esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Voltage Fluctuations Generated by Arc Furnaces
## 5 Summary  
The main disturbances generated by arc devices are voltage fluctuations. Voltage fluctuations caused by arc furnaces are characterized by high dynamics of changes. The value of the voltage fluctuations depends mainly on the melting phase in the arc furnace and on the short-circuit power of the network in relation to the power of the arc furnace transformer. The periodicity of voltage fluctuations (number of changes during a given time, e.g., a second) changes to a lesser extent. Determining voltage fluctuations based on the analysis of voltage waveforms (oscillograms) enables one to assess the nature of their changes very accurately. The voltage fluctuations determined in this way can constitute a database for model research on the impact of arc furnaces on the power system. The assessment of voltage fluctuations based on the analysis of time courses requires the use of specialized measuring equipment and specialized software for data analysis. For this reason, this method has significant limitations in practical engineering applications. Arc furnaces are receivers that cause disturbances that will significantly affect the quality of electricity in the power system through which they are supplied. The main disturbance generated by arc furnaces are voltage fluctuations, which are characterized by rapid changes in amplitude and frequency. Voltage fluctuations are characterized by fast changes in amplitude and periodicity (a few changes in one second). The irregularity of changes in voltage fluctuations causes statistical parameters to be used to evaluate the fluctuations. The results of calculations of statistical indicators describing voltage disturbances and the results of measurements of energy quality parameters indicators are an attempt to detail the assessment of the impact of arc furnaces on the power system. The main aim of the article was to present a method allowing the determination of voltage fluctuations in the operation of arc furnaces. The article analyzes the dependence of the short-circuit power of the \(\rm{S_{CC}}\) network in relation to the power of a furnace transformer and refers to the actual conditions of supplying arc furnaces. Based on model studies and the analysis of the electric energy quality parameters recorded in the power supply networks, the following general conclusions were made: \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\rm{U_{i}}\) & \(\rm{n_{i}}\) & \(\rm{z_{i}}\) & \(\rm{F(z_{i})}\) & \(\rm{p_{i}}\) & \(\rm{np_{i}}\) & \(\rm{(n_{i}\text{-}p_{i})^{2}/np_{i}}\) \\ \hline \(-2.00\) & 7 & \(-1.67\) & 0.0436 & 0.04 & 6.58 & 0.026 \\ \(-1.19\) & 20 & \(-1.00\) & 0.1515 & 0.11 & 16.29 & 0.843 \\ \(-0.38\) & 32 & \(-0.33\) & 0.3623 & 0.21 & 31.83 & 0.001 \\ \(0.44\) & 40 & 0.36 & 0.6368 & 0.27 & 41.45 & 0.051 \\ \(1.25\) & 26 & 1.03 & 0.8485 & 0.21 & 31.97 & 1.114 \\ \(2.06\) & 19 & 1.70 & 0.9564 & 0.11 & 16.29 & 0.450 \\ \(2.88\) & 7 & 2.38 & 0.9918 & 0.04 & 6.58 & 0.026 \\ Sum & 151 & x & x & 1.00 & 151 & 2.511 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical data for the \(\chi^{2}\) Pearson test.
## 2 Related Work
### The Semantic Parsing Approaches  
Semantic parsing-based question-answering systems typically convert the natural language-processing-generated question into some intermediate logical form, such as a query graph or query tree, and then convert the intermediate logical form into query statements, such as SPARQL [13], GraphQL [14], etc., and finally send the query statements to an executor for execution. STAGG [6] converts the query graph's constraint nodes into SPARQL for execution and then adds the constraint nodes to the primary relational chain to acquire the answer framework. Multi-constraint query graph generation (MultiCG) [15] based on the STAGG framework was developed in response to criticism espousing that SATGG cannot handle some complex constraints. MultiCG extends the types of constraints and types of operations and develops more rules to parse complex problems. A query graph generation strategy was put forth that gradually condenses the entire knowledge graph to the desired query pattern [16].  
Deep learning models were also incorporated into the query graph generation process. Since poor entity linking results directly impact the performance of the question-answering system, HR-BiLSTM [7] improves the entity linking and relation detection approaches to improve accuracy. To determine the degree to which query graphs and questions are similar to one another, a similarity computation model based on STAGG was proposed [17]. In order to determine which query graph is the correct response, the model builds a neural network using BiGRU and ranks them according to how similar they are. Additionally, a slot matching model based on a self-attention mechanism showed that performance is greatly enhanced when the model is applied to small QA datasets after learning experiences on large QA datasets [18].  
In addition to the aforementioned studies, other researchers have parsed the issue using various structures. The tree structure serves as a query graph, the question is parsed into a tree structure, and the tree is then encoded into a sequence for subsequent operations by using a tree-based LSTM [19]. The complex question is decomposed into a number of simple questions, and the constraints in the question are abstracted, resulting in a computational tree generated by merging a series of simple questions and constraints [20]. Moreover, the operations are redefined to build the query graph, and the model chooses the top rated one in the query graph set as the answer output for the following session, with overall training accomplished using the policy gradient in reinforcement learning [9].  
