esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 4 Discussion
For example, CYGNSS FDS winds are not generally valid in tropical cyclones as their retrieval algorithm generally does not provide wind speeds in excess of 20 m s\({}^{-1}\), while the YSLF retrieval algorithm allows for higher wind speeds [20]. Thus, comparing CYGNSS FDS to ASCAT in tropical cyclones would result in a low bias in CYGNSS winds. However, CYGNSS YSLF results were similar to the FDS results, supporting the inference that tropical cyclones were not significantly impacting the overall statistics. An additional possible influence on the results is the effect of the CYGNSS v2.1 dataset, which was afflicted by variability in calibration, both in time (e.g., Figure 4) as well as between different specularpoint tracks. This is a known issue that is substantively addressed by track-wise debiasing in the new climate data record (CDR) v1.0 dataset [43]. Select months from this dataset were processed the same way as v2.1 (c.f. Section 2), but the results were not significantly different than for v2.1 (not shown). This is likely because CDR v1.0 does not appear to significantly improve CYGNSS and ASCAT agreement in rainfall [44].
# Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
## 3 Proposed Method
### DSM Practices and Concepts for the Retail Industry
Therefore, data analysis services can effectively transform this game-like interaction into a feasible DSM model [43]. ESCO providers are the best aggregators because they already have a mature energy management system. However, they currently do not have energy consumption prediction technology, so they cannot provide reference to retailers about the effectiveness and economic benefits of this model. If ESCO providers can successfully develop energy data analysis and prediction technologies, they can provide software to power suppliers with relevant features, and power suppliers will pay licensing fees to ESCO providers. However, in Taiwan, the power industry is focused on the strategic development of the Taiwan Power Company, and unfortunately, the Taiwan Power Company has not implemented such measures. In the industrial or production sector, there are already many energy management methods, and the possibility of suppressing or shifting peak electricity usage is relatively low. Retail electricity users, such as supermarkets, convenience stores, and restaurants, constitute a unique \"small\", \"multiple\", and \"miscellaneous\" market. If they can be integrated through DSM aggregator models, during times of possible electricity shortages, power suppliers (such as the Taiwan Power Company) can issue demands to electricity aggregators and provide rewards to them and all participating retail stores. In addition to effectively managing the use of electricity, the electricity aggregators and all participating retailers can also make a profit. Therefore, developing DSM measures such as aggregators and energy consumption prediction analysis may be an effective approach to energy saving, carbon reduction, and sustainable operations in the retail industry.
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 2 Materials and Methods
### Materials  
The silver nanowires used in this study (purchased from Shanghai Chaowei New Materials Co., Ltd., Shanghai, China) have diameter of 20-35 nm and a length of 10-20 \(\upmu\)m. The SEM image of the silver nanowires is shown in Figure 2.  
The materials used in the PSLC device include a negative LC mixture, a photo polymerizable LC monomer and a photo initiator. The negative LC mixture was purchased from Jiangsu Hecheng new materials Co. Ltd., Nanjing, China. The dielectric constant \(\Delta E\) is \(-8.3\), \(n_{\text{e}}\) is 1.633, \(n_{\text{o}}\) is 1.484, melting point is \(-40\)\({}^{\circ}\)C, and clear point is 94 \({}^{\circ}\)C. The photo polymerizable monomer used in this study is RM82 from Merck KGaA, Darmstadt, Germany. The photo initiator is Irgacure 651 (IR651, BASF SE, Ludwigshafen, Germany). The liquid crystal monomer has acrylate groups at both ends. Under UV irradiation, these acrylate groups will react with each other and form a polymer network. This reaction was started by the photo-initiator. The molecular structure of RM82 and IR651 are shown in Figure 3. The alignment agent used in this study is a vertically oriented polyimide purchased from Shenzhen Dalton electronic materials Co. Ltd., Shenzhen, China.  
Figure 1: Schematic of PSLC device without electric field (transparent state) and with electric field (scattering state).  
Figure 2: SEM image of Ag nanowires used in the experiment.  
The concentration of materials used for PSLC fabrication in this study is shown in Table 1. The amount of negative LC was adjusted slightly when the concentration of silver nanowire changed.
