esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The data is provided every 4 s and its use is demonstrated to be beneficial for performance monitoring purposes. As stated by the authors, the dynamic behaviour of the WT is understood, learned, and reproduced by multivariate non-parametric regression models, using data at a sampling frequency of 4 s. The use of 5-min data typically smooths out these dynamics and does not allow for the precision that high-frequency data provides. The recommendations provided by [9] also highlights that 5-min data is not suitable as a standalone solution, but is a good and important complement for designing condition monitoring systems (CMS). Another interesting work [10] compares SCADA data for low and high frequency data, providing evidence for monitoring at higher resolutions in practical scenarios. Therefore, high-frequency data seems to be a good choice to develop CMS and provide more intelligence for wind farm management. To our best knowledge, however, there are no works on investigating the effect of the aggregation of data to higher temporal periods, such as 20-min or 60-min data obtained from the 5-min ones. The interest of such aggregation is basically to simplify models and systems in a way that less information needs to be stored, transmitted, and processed by them. In this paper we explore this effect in depth and demonstrate experimentally that aggregation to longer time periods from 5-min data does not degrade the results but instead tends to generate better normality models when predicting a target variable using the rest of the variables of the system/subsystem and computing the error deviation from the real target variable. The rest of the paper is organised as follows: In Section 2, the approach is briefly presented, describing the characteristics of the SCADA database, the normalisation method, and the details of the ELM technique used for building the models. Section 3 contains the experimental results, while in Section 4 they are discussed and compared to highlight their interest. Finally, Section 5 is dedicated to conclusions and outlining future work.
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 3 Optimization of Real-Time Performance for RISC-V Processors
### Optimization of the DIV Module (Division Module) Based on Data Dependency  
#### 3.3.1 Division Module and Its Working Principle  
In the Rev-riscv, the divider uses the trial division method, which requires 33 clock cycles for each division operation. During these 33 clock cycles, the pipeline is paused. This approach limits the processor's operating speed.  
When performing a division operation, the divider outputs a busy (in calculation) flag signal to the Ex (execute) module. The Ex (execute) module then converts this signal into a pipeline pause signal, thereby suspending the entire processor's pipeline process.  
The trial division method works by shifting and comparing the dividend (or the intermediate value of the dividend minus the divisor) with the divisor. The comparison result (1 or 0) is used as the corresponding bit value of the quotient. The schematic diagram of the divider is shown in Figure 9.  
Figure 8: Branch predictor module working principle diagram.  
Figure 9: Divider working principle diagram.
## 4 Evaluation of Results
### Public Transport Priority
#### 4.2.2 Conditional Priority  
To attempt to enable the functioning of the traffic system in the central part of the City of Zagreb without construction interventions and while maintaining a certain priority of tram lines, changes were introduced in relation to the previously analyzed unconditional priority. At intersections at which large queues were created during the unconditional priority simulation, the weight coefficients in the EPICS software tool were given conditional priority to tram traffic.  
This type of priority of trams is called conditional because trans at these intersections are not given priority passage; rather, priority depends on the ratio of the number of passengers in individual vehicles and trams based on Equation (2).  
The average reduction in travel time in the morning peak period varied from 7.64% to 18.76%. In the afternoon peak period, improvements ranged from 5.60% to 22.50%. It was noticeable that larger reductions in travel time were achieved in the corridors in which trams were separated from individual traffic.  
However, analysis and comparison with \"do-nothing\" scenario in the morning peak period, increased the waiting time of individual vehicles at almost all of the analyzed intersections, i.e., the average waiting time at all analyzed intersections increased by 29.8%, which is an expected consequence of the priority of tram vehicles. However, the conditional priority enabled the flow of private traffic, i.e., there was no blockade of traffic flow in the simulation. In the afternoon peak period, the average waiting time of private transport increased by 34.97%.  
Based on the analysis of the unconditional and conditional priority of tram vehicles, it can be concluded that the unconditional priority would lead to a complete disruption of individual traffic in the central part of the City of Zagreb, and therefore the travel time of trams would increase by 13.03%. Conditional priority enables individual traffic and a reduction in tram travel time but increases the waiting times of individual vehicles by 32.38% on average in the morning and afternoon peak period in comparison to scenario \"do-nothing\". Figure 10 shows summed travel times on all tram lines in morning and afternoon peak periods in the existing state, \"do-nothing\" scenario, unconditional and conditional priority scenarios. Unconditional priority is the worst scenario with increase of travel time by 12.5% in comparison to the \"do-nothing\" scenario. As mentioned in Chapter 4.2.1. this is expected due to the blockade of the whole corridor. On the other hand, conditional priority achieves a reduction in tram travel times of 11.01% compared to the existing state, 14.56% compared to scenario \"do-nothing,\" and 24.05% in comparison to unconditional priority.  