This approach, however, has the disadvantages of difficult segmentation training, a reduced ability to parse complex questions, insufficient question representation, and a high cost.
## 2 Materials and Methods  
### Experimental Site and Plant Material  
The experiment was carried out during two consecutive years (2014 and 2015) in a commercial pistachio orchard in Southern Italy (Caltanissetta, 37\({}^{\circ}\)26'02\" N, 14\({}^{\circ}\)03'12\" E; altitude 360 m). Nuts were collected from nine 30-year-old trees, cultivar Bianca, grafted on _Pistacia treebithus_ rootstock.  
The climate at the experiment site is typically Mediterranean, characterized by dry and hot summers and mild winters with irregular rainfalls mostly distributed outside a four/five-months summer drought period [28]. Trees were subjected to three different irrigation treatments: rainfed--no water applied (T\({}_{0}\)), 50 mm (T\({}_{1}\)) and 100 mm (T\({}_{2}\)) of water supplied during the growing season (from May until August). The experimental design was a complete block. Three blocks of nine trees distributed on three adjacent rows were replicated three times for each irrigation treatment. Three trees for each block were selected for their uniformity in the central row for a total of nine trees per treatment. Water was supplied to the trees by two pressure compensating integral driplines per row with emitters (1.6 and 3.5 L h-1) spaced 80 cm along the pipe. Daily climatic data were acquired from a public weather station (SIAS--Servizio Informativo Agrometeorologico Siciliano), 0.6 km far from the experimental site. Cumulated precipitations were 421.2 mm in 2014 and 682.6 mm in 2015. In both years, rainfalls were concentrated during fall-winter months. Taking into consideration the rainfall during the growing season and the amount of water supplied, the total amount of water received by plants was 103.6 mm in T\({}_{1}\) and 158.5 mm in T\({}_{2}\) plants in 2014, while it was 123.5 mm in T\({}_{1}\) and 175.2 mm in T\({}_{2}\) plants in 2015. Rainfed trees received through rainfalls 57.4 mm in 2014 and 80 mm in 2015. The average temperature during the growing season followed very similar pattern in the two experimental years. Starting from April (when the average temperature was 13.8 \({}^{\circ}\)C and 13.6 \({}^{\circ}\)C in 2014 and 2015, respectively), the temperature increased gradually (average temperature in June was 22.7 \({}^{\circ}\)C in 2014 and 22.5 \({}^{\circ}\)C in 2015), reaching the maximum in August (average 26.7 \({}^{\circ}\)C and 26.5 \({}^{\circ}\)C in 2014 and 2015, respectively). At the harvest in September, the average temperature was 24.2 \({}^{\circ}\)C in 2014 and 23.6 \({}^{\circ}\)C in 2015.
###### Abstract  
Satellite data are underutilized in many branches of operational oceanography. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access challenging. The mission of the NOAA CoastWatch Program is to make ocean satellite data easier to access and to apply to operational uses. As part of this mission, the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using common analysis software. These tools leverage the data services provided by ERDDAP, a data distribution system designed to make data access easier via a graphical user interface and via machine-to-machine connections. The course has been offered annually since 2006 and has been attended by over 350 participants. Results of post-course surveys are analyzed to measure course effectiveness. The lessons learned from conducting these courses include using the preferred software of the course participants, providing easy access to datasets that are appropriate (fit for purpose) for operation applications, developing tools that address common tasks of the target audience, and minimizing the financial barriers to attend the course.  
remote sensing; satellites; NOAA; CoastWatch; oceanography; training; education; operational oceanography; ERDDAP; value added provider 28 June 2019; Accepted: 8 August 2019; Published: 10 August 2019
## 7 Performance Analysis
### Simulation Results
#### 7.2.1 Performance Comparison under Different Sun Outage Durations  
In order to simulate the performance of the algorithm under different sun outage durations, based on the simulation results in Section 4, we set the minimum duration of sun outage to 60 s, the longest duration to 180 s, the step size is 20 s and fixed the average load of the link to 0.33. As shown in Figure 11, in terms of packet loss rate, LPRA is always better than other algorithms. The effect of SPF is far worse than LPRA and IADR. This is because, in the scenario with the SPF algorithm, after the link fails, services will choose the shortest path to bypass, making the originally congested link even more congested, resulting in very serious packet loss. The IADR algorithm evaluates link utilization and selects the best path. However, since this selection is made independently by each service, it does not consider global load balancing. After the link fails, the amount of traffic that needs to be rerouted is very large. At this time, the original optimal alternate path becomes unsatisfactory. In terms of latency, LPRA still maintains the optimal performance, but LPRA is most affected by the duration of the sun outage. The main reason is that LPRA uses many detour paths, and the latency performance of the preset paths gradually decreases as the topology changes.
