esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article's main findings; and (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article; it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.  
Wind energy is currently the most competitive renewable energy resource, and it has experienced a rapid increase. The installed capacity of wind turbines worldwide reached 600 GW by the end of 2018. China, by far the largest wind turbine market, installed 25.9 GW in 2018, reaching a total accumulated wind turbine capacity of 221 GW. The USA added 7.6 GW in 2018, reaching a total wind turbine capacity of 96 GW. Many other countries also showed substantial growth, such as Germany, India, United Kingdom, Brazil, and France [1]. EU-28 has a total installed capacity of 178.8 GW. Wind power is the second largest form of electricity generation capacity in the EU-28, and it is likely to overtake natural gas installations in 2019. Wind energy covered 14% of EU electricity demand in 2018 [2].  
Wind power technology as a commercialized energy conversion technology covers many disciplines, and large-scale wind power plants are being developed both onshore and offshore worldwide. However, wind power technology still faces many challenges. To further reduce the levelized cost of wind energy, increase reliability, and support power grid operation and control [3, 4], research efforts to address various challenges of wind power technology are still very active.  
This Special Issue presents some research on wind power technology to highlight the most recent wind power research activities. Ten papers have been selected for this Special Issue, which are related to a number of important topics of wind energy application, including the wind power industry value chain, wind turbine systems, wind farm operation and control, wind speed forecast, and wind power in power systems.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 2 Analysis of Storage and Exchange Efficiency for SAR Data
### Analysis of Existing Data Exchange Solutions
#### 2.3.3 Scalar Pipeline  
Pipeline technology is one of the most commonly used techniques for instruction processing in general-purpose processors, which can significantly increase the CPU's main frequency. Currently, pipelining technology is also increasingly applied to specific processors, such as SAR real-time processing. The use of pipelining technology can fully utilize the built hardware units, reduce idle, and improve the processing efficiency of the system.  
In the paper [35], the system pipeline is a scalar pipeline, that is, when data are accessed, the data are read from DDR3A and transmitted to the processing engine for processing. When the processed data are written into DDR3B, DDR3A reads the next data in the range direction or azimuth direction for processing. This operation is repeated many times, until all the data are processed. The time-space diagram of the scalar pipeline process during data access is shown in Figure 4. The above data-access process is transferred from DDR3A to DDR3B while DDR3B is read, and after processing, writing to DDR3A is similar. The scalar pipeline is more efficient than the no-pipeline design, but the long data processing time in the SAR imaging system causes the pipeline to generate a waiting or blocking state, which has an effect on real-time system processing.  
The range and azimuth read and write efficiency and bandwidth in the paper [35] are summarized in Table 2.
## 4 Experiment and Result Analysis  
### Dataset  
The proposed architecture was evaluated on the Berkeley deep drive dataset [4], which is proposed for autonomous driving research, regarding rich annotation for drivable areas, traffic objects, lane segments, image-level descriptive labels, and the road type for scene classification. Furthermore, this large dataset covers the varying weather, scenes, and times of the day compared to other datasets that have a relatively small number of samples or tasks. This is important to understand the current autonomous driving environment  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input** & **Operator** & **Exp Size** & **\#out** & **SE** & **Stride** \\ \hline \(224^{2}\times 3\) & conv2d & - & 16 & - & 2 \\ \(112^{2}\times 16\) & bneck, \(3\times 3\) & 16 & 16 & - & 1 \\ \(112^{2}\times 16\) & bneck, \(3\times 3\) & 64 & 24 & - & 2 \\ \(56^{2}\times 24\) & bneck, \(3\times 3\) & 72 & 24 & - & 1 \\ \(56^{2}\times 24\) & bneck, \(5\times 5\) & 72 & 40 & \(\checkmark\) & 2 \\ \(28^{2}\times 40\) & bneck, \(5\times 5\) & 120 & 40 & \(\checkmark\) & 1 \\ \(28^{2}\times 40\) & bneck, \(5\times 5\) & 120 & 40 & \(\checkmark\) & 1 \\ \(28^{2}\times 40\) & bneck, \(3\times 3\) & 240 & 80 & - & 2 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 200 & 80 & - & 1 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 184 & 80 & - & 1 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 184 & 80 & - & 1 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 480 & 112 & \(\checkmark\) & 1 \\ \(14^{2}\times 112\) & bneck, \(3\times 3\) & 672 & 112 & \(\checkmark\) & 1 \\ \(14^{2}\times 112\) & bneck, \(5\times 5\) & 672 & 160 & \(\checkmark\) & 2 \\ \(7^{2}\times 160\) & bneck, \(5\times 5\) & 960 & 160 & \(\checkmark\) & 1 \\ \(7^{2}\times 160\) & bneck, \(5\times 5\) & 960 & 160 & \(\checkmark\) & 1 \\ \(7^{2}\times 160\) & bneck, conv2d, \(1\times 1\) & - & 960 & - & 1 \\ \(7^{2}\times 960\) & bneck, pool, \(7\times 7\) & - & - & - & 1 \\ \(1^{2}\times 960\) & bneck, conv2d \(1\times 1\) NBN & - & 1280 & - & 1 \\ \(1^{2}\times 1280\) & bneck, conv2d \(1\times 1\), NBN & - & k & - & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model structure.  