# The Value of Simple Heuristics for Virtualized Network Function Placement
## 4 Modeling Environment and Results  
Two platforms are being used to solve the placement problems: MATLAB to implement our heuristic algorithms, and AMPL to solve the mathematical optimization model and the alternative heuristic proposed in [13]. AMPL is a modeling language designed to be used for solving optimization problems such as linear and non-linear programming problems [16]. We used AMPL to solve the optimization model and the compared heuristic as it works with a wide range of solvers. We used BARON for solving our optimization model in AMPL as described in more detail in [16]. Unlike AMPL, which is designed for solving optimization models, MATLAB allows us to develop our heuristic algorithm for VNF placement.  
The wireless topologies are generated with the use of the method proposed in [17]. The nodes are randomly deployed in a square area, based on a uniform distribution. We generate topologies with 20, 30, 40, and 100 nodes, the network area grows with the number of nodes. We keep the average node density constant, consequently the network size ranges from \(490*490\) m\({}^{2}\) for the 20 nodes network to \(980*980\) m\({}^{2}\) for the 40 nodes network [17]. Nodes in the wireless network are directly connected if their distance is less than or equal to the transmission range of the nodes. This transmission range is constant for all nodes and we verified that all of the generated topologies are connected.  
We used the same parameter values as [1] in order to be able to compare our results. The nodal resource of nodes and the bandwidth of links are values uniformly distributed between 100 and 150 in the first network scenarios. In later scenarios, we increase the available BW and the nodal resource availabilities to observe their impact on the performance of the heuristics. Flows arrive over time, following a Poisson process with an average rate of four flows per 100 time units. Each flow has a lifetime, exponentially distributed with an average of \(\mu=500\) time units and is accompanied by a SG, defining the required NFs and their interconnection to handle this flow. There are 6 NFs perrequest. The nodal resource demands of each NF follow a uniform distribution between 1 and 20. The bandwidth requirement of all links of the request is the same and chosen uniformly from between 1 and 50 units.
# Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
## 3 Descriptive Analysis (RQ1)
Table 10 shows the Journals with more than 100 citations; those with the greatest impact factor are Computers in Industry (11.245), IEEE the Internet of Things Journal (10.238), Computers and Electronics in Agriculture (6.757), Agricultural Systems (6.765), and Information Processing in Agriculture (6.409). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Studies** & **Category** & **Total** & **\%** \\ \hline Theoretical & Reviews & 59 & 34 \\  & Systematic literature reviews & 16 & 9 \\  & **Total** & **75** & **43** \\ Empirical & Modelling and simulations & 68 & 39 \\  & Case studies & 13 & 7 \\  & Surveys & 20 & 11 \\  & **Total** & **101** & **57** \\  & _Overall total_ & **176** & **100** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The studies identified. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Score** & **Country** & **Publications** & **Citations** \\ \hline
1 & The Netherlands & 5 & 1629 \\
2 & India & 37 & 1499 \\
3 & Greece & 3 & 1002 \\
4 & China & 23 & 899 \\
5 & Spain & 5 & 868 \\
6 & USA & 10 & 679 \\
7 & Australia & 5 & 649 \\
8 & Brazil & 9 & 556 \\
9 & France & 2 & 232 \\
10 & Egypt & 3 & 185 \\
11 & New Zealand & 2 & 168 \\
12 & Italy & 7 & 157 \\
13 & Pakistan & 5 & 144 \\
14 & Malaysia & 7 & 115 \\
15 & Portugal & 2 & 107 \\
16 & Canada & 3 & 105 \\
17 & Chile & 4 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Most influential countries. Table 11 shows the ten institutions with the highest citation volume. This ranking was led by universities from the USA, The Netherlands, three universities from China, India, Chile, and two universities from Malaysia and Brazil. Finding 1: The publications and citations of artificial intelligence techniques applied to agriculture increased almost six times over the last three years, demonstrating the importance and timeliness of this research line. The most influential countries identified were among the world's largest food producers, and there were different Journals with high-impact factors in publishing in this field.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model
### Pulley Internal Thermal Analysis
#### 2.4.3 Modeling Structure  
Both solid and fluid regions are created in this numerical study as shown in Figure 8. The solid region is created for the pulley and its connected shaft to simulate the thermal conduction inside the pulley and shaft. In addition, a rounded fluid region encloses the solid region. The diameter of the fluid region is three times larger than the pulley diameter, which provides a sufficiently large fluid domain to simulate the turbulence and small vortex flows around the pulley. The structure of the contacted belt is not considered. Furthermore, the frictional heat caused by the belt slipping against the pulley is applied through the boundary conditions, instead of modeling the slip motions to acquire the heat in the simulation.
