk index (IR) for the rice blast disease is computed as follows:  
\[\text{IR}_{d}= \begin{cases}0,&\text{if }\sum_{h=1}^{24}\text{INF}_{d,h}=0\\ 1,&\text{if }\sum_{h=1}^{24}\text{INF}_{d,h}=1\\ 2,&\text{if }2\leq\sum_{h=1}^{24}\text{INF}_{d,h}\leq 3\\ 3,&\text{if }\sum_{h=1}^{24}\text{INF}_{d,h}>3\end{cases}  \tag{9}\]  
where the subscripts \(d\) and \(h\) mean the hour \(h\) of day \(d\). Finally, the alert level \(\text{LAB}_{D}\) is  
\[\text{LAB}_{D}= \begin{cases}1,&\text{if }\sum_{d=D-1}^{D+1}\text{IR}_{d}<4\\ 2,&\text{if }4\leq\sum_{d=D-1}^{D+1}\text{IR}_{d}\leq 6\\ 3,&\text{if }\sum_{d=D-1}^{D+1}\text{IR}_{d}>6\end{cases}  \tag{10}\]  
SMARTerra computes the risk indices and the alert levels for the whole grid of Sardinia with resolution 100 m and for the higher resolution (10 m) grids of the rice districts. This allows us to assign IR and LAB values to each single pixel of the grids as opposed to the procedure employed by LAORE which performs the computation for a very limited number of locations.
## 3 Data Analysis and Forecasting Process
### Data Pre-Processing
#### 3.2.3 Normalized Processing  
In order to normalize the data, a min-max scaling method is used to scale the data values of input data down to the range of [0,1], which can significantly accelerate the network model training process. The detailed process is shown as follows:  
\[y=\frac{x-x_{min}}{x_{max}-x_{min}}. \tag{19}\]
# A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
## 4 Experiments
### Simulation Results and Analysis
Initially, the OSPF algorithm outperforms the RKP algorithm and the algorithm in this paper because the OSPF algorithm reduces the complexity by selecting the shortest path for transmission, resulting in low latency transmission. However, at a later stage, the OSPF routing algorithm does not consider the status of the key pool of the QKD device, and therefore a longer waiting time may occur. The RKP algorithm, on the other hand, is based on a routing algorithm with the amount of remaining quantum keys in the quantum key pool as the weight. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Mean Value** & **Standard Deviation** & **Least Value** & **Maximum Value** & **Median** \\ \hline OSPF Algorithm & 35.54685 & 22.4593 & 3.2000 & 75.0000 & 35.8500 \\ RKP Algorithm & 47.38364 & 20.96505 & 5.2100 & 83.5000 & 51.19958 \\ This article Algorithm & 53.6791 & 23.25971 & 6.3400 & 86.3500 & 58.82423 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical calculation of the algorithm. In contrast, only the number of keys in the key pool is considered without considering the specific condition of the link. And thus, unreliable connections may be selected for routing in the case of poor link status. In the early preparation stage, the algorithm in  
Figure 8: (**a**) Key blocking probability when traffic volume is 50 Erlang; (**b**) Traffic blocking probability when traffic volume is 100 Erlang; (**c**) Traffic blocking probability when traffic volume is 200 Erlang. Figure 9: Comparison of average latency of key transmission. this paper takes some time to process and calculate the path, resulting in high latency. However, at a later stage, the algorithm in this paper considers the congestion situation. It can optimize the path selection, thus reducing the latency to a large extent.
## 3 Results and Discussion
### Effect of Inner Electrode Shape  
The effect of electrode shape on NO oxidation is shown in Figure 16. The results show that both the rod electrode and the screw electrode can make the oxidation degree of NO\({}_{\mathrm{X}}\) reach 45%. However, the energy efficiency of the rod electrode is higher. The experimental result is different from the result of [PERSON] and [PERSON] under NO/N\({}_{2}\) condition, as they showed screw electrode has a higher NO removal and energy efficiency [16; 18; 19].  
To explore the reasons, the NO, NO\({}_{2}\) and NO\({}_{\mathrm{X}}\) concentration were measured during the discharge process, as shown in Figure 17. The experiment results show that screw electrode has a higher NO and  
Figure 16: The effect of electrode shape on oxidation degree of NO\({}_{\mathrm{X}}\) under oxygen-enriched condition (electrode length is 100 mm, diameter of round rod electrode is 14 mm, diameter of external thread of threaded electrode is 14 mm, pitch is 1.5 mm and thread depth is 1 mm).  
Figure 15: Effect of electrode materials on oxidation degree of NO\({}_{\mathrm{X}}\) (electrode length is 100 mm and inner electrode diameter is 14 mm).  
NO\({}_{\rm X}\) concentration and a lower NO\({}_{2}\) concentration under the same SED. The distribution of E in the gas gap of DBD reactor with different inner electrode shape was simulated to analyze the mechanism, as shown in Figure 18. The simulation result shows that electric field strength (E) of the screw electrode near the top of the screw is much higher than the surface of rod electrode, which promotes the generation of N radical and the reduction efficiency of NO under NO/N\({}_{2}\) component [41]. However, the reduction of NO with N radicals is almost completely counterbalanced by the production of NO at 10% O\({}_{2}\) concentration [30; 42]. Moreover, the high E/N makes the generation efficiency of O radicals decrease, as shown in Figure 9b. As a result, the NO oxidation and energy efficiency of DBD reactor with screw electrode decreases under oxygen-enriched condition.  