of vehicles by distinguishing the area of the ego lane and other lanes or backgrounds. There are three classes for the drivable area estimation task: (1) direct; (2) alternative; and (3) background. The ability of the network to classify road types facilitates decisions regarding whether to use the drivable area in different lanes. The other lanes include all pixels that are part of the load, except the ego lane. The BDD dataset consists of 100 K images with a resolution of 1280 \(\times\) 720. We divided this dataset into three categories: 70 K for the training set, 10 K for the validation set, and 20 K for the testing set, following official standard in the training and testing process as done in the literature [2; 3; 4].
## 3 Materials and Methods
### Feature Selection
Filter methods are used as a preprocessing step, independent of the classifier, to reduce data dimensionality by selecting only the most significant features. Filtering methods are known for their simplicity, speed, scalability to high-dimensional data, and efficient computational complexity [37]. Typically, filter methods are divided into two categories: univariate and multivariate [6; 7; 36]. Examples of common filter methods include Mutual Information, which measures the dependency between variables; Pearson Correlation, which evaluates linear relationships between numerical variables; Chi-Square, which assesses the independence between categorical variables; Information Gain, which calculates feature importance based on entropy reduction; Fisher Score, which assesses the ratio of variance between and within classes; and ANOVA, which tests differences in means across groups. A detailed table of filter methods is provided in [6]. The Pearson Correlation method is used for numeric values, whereas the Chi-Square method is used for categorical values. The following subtitles give information about filter methods.
## 3 WSAN System Models
### Modified AODV over IEEE 802.15.4 for Building Temperature Control
#### 4.2.3 Simulation Results
As formerly discussed, there is no background traffic flow on the upper path; therefore, the level of successful path setup and the successful transmission of data packets are increased. An efficient route setup can directly help nodes to conserve their energies. For the CBR rates from 2.50 to 3.25 Mbps as in Figure 11(d), the simulation results are almost the same as the original AODV routing. The same intuitive explanation as discussed before can be applied for these results. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**CBR Rate (Mbps)**} & \multirow{2}{*}{**Number of Packets Sent at the Source**} & \multicolumn{2}{c}{**Number of Packets Received by the**} \\  & & & **Sink** \\ \hline
0.00 to 1.00 & 107 & 107 \\
1.25 & 104 & 104 \\
1.50 & 100 & 100 \\
1.75 & 97 & 97 \\
2.00 & 95 & 95 \\
2.25 & 40 & 37 \\
2.50 & 78 & 73 \\
2.75 & 94 & 91 \\
3.00 & 104 & 103 \\
3.25 & 106 & 106 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of data packets received by the sink (modified AODV). Figure 10: Total energy consumption (modified AODV). Figure 11: Energy consumption at each window interval _vs._ CBR rate. (**a**) Vary the CBR rates from 0 to 0.75 Mbps. (**b**) Vary the CBR rates from 1.00 to 1.5 Mbps. (**c**) Vary the CBR rates from 1.75 to 2.25 Mbps. (**d**) Vary the CBR rates from 2.50 to 3.25 Mbps.