## 6 Result and Discussion
### Maximum Ductility Factor of BRBs  
For the selected BRBs, the envelope of maximum ductility factor under successive application of multi-hazard scenarios is presented in Figure 20. According to the literature review, for the common types of BRB in Japan [47; 48; 49], the maximum ductility demand for BRBs with different configuration and cyclic loading protocols are reported to have a range of 20-24 before exceeding the ultimate capacity. Comparing the maximum ductility factor under Level-2 earthquake (\(\mu\) = 3-8) and multi-hazard scenarios (\(\mu\) = 10-25), it is seen that the ductility demand is three times larger in comparison to a Level-2 earthquake, as recommend in Japanese design provision.
take appropriate measures at the right time to protect the crops from diseases. The UAV with low-altitude remote sensing has more advantages like good mobility, easy construction, and high resolution for obtaining the images [5]. The quality of the crop and yield benefits depends on biotic and abiotic factors. In the past, the farmers rely based on their experiences for the production of the crops. Different types of farmers are moving towards remote sensing platforms like UAV-based technology, which helps them protect the crops. In the future, precision agriculture will rely on Sensors, Robotics, the Internet of Things, Machine Learning, and Decision-based support systems. In [6], IoT-based technology has also been adapted to agricultural systems, incorporating cloud computing, big data storage, security issues, and analytics. In [7], they implemented an energy harvesting mechanism using solar energy and a wind turbine by integrating a long-range (LoRa) communication model in agricultural field.  
This review contributes the best solutions for protecting the crop and pest management to solve the farmer's problem and their day-to-day challenges in the agriculture field. We provide a brief overview to the necessity for UAVs. The goal of precision farming using remote sensing technologies is explained to reduce the potential risks and improve the agricultural yield. We focus on UAVs and their types with clear explanations with a comparison between the different types of UAVs including their technical specifications. The role of UAV in precision pest management is discussed. We provide the conclusion with a challenges and future scope in precision agriculture.  
The rest of the paper is organized as follows. Section 2 gives a brief overview about the precision agriculture. Section 3 describes different types of UAVs. Section 4 juxtaposed the qualitative parameters of various types of UAVs and their applications in precision agriculture. Section 5 investigates the role of UAVs in precision pest management. In the last section we have drawn our conclusions.
## 3 Performance Analysis  
In this section, the Gaussian approximation method is used to derive the theoretical BER of the MSSS-I/Q-IM system over an AWGN channel.
# Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue
## 4 Experiment and Analysis
### Search Route Planning for the Regional Search Stage  
We conducted another experiment in order to verify the performance of the ship search route planning model in the regional search stage. Taking the rescue location as the starting point, a search and rescue area of 109\({}^{\circ}\)13.4\({}^{\prime}\) E\(-\)109\({}^{\circ}\)19.2\({}^{\prime}\) E, 18\({}^{\circ}\)9.9\({}^{\prime}\) N-18\({}^{\circ}\)14.3\({}^{\prime}\) N (about 130 square kilometers) was selected, the average speed of the search and rescue ship was set to 11 knots, the visual range was 6 km, and the effective search and rescue time was 30 min. The differential evolution algorithm [24] and the algorithm proposed in this paper were used for comparison experiments, and the experimental results are shown in Table 5. From the perspective of path length, when the regional search task is carried out within the same time limit, the path planned by the proposed algorithm is shorter, and the regional coverage rate is higher, which provides obvious advantages in terms of planning efficiency.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
###### Abstract  
Accurate determination of the thermophysical properties of hydrogen is a crucial issue in hydrogen system design. By developing computational programs, a statistical thermodynamic model based on fundamental equations of state was implemented to determine hydrogen's thermophysical properties, including the ortho-hydrogen fraction in equilibrium hydrogen, para-ortho hydrogen conversion heat, isobaric heat capacities and enthalpies. The deviations of calculated para-hydrogen enthalpies from REFPROP data were within 2.22%, ranging from 20 K to 300 K at 0.1 MPa, and within 2.32% between 100 K and 1500 K at pressures from 0.1 MPa to 20 MPa. To quantitatively assess the convenience of the statistical thermodynamic method, the running speeds of programs with different methods for determining hydrogen's thermophysical properties were compared. The time required for statistical thermodynamic calculation was 7.95% that required for treading REFPROP data when the performance of the variable density multilayer insulation combined with a one vapor-cooled shield and para-ortho hydrogen conversion was calculated. The programs developed based on the statistical thermodynamic method can be used to determine the thermophysical properties of hydrogen or other fluids.  