Figure 9: Comparison of average travel time of all trams (s) in scenario (2) and scenario (1).
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
## 3 A Gain for Query Reformulations  
Our initial hypothesis in this work is that: (a) we have one information need expressed with different query reformulations, and (b) the topic \(t\) is unknown. In particular, given an information need \(i\) and its set of reformulations \(V_{i}\), we assume that each reformulation \(q\in V_{i}\) is able to'reveal' different facets of \(i\). Consequently, we need to redefine the expression of the gain of Equation (4) as:  
\[\text{Gain}_{i,q}(D)=\left[1-\prod_{d\in D}(1-\text{Rel}_{i,q}(d))\right] \tag{6}\]  
where \(i\) is the _information need_ and \(q\) is a specific (re)formulation.  
The main difference with the original approach, apart from changing variable names, is the fact that (i) we do not have a'reference' topic \(t\) that describes completely the information  
Figure 1: An example of clusters of subtopics and aspects.  
need \(i\), and (ii) we have one single cluster of query reformulations, or _variants_, \(V_{i}\). For these reasons, we also need an alternative definition of relevance that adapts to our case study:  
\[\text{Rel}_{i,q}(d)=\frac{\sum_{s\in V_{i}}\text{Imp}_{q}(s)\text{Rel}_{s}(d)}{ \sum_{s\in V_{i}}\text{Imp}_{q}(s)} \tag{7}\]  
where the relevance of \(d\), retrieved by the query variant \(q\) of the information need \(i\), is computed as the weighted average of the relevance of \(d\) with respect to all the alternative reformulations in \(V_{i}\). The two terms \(\text{Imp}_{q}(s)\) e \(\text{Rel}_{s}(d)\) remain unaltered compared to the previous definitions:  
\[\text{Imp}_{q}(s)=\sum_{d\in D_{i}^{N}\cap D_{q}^{N}}\frac{1}{\text{Rank}_{q }(d)}\quad,\quad\text{Rel}_{s}(d)=\frac{1}{\sqrt{\text{Rank}_{s}(d)}}\]
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 5 Conclusions, Policy Implications, and Recommendations
We recommend that governments in Africa focus and increase funding investment in ICTs to foster increased agricultural production while addressing structural challenges that can increase access to digital agricultural technologies. Agricultural extension and advisory services should harness the potential of digital technologies to bridge the deficit in the agricultural extension to farmer ratio. Additional efforts are required by extension agents to build the capacity of smallholder farmers in the rural spaces to be adept with information and communication technologies. It might be useful if governments in Africa incentivize the telecommunication companies that extend coverage to rural areas with tax rebates and holidays to encourage rural inclusion in the digital space to close the digital divide eventually.
The ability to monitor the suitability of water for a particular use requires determining the quality of groundwater. Anthropogenic activities have the potential to alter the relative contributions of natural sources to variability, and cause introduce pollution effects [18; 21; 39]. Periodic groundwater analysis is a typical approach to determine seawater intrusion into aquifers in coastal areas [40; 41]. There has been no scientific publication regarding groundwater quality in the present study area. In this work, a comprehensive study was conducted to evaluate the geochemical processes that regulate groundwater quality [9]. This study suggests that the analysis of groundwater quality based on various attributes can reveal the hydrogeochemical characteristics and the need for the regulation of water quality factors in this coastal region. The objective of the present research work is to understand the geochemistry of groundwater in a part of the Ramanathapuram district in Tamil Nadu, India. The main objectives are (i) to study the physico-chemical parameters and spatial variations of cations and anions, (ii) to determine the drinking and irrigation quality of groundwater, (iii) to identify the geogenic processes that degrade groundwater, and (iv) to study the intrusion of seawater into the coastal aquifer.