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 5 Experiment Results and Analysis
### Staright Driving Condition  
In this experimental condition, the test vehicle accelerates from 0 to 30 km/h. In the straight driving condition, only the longitudinal state is discussed in this study. The experimental data and estimation results are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbols** & **Value** \\ \hline Vehicle mass & m (kg) & 3000 \\ Length of the vehicle & L (m) & 4.7 \\ Width of the vehicle & B (m) & 2.060 \\ Length of the arm & l (m) & 0.7 \\ Distance between 1 th and 2 nd axis & a (m) & 1.836 \\ Distance between 2 nd and 3 rd axis & b (m) & 1.036 \\ Distance between 2 nd and CM & x (m) & 0.244 \\ Distance between each axis & c (m) & 1.400 \\ Height of CM & H (m) & 0.600 \\ rotational inertia round \(z\)-axis & I\({}_{\mathrm{z}}\) (kg:m\({}^{2}\)) & 6390 \\ Transmission rate & i & 27.13 \\ Longitudinal stiffness of tire & C\({}_{\mathrm{x}}\) (N) & 31300 \\ Lateral stiffness of tire & C\({}_{\mathrm{a}}\) (N) & 40200 \\ Width of the tire & B\({}_{\mathrm{w}}\) (m) & 0.210 \\ Radius of the tire & R (m) & 0.375 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle intrinsic parameters.  
Figure 7: The longitudinal state when a skid-steered wheeled vehicle drives straight.  
In Figure 7, the AN-STUKF (blue line) shows better tracking rate than the traditional UKF (red line) when the vehicle accelerates. After the vehicle longitudinal velocity reaches steady state, the AN-STUKF and the traditional UKF both remain steady near the true state given by RTK-GNSS. The results of this experiment verify the accuracy of AN-STUKF and the strong traceability of the strong tracking algorithm. When the vehicle drives steadily, the UKF and AN-STUKF possess almost similar performance of estimation accuracy and tracking rate.  
Figure 7 also provides the longitudinal slip rate of the two side wheels. The longitudinal slip rate is calculated by (4). When a vehicle begins to accelerate, the longitudinal rate increases. The longitudinal slip rate remains at almost zero when the longitudinal velocity is steady.  
The skid-steered vehicle performs with almost no difference as compared with an Ackerman turned vehicle when it drives straight. In the following section, the feasibility of the proposed algorithm, when the skid-steered vehicle turns, is verified.
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 4 Results and Experimental Validation of the Developed Model
### Increased Battery Size to Avoid Temperature Limits (14s12p)  
To reduce the fuel consumption of the vehicle while keeping the thermal condition of the battery within the optimal range, the battery pack capacity was doubled. Since an increase of the number of parallel cells results in a decrease of the current per cell, this should affect positively the battery thermal behavior. Therefore, the number of parallel cells was gradually increased (using integer numbers only) from 14s6p to obtain a 14s12p configuration. The profile of the different variables over the WLTC cycle is shown in Figure 21.  
The results show (Figure 22) that starting from a 14s12p configuration the battery temperature stays within the optimal range, even in the most aggressive cycle considered in the study. The results for this configuration are 50 \({}^{\circ}\)C, 37 \({}^{\circ}\)C and 61 \({}^{\circ}\)C in the UDDS, NEDC and WLTC cycles, respectively. The fuel consumption and its percentage reduction (in brackets) relative to the conventional vehicle are as follows: 611.4 g (\(-\)12.6%), 544 g (\(-\)8.2%) and 1185 g (\(-\)9.4%), for the UDDS, NEDC and WLTC driving cycles, respectively.  
Figure 21: Simulation results with no thermal limitations for WLTC driving cycle: **(a)** ICE torque, **(b)** EM torque, **(c)** Battery SOC, **(d)** Vehicle speed.
## Appendix A
### Raw Data Ingest  
Figure 14 shows the data ingest flow for an interval-based HTTP polling data ingest process. In this flow, a trigger is fired at periodic intervals (10 min) to begin a data ingest from a specific HTTP API from another cloud platform. The flow begins by authenticating with the API. This may be via API keys, usernames and passwords, or a combination of the two. Once authenticated, a request for data is sent to the API to retrieve the desired data recorded in the prior interval time (10 min). When the data are returned by the API, the endpoint (sensor or API type) is tagged as metadata to the current data flow and pushed on to the payload sanitizing flow.  
In the implementation of the data integration system presented in this paper, this polling type ingest flow requested the data for all sensors of the same type (e.g., the Weather Station devices from Table 2) and handled data from each device sequentially in the subsequent operations.  
For event-triggered or Websocket-like HTTP APIs, Figure 15 shows the corresponding data ingest flow for a HTTP POST endpoint service to handle push data from a configured API service. This flow type suits IoT cloud platforms that allow a system integrator to provide an URL (Uniform Resource Locator)--a unique identifier that is used to locate a resource on the Internet, or an endpoint to push event-based data to an external service. This flow consists of an initial HTTP POST endpoint handler that forwards requests to be validated (i.e., from allowed sources) before tagging the endpoint source, and finally forwarding the received payload to the sanitizer process.  