Figure 17: Effect of electrode shape on NO, NO\({}_{2}\) and NO\({}_{\rm X}\) concentration (R means, rod electrode; S, screw electrode).  
Figure 18: (**a**) Distribution of E in DBD reactor with screw electrode; (**b**) distribution of E in DBD reactor with rod electrode; (**c**) distribution of E in gas gap of DBD reactor with screw electrode; and (**d**) distribution of E in gas gap of DBD reactor with rod electrode. (Supply voltage is 5 kV, relative dielectric constant of the dielectric barrier is 3.7 and relative dielectric constant of the gas is 1.).
## 7 Simulation Results and Performance Analyses
### The Time Complexity  
In this section, we analyze the time complexity of our proposed EECRP and its competitors, which is shown in Table 2. We can see that the time complexity of the LEACH and the LEACH-ERE is lower compared to other four algorithms. This is because they are older and more basic algorithms, and they are simpler and easier to be implemented. However, their performances in energy consumption are not as good as the newer algorithms that have been improved on the basis of the classic clustering approaches. The improved algorithms such as the EGRC, the FBCPSO, and the FCMMFO have the time complexity of \(O(n^{2})\). The time complexity of our proposed EECRP is the same as these three algorithms, but the EECRP has the best performance in reducing the energy consumption, prolonging the network lifecycle, and decreasing the packet loss rate. Moreover, in the EECRP, the training process of the BPNN and the process of the GA are accomplished by the SN as the SN has energy supplies. That can save the energy of nodes and extend the lifecycle of UWSNs. Therefore, our proposed EECRP possesses a high value and a wide prospect of applications in UWSNs. In addition, in the future research, we plan to lower the computational complexity of our protocol while keeping the energy-efficiency in UWSNs.
# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 4 Architecture of MR Systems
### Network Issues  
The network is an important part of the MR architecture that provides communication between the client and the server through a configuration mechanism. When the IP of the network is achieved, each client can communicate with other clients and the server to access the MR package containing the virtual model [97]. In this mechanism, considering privacy issues is essential. Privacy in MR spaces contains two channels, i.e., real and virtual, which require separate mechanisms [81]. For example, when content is shared, concern regarding access and encryption is important; however, simultaneously, it is important to understand who is inside the remote controller. The network performs the process by following standard protocol. The network protocol (such as TCP/IP) provides the ability to combine various sources of tracking information, justification, registry, and other sources, such as AI and analytical engines, and then runs the online script [98]. Using the appropriate protocol is critical, especially when uploading a video file during which there is an increase in the number of clients and a decrease in bandwidth. Ref. [99] proposed a demand driven approach (which, of course, has the challenge of delays) or a solution, such as the upload of a video with separate wiring (which suffers from synchronization issues) and IP multicast architecture based on 1 to N communication. Structures, such as distributed interactive simulation (DIS), take latency into account [100]. Apart from these issues, MR platforms require a new generation of networks that allow adaptation based on the individual (user). For example, when a video file is sent, each user needs to focus on a specific area and content. This problem requires greater network bandwidth. Using a 5G system solves the problem by using multiple-input multiple-output (mMIMO), or broadband created by millimeter-wave systems [101].  
Of course, despite the importance of the proposed architectures, studies on proposing new Web architecture are needed. This architecture supports concepts such as shape dynamics and geometries. This architecture provides more contextual data and discovers relevant information through associative browsing and orchestrating content to support data from different sources in a dynamic environment [87; 88]. In distributed MR based on using sensor data, increasing the remote participant will require the use of several centralized controls and dynamic shared state management techniques that maintain interactivity [90]. Edge computing can be used to decrease delays and increase service quality while sending a portion of large data required for display. For example, we can refer to the MR network-oriented application that operates a robot through user gestures and displays location-aware information through the edge server. In this structure, one edge server is provided for the robot while another is used for user information. This architecture sends only the portion of the information required for display for the application [102].
## 6 Results and Discussions  
### Study System  
In order to verify the effectiveness of the proposed method, a case study was carried out at the modified IEEE-33 bus system, in which three MGs were located at the nodes 12, 24 and 30, as shown in Figure 1[27]. The basic data for the PV power generation and load demand in each MG are shown in Figure 3. The control range of the transferable load was 80%-120% of the load demand. The battery was selected as an energy storage device with a total capacity of 200 kWh; the initial state of charge (SOC) was 0.2 and the maximum and minimum allowable SOCs were 0.9 and 0.1, respectively. The maximum power allowed to charge and discharge the battery was 50 kW, the charging efficiency \(\eta^{ch}\) and the discharging efficiency \(\eta^{dis}\) were 0.9, and \(k_{1}\) took 0.0004 while \(k_{2}\) took 0.001. The penalty factor \(\theta_{1}\) and \(\theta_{2}\) took 100. The grid electricity price was taken and the actual time-of-use electricity price is shown in Table 1.