## 3 Extracting Influencing Factors
### Traffic-Related Influencing Factors  
Speed expectation and the standard deviation of speed are traffic-related characteristics that express the traffic of link. Some certain correlation between target link and adjacent link exists for these factors [7]. Consequently, we extracted features between a target link and adjacent links from quantities of statistical travel time information from taxis traversing the target link.  
As shown in Figure 2, we extracted link traffic features in accordance with the direction of traffic flow. The traffic flow of black arrow direction of link 81 and link 88 has an effect on traffic flow of link 82 whose direction is black arrow direction too. The traffic flow of link 82 whose direction is black arrow direction also has influence on the traffic flow of link 77 and link 76 whose direction is also black arrow direction. At the same time, that the traffic flow is red arrow direction also influences each other. Therefore, we extracted speed expectation and speed standard deviation of link every 30 min according to the traffic flow direction. Subsequently, the calculation of traffic-related influence factors is as follows. Here, \(l\) denotes link length, \(t_{i}\) denotes travel time of the \(i\)th taxi traversing link, \(v_{i}\) denotes average speed of the \(i\)th taxi traversing link, \(E(v)\) denotes speed expectation of taxi traversing link, and \(D(v)\) denotes speed standard deviation of taxi traversing link.  
(1) Expectation of speed: \(E(v)\).  
We calculated average speed of single taxi traversing link according to Equation (2). As the travel time of every taxi traversing link is different, we computed the expected speed according to Equation (3) for every 30 min, representing the overall taxi travel speed.  
\[v_{i}=l/t_{i} \tag{2}\]  
\[E(v)=\sum{(v_{i}.\frac{t_{i}}{\sum t_{i}})} \tag{3}\]  
(2) Standard deviation of speed: \(D(v)\).  
We calculated standard deviation of speed according to Equation (4) for every 30 min according to historical probe vehicle data, which reflects the variable speeds of different taxis traveling links over every 30 min.  
\[D(v)=\sqrt{\sum{([v_{i}-E(v)]^{2}\frac{t_{i}}{\sum t_{i}})}} \tag{4}\]  
Figure 3 is a line chart reflecting the relationship of expected speed among links 77, 82 and 88. As shown, the expected speed of link 82 increases when the expected speed of adjacent link 88 and link 77 increases, i.e. a positive correlation.  
Figure 4 shows the variance in the standard deviation of speed over time among link 82, link 77 and link 88, respectively. As depicted in Figure 4, the standard deviation for speed decreases since taxis run at slower speeds during rush hours in mornings and evenings. A large variance occurs at other times of the day, when taxis travel with quite different speeds.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 5 Evaluation and Discussion
### Regional Targets, Non-Uniformly Distributed  
In this section, we evaluate the performance of the HEED, FTG, and NU-FTG for varied \(P_{r_{1}}\). The parameters for the simulation environment are configured as follows:  
* \(N_{tgt}=1200\),
* \(\Delta T=0\), and
* \(P_{r_{1}}=10\), \(30\), \(50\), \(70\), or \(90\%/P_{r_{2}}=P_{r_{3}}=P_{r_{4}}=0\%\)  
Figure 4: Results of experiments for varied number of uniformly distributed targets: (**a**) the average number of collected tracks for each sequence; and (**b**) the number of created local fusion nodes.  
We executed three experiments for HEED, FTG, and NU-FTG, respectively. An experiment consists of five sequences with different \(P_{r_{1}}\): 10, 30,..., or 90%.  
Figure 5a shows that the NU-FTG performs better than HEED and FTG. The performance gap between NU-FTG and the others increases as the percentage of deployed targets in \(r_{1}\) increases. This result can be explained with Figure 5b,c. The number of deployed targets in \(r_{1}\) increases as the percentage of deployed targets in \(r_{1}\) increases. However, in Figure 5b, the number of local fusion nodes increases only when the NU-FTG is applied. Especially, in Figure 5c, the number of local fusion nodes in \(r_{1}\) increases. Therefore, the performance of the fusion process for the non-uniformly distributed targets is not degraded only when the NU-FTG is applied.  
Figure 5: Results of experiments for non-uniformly distributed regional targets: (**a**) the average number of collected tracks for each sequence; (**b**) the number of created local fusion nodes for hybrid energy-efficient distributed (HEED), fusion tree generation (FTG), and non-uniform FTG (NU-FTG); and (**c**) the number of created local fusion nodes of each of region for NU-FTG.