## 2 Related Work
### NFV and SDN  
Although the concept of NS is still nascent, NFV and SDN paradigms for achieving NS have been maturely researched on concrete solutions and readily available platforms [16; 17; 18]. By logically partitioning physical network resources in an efficient manner, NFV resolves many problems facing NS. For example, NFV can tailor the slice for a specific scenario and assign specific network functions to each slice flexibly [19], constructing dynamic and service-aware networks with lower operating and capital expenses [20]. As for SDN, [PERSON] et al. [21] design a slicing tool, FlowVisor, which is used to achieve slicing and flow isolation. [PERSON] et al. [22] propose the use of the SDN paradigm at the tenant sets with common switches and tackle particular problems of this kind of networks, such as scalability and high bandwidth requirements. Flexible and efficient slicing is well supported by NFV and SDN; however, less research has been done on optimizing latency. As highlighted in [11], existing SDN systems can reason only about bandwidth and/or the number of hops in the network, without the possibility to build routing strategies with regard to latency parameters.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 1 Introduction
### Origin of Infrastructure Sharing  
Telecommunication infrastructure sharing ideas and proposals for various approaches began in Europe in the 2000s when the Universal Mobile Telecommunications System (UMTS) was granted licenses. Academia and industry proffered useful ideas and directed their focus on infrastructure sharing, and because of this, the 3 rd Generation Partnership Project (3 GPP) collaborated with other telecommunication associations to produce a technical report on different standards for sharing [5]. In 2001 the Telecommunications Industry Association (TIA) in Europe published a report on shared 3G network infrastructure, while in 2009 the North Stream produced a report that analyzed the competitive and practical effects of network sharing, and the technical sharing structure was geared towards mobile network operators (MNOs) and mobile virtual operators with service level agreements [5]. Countries like the United Kingdom and Sweden have witnessed the structural separation between infrastructure and service provisioning whereby fixed line operators can leverage infrastructure sharing to optimize the use of their networks [6]. In the UK most of the infrastructures are shared to reduce the number of sites and masts [4; 6]. This reduces site acquisition times for new entrants, provides easy access to places of strategic importance where spaces for new sites are limited, reduces capital and operating expenditure and reduces environmental health issues and visual impact [5; 6]. It also helps to increase service coverage. The position of telecommunication infrastructure in strengthening economic growth is focused on by economists [5; 6; 7]. Economists argue that the development of a nation to its full potential in the contemporary world cannot be achieved without a modern telecommunication infrastructure in place [8; 9]. As such, the development of telecommunication infrastructures significantly enhances economic growth and development. Studies have shown that there is a strong relationship between telecommunication infrastructure development and economic growth and a strong correlation between mobile phone penetration and economic growth [4]. Though telecommunication infrastructure sharing as a concept and practice is well established in Europe, America and Asia, it is new in Africa, and currently many operators are not willing to share infrastructure. Hence, there are no comprehensive reports on the environmental and social profitability of this model in Africa. However, it has proved successful in other international markets, and hence it will be appropriate to test its practicability and profitability in an African context. However, the African telecommunication market is somewhat unique, and hence presents peculiar challenges that other markets do not. The focus of this study was Nigeria.
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 3 1 \(\times\) 2 Array Antenna
### Measured Results and Discussion  
As is apparent from Figure 7, the array antenna is constructed on a flexible polyimide substrate. The Anritsu S820E VNA (Atsugi, Japan), which operates in the frequency range of 1 MHz to 40 GHz, is used to measure the scattering parameters of the suggested antenna. Figure 8 shows the reflection coefficient between the simulated and evaluated findings of  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Parameter** & **Ws** & **Ls** & **g** & **z1** & **z2** & **z3** & **z4** & **z5** & **z6** & **z7** & **z8** \\ \hline Value (in mm) & 50 & 26 & 30 & 8 & 29 & 0.55 & 6 & 0.8 & 10 & 6.4 & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dimensions of the \(1\times 2\) array antenna.  
Figure 5: The configuration of the \(1\times 2\) array antenna.  
Figure 6: Reflection coefficient comparison between the array and single-element antenna.  
the suggested array antenna. The array antenna provides a dual-band response with a 3.59-3.83 GHz to 5.68-6.01 GHz operating range.  
The radiation characteristic evaluation of the array antenna using the anechoic chamber is given in Figure 9. A double-ridged horn antenna (18 GHz) is utilized as a reference antenna while the fabricated array antenna is used as the AUT (antenna under test). The 2D radiation patterns (co- and cross-polarization) at the major E and H planes at the resonant frequencies 3.75 and 5.76 are given in Figure 10.  
Figure 8: Reflection coefficients of the array antenna.  
Figure 7: Array antenna fabricated prototype.  
Figure 9: Array antenna radiation pattern measurement.  
The proposed array antenna exhibits bidirectional patterns in the E-plane and quasi-omnidirectional patterns in the H-plane, as portrayed in the figure. Figure 11 displays the gain as well as radiation efficiency of the array antenna. The array antenna exhibits a gain and efficiency of 4.8 dBi/91% and 7 dBi/90%, respectively, for the first and the second operating bandwidth. The gain graphs in Figure 11 demonstrate that the array antenna's gain outperforms the single-element antenna.