The final type of data ingest flow is an MQTT subscriber client, which allows the MQTT publish/subscribe protocol to be used to gather data from supporting cloud platforms. This type of data ingest flow is suitable for implementing on a Cloud platform, where MQTT brokers, that facilitate the information exchange among MQTT publishers and subscribers, are running on the Cloud to offload the computational and communication burden of the publishers and subscribers Figure 16 shows the data flow that requires only for an MQTT client to subscribe to the desired topic(s), tag the endpoint for which the data was retrieved from (MQTT device type or cloud platform), and pass the payloads on to the payload sanitizer.  
Figure 14: HTTP interval polling-based data ingest step for device data integration.  
Figure 14: HTTP POST endpoint service-based data ingest step for device data integration.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Transformer Model for Solar Irradiance
In the later steps of the cosine scheduler, the learning rate reaches a maximum from which it then only decreases until the end of the learning process, due to fine-tuning of the error of the model. The input to the model is regional daily data, which contains a combination of past solar irradiance and past weather. Moreover, the model is fed with point solar irradiance data, which are shifted in a sequence during the prediction process of data points, in the sense that the oldest measurement is removed, and the last predicted measurement is lined up at the end of the data timeline.
## 5 Case Studies and Co-Simulation Validation
### Dynamic Response of the Wind Turbine in Weak Grids  
One of the consequences of increasing the level of power electronic converters in a grid is the decrease of its strength, often reflected in its short circuit capacity (SCC). This is a measure for the strength of the system and it is related to the current flowing to a severe fault (i.e., a fault with very low impedance) at a considered bus in the grid. The SCC is calculated as [63]:  
\[SCC=\frac{U_{\text{g}}^{2}}{Z_{\text{g}}} \tag{11}\]  
in which \(U_{\text{g}}\) and \(Z_{\text{g}}\) are the Thevenin equivalent voltage and impedance as seen from the terminal bus, shown in Figure 9. The short circuit ratio (SCR) at the wind terminal is defined as [64]:  
\[SCR=\frac{SCC}{S_{\text{WT}}} \tag{12}\]  
with \(S_{\text{WT}}\) the apparent power of the wind turbine. If the SCR is between 2.0 and 3.0, the grid is considered as weak [65].  
The objective of this study case is to investigate whether there is a difference in the dynamic response of wind turbine to the reference fault in the co-simulation and the monolithic approach in a weak grid. In order to model a weak grid, the external grid with a short circuit power \(S_{\text{k}}=\)100 MVA is disconnected. This way, the SCC of the system at the grid terminal is decreased from 104 MVA to 4.8 MVA. Consequently, the SCR is decreased from 44.2 to 2.04. The results after the reference fault at the grid terminal are shown in Figure 21. Additionally, the effect of including an auxiliary damping signal for eliminating the low-frequency oscillations in the mosaik co-simulation is presented in Figure 21. It can be observed that the wind terminal voltage is supported by the grid-forming control and that the co-simulation responses shows a good correlation with its monolithic counterpart. However, there is an inherent delay involved when interfacing between PowerFactory and other FMUs, which makes the response of controllers slightly delayed. This delay is assumed proportional with the synchronisation time between the FMUs. It can be seen that in the monolithic case, without a PSS, the dynamic responses after fault clearance reach their steady state level faster compared to the co-simulation. Although adding the PSS bears the benefit of oscillation damping, it introduces a delayed response to the systemic  
Figure 20: Dynamic response of the wind turbine to the fault considering different delay time constants \(T_{\text{del}}\) for the monolithic simulation.  
variables. Therefore, the reaction of mosaik with PSS is slower than the co-simulation without PSS. Nevertheless, the voltage reaches its steady state within almost 500 ms and it is in compliance with the defined criteria in Section 4.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 1 Introduction
Therefore, the method of providing analysis results for the complex behavior of the anchorage device in advance can efficiently determine design specifications when designing the anchor head. The purpose of this study is to design a mono-anchor head for anchoring seven-wire 15.2 mm 2360 MPa high-strength strands. In order to design a mono-anchor head, we referred to the anchor head performance standards presented by PTI [11]. In PTI [11], the permanent deflection of the upper surface of the anchor head is adopted as the evaluation criteria, but the shape characteristic of having a wedge hole in the center makes it difficult to measure the permanent deflection of the upper surface of the mono-anchor head. Therefore, it has been transformed into an index that can be evaluated. Then, the mechanical behavior characteristics of the mono-anchor head were analyzed, and the main variables affecting the performance criteria were identified. An analytical study was conducted according to the combination of various design variables, and the analysis results were quantified to evaluate the performance criteria according to the design specifications. In this process, a static load test and a test of the materials used were conducted in advance to construct an analytical model that reflects the actual behavior. A design procedure is presented based on the quantified analysis results, and if this is used when designing a mono-anchor head, design variables can be easily determined without experimentation and analysis.