thernophysical properties; hydrogen; statistical thermodynamic method; Helmholtz free energy
## 1 Introduction
### Literature Review  
Early tool wear prediction established a physics-based model based on domain knowledge, which considers various machining conditions, such as the material properties, cutting speed, feed rate, cutting forces, etc. The physics-based model depends on expert knowledge and is often limited by the uncertainty of system dynamics. Since then, finite element simulation-based methods have emerged to overcome the limitations of physics-based models, but they still depend on expert knowledge and demand significant computational resources and time [5; 6]. More recently, thriving smart manufacturing incorporates advanced information technologies, which has introduced a tool condition monitoring system. The system can acquire a large amount of processing data for tool wear prediction using machine learning and deep learning [7; 8]. The data-driven methods depend on processing data and learning complex relationships in the data by automatically capturing features without physical laws and knowledge.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 5 Discussion  
An AMSIM method with high precision and efficiency was proposed and applied to a case study on ZY-1-02C and ETM+ images. The advantageous characteristics of the proposed method include relatively independent initial matching value prediction models and an effective method to eliminate gross errors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Time Cost** & **Number of Total** & **Number of Error** \\  & **(seconds)** & **Matches** & **Matches** \\ \hline SIFT & 354.2 & 1897 & 525 \\ A-SIFT & 1252.4 & 2458 & 59 \\ SURF & 261.5 & 1763 & 147 \\ Our Method & 217.8 & 2324 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of computational cost and matching results.  
Figure 10: Comparative results of the checkpoints between modified ZY-1-02C images and the DOM of ETM+.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 4 The Goals and Challenges of Middleware Architectures for WSNs
### Scalability  
Middleware architectures should be scalable to dynamic resources and interfaces to ensure superior performance as the size of the network grows. Scalability is challenged when any change occurs on large-scale networks. For example, when adding new nodes, the network should adopt and synchronize them with the existing nodes. An efficient middleware design is capable of maintaining a large network and adapting to any changes that occur without impacting network performance.
## 4 Experiments
### Results & Analysis  
#### 4.2.1 Recognition Performance of Swin-Transformer  
In the first experiment, we evaluate the recognition performance of Swin Transformer on 8 different radar jamming types. From Figure 4, it can be seen that Swin Transformer only shows certain robustness for some of the tested radar jamming. At extremely low JNR between \(-16\) dB and \(-10\) dB, the identification accuracy for ISRJ, CIJ, NFM] can be maintained between 76% to 95%, while NAMJ, NCJ, NPJ gradually increase from 17-32% to 45-54%. At \(-4\) dB, the recognition accuracy for all jamming types reaches above 80%, and exceeds 90% for all jammings at 2 dB as JNR increases.
## 3 Minimum Expected Delay Based Routing Protocol
### The Update Scheme of MED  
With a view to saving energy, the period of sending hello messages for each node should not be too short, so this may make the performance of MEDR descend a little to update MED and its valid period only through hello messages, when the topology of the network changes frequently and rapidly. The reason is that the spread of MED between neighbors is not timely enough so that MEDR cannot find some paths that should be used for messages transmission. Therefore we introduce the update scheme of MED as follows:  
For any one node (e.g., node i), while receiving a hello message sent by one of its neighbors (e.g., j), node \(i\) judges whether all the following three conditions hold: (a) node \(j\) is a new neighbor, _i.e._, the neighbor list has no record about node j; (b) \(\,\mathrm{MED}_{j}\) is valid and \(MED_{j}+t_{hop}<\mathrm{MED}_{i}\); (c) node \(i\) has other neighbor except node j. If all of the three conditions hold, then node \(i\) updates its neighbor list, generates an update message and broadcasts the message to its neighbors. Or else, node \(i\) only updates its neighbor list. Here, update messages have the same content as hello messages, except the message type and the sending occasion.  
Upon receiving an update message from a neighbor (e.g., node k), node \(i\) checks whether the following two conditions hold at the same time: (a) \(\,\mathrm{MED}_{k}\) is valid and \(MED_{k}+t_{hop}<\mathrm{MED}_{i}\); (b) node \(i\) has other neighbor except node k. If both of the two conditions above hold, then node \(i\) updates its neighbor list and broadcasts the update message to its neighbors; otherwise, node \(i\) only updates its neighbor list.  
The update scheme of MED will increase the transmission overhead of the DTMSN to a certain extent. Fortunately, the overhead resulted from the update scheme is very limited, since the generation and transmitting of update messages strictly bounded by the aforementioned conditions. In the simulation experiments, the ratio of the total number of update messages to that of hello messages is only 4.6% in the worst case (_i.e._, 180 mobile nodes deployed in the scenery of 200 \(\times\)200 m\({}^{2}\)) that the node density is the highest and the number of update messages is the most among all simulation scenarios. Therefore, the transmission overhead of the update scheme of MED is low and acceptable.