# A Visual Object Tracking Algorithm Based on Improved TLD
## 4 Experiments and Analysis
### Tracking Accuracy and Robustness  
#### 4.1.1 Comparison with the Original TLD  
Considering that visual object was represented by a bounding box, the bounding box OR was chosen to measure the tracking accuracy between TLD and the improved TLD for the same video sequences. The more accurate the boundary of the object, the better it is for tracking. Formula (7) indicates the definition of OR:  
\[OR=\frac{\text{area}(R_{T}\cap R_{G})}{\text{area}(R_{T}\cup R_{G})} \tag{7}\]  
where RT represents the estimated bounding box of the tracker, and RG stands for the ground truth bounding box. Likewise, average overlap rate (AOR) is the average of the overlap rate for a full video.  
Experiments were conducted using several video sequences. Table 1 lists the results of four of the video sequences and includes the frames, AOR, and the growth rate of AOR. According to Table 1, the proposed algorithm showed better tracking accuracies. Figure 11 shows the results of the overlap rate in every frame in detail; the vertical axis shows the overlap rate and the horizontal axis represents the frame number. An OR of 0 means that the tracker lost the target. As can be seen from Figure 11, the overlap rate fluctuated during a video. The overlap rates of the proposed algorithm exceeded TLD in the majority of frames. Furthermore, the number of times the target was missed was reduced after the improvements were implemented.  
Figure 10: The difference between global scan and local detection: (**a**) TLD generates detection windows on the full frame; (**b**) the improved TLD just generates windows around the predicted location (red point) calculated by the tracking module.  
Figure 12 indicates that both spatial shifts and scales will result in a low overlap rate. The tracker cannot obtain the accurate state of an object if only part of the object is included in the bounding box. Similarly, too much background may make the tracker mistake then background as a part of the object. Since the patch may be added into the object model in TLD, the tracking precision in the following frames will be influenced by the previous results. Especially for the tracking module of the original TLD, the target feature points are obtained by spreading the points uniformly and calculating the FBE, which is not representative and robust enough to describe the object itself. In contrast, the proposed algorithm uses HOG descriptors in the tracking module, which pays more attention to the statistical characteristics and is insensitive to interference from individual points. A reliable tracking module will lay a good foundation for other parts of a tracker. Thus, the improved TLD achieved better tracking accuracy. Nevertheless, HOG descriptors have drawbacks. They rely on the spatial configuration of the target and perform poorly if the object is severely plastic-deformed. A combination of multiple features is a possible way to solve the problem and worth further research in the future.
## 3 Optimization of Day-Ahead Operation
### Optimization by TVMS-BPSO  
PSO is an optimization algorithm based on the dynamic behavior of a group of agents interacting with each other. Important variables such as the position (\(g_{(i,t,k)}\)) and velocity (\(v_{(i,t,k)}\)) of each agent (i) are considered during the evolution of the algorithm (\(k\)). Using constriction factor approach, the agent velocity can be expressed using (31):  
\[v_{(i,t,k+1)}=\chi\Big{[}v_{(i,t,k)}+C^{a}_{PSO}R^{a}_{PSO}\Big{(}g^{PBEST}_{(t )}-g_{(i,t,k)}\Big{)}+C^{b}_{PSO}R^{b}_{PSO}\Big{(}g^{GBEST}_{(t)}-g_{(i,t,k)} \Big{)}\Big{]} \tag{31}\]  
where \(C^{a}_{PSO}\) and \(C^{b}_{PSO}\) are coefficients selected so that the condition (32) is fulfilled,  
\[\varnothing=C^{a}_{PSO}+C^{b}_{PSO};\ \varnothing>4. \tag{32}\]  
The coefficient \(\varnothing\) is then used to calculate the factor \(\chi\) required in (31),  
\[\chi=\frac{2}{\big{|}2-\varnothing-\sqrt{\varnothing^{2}-4\varnothing}\big{|}}. \tag{33}\]  
Using the coefficient \(\emptyset\), the convergence of the algorithm can be managed.  