## 2 Optimal Scheduling Models for AC/DC Distribution Systems
### VSC Constraints  
Each AC subnetwork in the system interacts with the DC ring network through the VSC for power interaction, and in this paper, the VSC current model has been transformed by second-order cone-convex relaxation and linearized by circular constraints [36; 37].  
The power balance across the VSC must be maintained according to Equations (29) and (30):  
\[P_{j,t}^{\text{VSC,AC}}+P_{j,t}^{\text{VSC,DC}}=r_{j}^{\text{VSC}}\widetilde{I }_{j,t}^{\text{VSC}},\forall j\in B^{\text{VSC}},\forall t\in T \tag{29}\]  
\[Q_{j,t}^{\text{VSC,AC}}-Q_{j,t}^{\text{VSC}}=x_{j}^{\text{VSC}}\widetilde{I}_{ j,t}^{\text{VSC}},\forall j\in B^{\text{VSC}},\forall t\in T \tag{30}\]where \(P_{j,t}^{\text{VSCAC}}\) and \(P_{j,t}^{\text{VSC,DC}}\) are the VSC power on the AC and DC sides at node \(j\) at time \(t\); \(\bar{I}_{j,t}^{\text{VSC}}\) is the squared current magnitude of VSC at node \(j\) at time \(t\); \(Q_{j,t}^{\text{VSC,AC}}\) is the reactive power on the AC side at node \(j\) at time \(t\); and \(Q_{j,t}^{\text{VSC}}\) is the reactive power provided by the VSC at node \(j\) at time \(t\).  
The VSC operation must satisfy Equation (31):  
\[\left\{\begin{array}{l}U_{j,t}^{\text{VSC}}=\frac{\sqrt{3}}{3}\mu M_{j,t}^{ \text{VSC}}U_{j,t}^{\text{VSC,DC}}\\ Q^{\text{VSC,min}}\leq Q_{j,t}^{\text{VSC}}\leq Q^{\text{VSC,max}}\\ -S_{j}^{\text{VSC}}\leq P_{j,t}^{\text{VSC,DC}}\leq S_{j}^{\text{VSC}}\\ -S_{j}^{\text{VSC}}\leq Q_{j,t}^{\text{VSC}}\leq S_{j}^{\text{VSC}}\\ -\sqrt{2}S_{j}^{\text{VSC}}\leq P_{j,t}^{\text{VSC,DC}}+Q_{j,t}^{\text{VSC}} \leq\sqrt{2}S_{j}^{\text{VSC}}\\ -\sqrt{2}S_{j}^{\text{VSC}}\leq P_{j,t}^{\text{VSC,DC}}-Q_{j,t}^{\text{VSC}} \leq\sqrt{2}S_{j}^{\text{VSC}}\end{array}\right.,\forall j\in B^{\text{VSC}}, \forall t\in T \tag{31}\]  
where \(U_{j,t}^{\text{VSC}}\) is the equivalent VSC voltage magnitude at node \(j\) at time \(t\); \(U_{j,t}^{\text{VSC,DC}}\) is the voltage magnitude on the DC side at node \(j\) at time \(t\); and \(M_{j}^{\text{VSC}}\) is the modulation index of the VSC at node \(j\) and satisfies \(0\leq M_{j}^{\text{VSC}}\leq 1\).  
In this study, the DC voltage utilization rate \(\mu\) is set to 0.866, in accordance with the standard steady-state VSC modeling widely adopted in the AC/DC distribution system optimization literature [31; 38; 39; 40]. Although advanced PWM techniques such as third-harmonic injection can achieve higher values, such strategies are primarily relevant in converter-level control design and are not typically considered in system-level operational optimization studies. Therefore, for consistency and comparability with existing works, we set \(\mu=0.866\) in our system-level optimization models.