TVMS transfer function has been recently proposed by [PERSON] [40] to improve the capabilities of BPSO. TVMS-BPSO uses two sigmoid functions during the conversion of reals to binaries, and these functions are shown in (34) and (35):  
\[S^{a}_{PSO(i,t,k+1)}=\frac{1}{1+e^{\sigma_{(k)}(-\overline{v}_{(i,t,k+1)})}}; \tag{34}\]  
\[S^{b}_{PSO(i,t,k+1)}=\frac{1}{1+e^{\sigma_{(k)}(\overline{v}_{(i,t,k+1)})}}; \tag{35}\]  
where the coefficient \(\sigma_{(k)}\) varies during the algorithm evolution according to (36):  
\[\sigma_{(k)}=(\sigma_{max}-\sigma_{min})\Big{(}\frac{k}{K}\Big{)}+\sigma_{min}. \tag{36}\]  
Once the variables \(S^{a}_{PSO(i,t,k+1)}\) and \(S^{b}_{PSO(i,t,k+1)}\) have been calculated, they are evaluated on (37) and (38) to get the binary variables \(J^{a}_{PSO(i,t,k+1)}\) and \(J^{b}_{PSO(i,t,k+1)}\), which are a preliminary result of the algorithm.  
\[J^{a}_{PSO(i,t,k+1)}=\left\{\begin{array}{l}1;\ R^{c}_{PSO}<S^{a}_{PSO(i,t,k+1)}\\ 0;\ R^{c}_{PSO}\geq S^{a}_{PSO(i,t,k+1)}\end{array};\right. \tag{37}\]  
\[J^{b}_{PSO(i,t,k+1)}=\left\{\begin{array}{l}1;\ R^{d}_{PSO}<S^{b}_{PSO(i,t,k+1)}\\ 0;\ R^{d}_{PSO}\geq S^{b}_{PSO(i,t,k+1)}\end{array}.\right.. \tag{38}\]  
The definitive result from the conversion of reals to binaries is based on the value of the objective function \(O^{a}_{(i,k)}\) and \(O^{b}_{i,k}\) obtained from the evaluation of \(J^{a}_{PSO(i,t,k+1)}\) and \(J^{b}_{PSO(i,t,k+1)}\) previously estimated. Then, the positions to be considered during the next iteration (\(k+1\)) are defined by using (39):  
\[g_{(i,t,k+1)}=\left\{\begin{array}{l}J^{a}_{PSO(i,t,k+1)};\ O^{a}_{PSO(i,t +1)}<O^{b}_{PSO(i,t+1)}\\ J^{b}_{PSO(i,t,k+1)};\ O^{a}_{PSO(i,t+1)}>O^{b}_{PSO(i,t+1)}\end{array}.\right.. \tag{39}\]  
Once the principles of TVMS-BPSO have been exposed. The problem of day-ahead BESS scheduling on a daily basis and the TVMS-BPSO performance to solve this problem are analyzed in Sections 4 and 5, respectively.
# Automated Test Creation Using Large Language Models: A Practical Application
## 4 AI-Powered Quiz Creation and Management Tool
### System Database  
The main entities in the database for storing the generated test questions, the composed tests, and their execution are presented in Figure 5. For visual representation of the database, an Enhanced Entity-Relationship diagram was created during the design process using MySQL Workbench. The main entities in the quiz database are:  
* Users: Contains information about all system users, including administrators, teachers, and learners. Attributes include a unique identifier, username, password, and role in the system.
* Educational Resources: Stores resources in the form of text or files, which are used for generating test questions. Includes a unique resource identifier, text, file name, and author identifier, which is a foreign key to the user who provided the resource.
* Test Categories: Stores test categories that define both individual questions and the overall test. Attributes include a category identifier and its name.
* Questions: Contains the questions generated through the OpenAI API. Includes a question identifier, question category identifier, question content, and an optional foreign key to the educational resource, as questions can be created without being tied to specific educational resources. Optional attributes for additional categorization of the question, used when generating it from the OpenAI API, include Bloom Level, Difficulty, Guidelines, Question Style, and Skill Training.
* Answers: Stores all answers associated with the questions. Consists of an answer identifier, question identifier, answer text, feedback for the answer, and a boolean flag indicating whether the answer is correct.
* Tests: Stores information about the created tests, including a unique test identifier, test title, test author identifier, test category identifier, test creation date, and a boolean flag indicating whether the test is active.  
* Test Questions: Establishes a relationship between tests and questions and contains the identifiers of the test and the question.
* Test Submissions: Records the test solutions submitted by learners and includes a unique submission identifier, test identifier, student identifier, and score.
* Student Answers: Contains the answers given by learners to the test questions, including the submission identifier, question identifier, and an optional identifier of the given answer.