## 3 Results
### Multi-Sensor FVI Quality (Using Real Image)  
Different types of image and feature fusion exist like multi-sensor, multi-resolution, multi-temporal, multi-angular, and model-based feature fusion [51]. Image fusion can be applied both to data collected from different sensors (inter-sensor), or from the same sensor (intra-sensor), as well as to data collected at the same time under the same conditions (single-dated) or at different dates (multi-date) [26]. We have already found a single-sensor fusion result in Table 5; our result of multi-sensor fusion are calculated and listed in Table 6.  
For GF1 (PAN) to GF4 (MS) fusion with SRR 1:6, in ARVI objective quality (CC, RMSE) is 84.27%, 33.97% for GS fusion which is better (higher CC and lower RMSE) than other fusion methods. For EVI, GNDVI, NDVI, OSAVI and SAVI, Ehlers fusion has higher CC but GS has lower RMSE but the value of CC for GS is very close to the Ehlers method. The result for GS fusion has CC (89.57 to 92.56%), RMSE (21.35 to 29.56%) for SRR 1:8, CC (79.46 to 89.93%), RMSE (22.86 to 33.74%) for SRR 1:10 and CC (88.24 to 91.75%), RMSE (24.86 to 34.67%) for SRR 1:12. Similarly, the detailed result of GF1 (PAN)+GF4 (MS), GF2 (PAN)+Landsat-8 OLI (MS), GF4 (PAN)+MODIS (MS) for GS, Ehlers, PC, MIHS and HPF algorithms for FVI is listed in Table 6. In multi-sensor fusion, most of the VIs in SRR 1:6 (GF1+GF4) and 1:10 (GF4+MODIS), the quality of FVI result is inconsistent with fusion algorithms.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 3 System Architecture  
The proposed adaptive IMM filter method in this study is applied to evaluate the GPS/INS combined data. These independent dynamic equations, including process noises of various sorts, are performed synchronously. Meanwhile, the covariance of measurement noise on the basis of residual information method is recursively applied. Aiming to curb the uncertainty process noise and measurement noise synchronously, the AIMM filter is introduced.
# Laser Scanning versus Wide-Field--Choosing the Appropriate Microscope in Life Sciences
## 4 Comparison of Wide-Field and Laser Scanning Microscopy
Conventional wide-field or spinning disk microscopy are preferential methods for high-speed experiments. If super-resolution is required, authors should consider the corresponding wide-field (SIM, single-molecule localization) or laser scanning (e.g., Airy scan or STED) methods, taking into consideration their relevant light exposures. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Resolution** & **Resolution** & **Typ. Rec. Time,** & **No. of Images at W/A** \\  & **Lateral/Axial** & **(A\({}_{\text{N}}\) = 1.40)** & **Lateral/Axial** & **No. of Raw Images** & \(\leq\) 10 J/cm\({}^{2}\)** \\  & **(A\({}_{\text{N}}\) = 1.40)** & **(A\({}_{\text{N}}\) = 0.50)** & **(Per Image)** & **[**27**]** \\ \hline \multicolumn{4}{c}{**Wide-field**} \\ \hline Conventional & \(\Delta\)x \(\leq\) 200 nm & \(\Delta\)x \(\leq\) 500 nm & \(\Delta\)t = 0.01–1 s & \\  & \(\Delta\)z not defined & \(\Delta\)z not defined & n = 1 & N \(\geq\) 100 \\ \hline OS-SIM & \(\Delta\)x \(\leq\) 200 nm & \(\Delta\)x \(\leq\) 500 nm & \(\Delta\)t = 0.1–5 s & \\  & \(\Delta\)z \(\sim\) 300 nm & \(\Delta\)z \(\sim\) 2.4 \(\upmu\)m & n \(\geq\) 3 & N = 30–40 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary on wide-field and laser scanning techniques, including lateral (\(\Delta\)x) and axial (\(\Delta\)z) resolution for high or moderate numerical aperture \(A_{\text{N}}\) (used for single-cell monolayers or 3D Scheme 10. J/cm\({}^{2}\)) [27].
## 4 Discussion
### Forecasting Electrical Characteristics of Flexible Sensing Materials by Regression Modelling  
This work employed four distinct models, RSM, RF, LSTM, and HKOA-LSTM, to forecast the electrical properties of nanocomposites. These four models were selected for their capacity to manage nonlinear data, generalise pattern recognition, and enhance prediction accuracy. A proportion of 75% of the dataset acquired from the experiment was allocated for training, while 25% was designated for testing. Identifying the optimal hyperparameter setup is essential throughout the training phase. In neural networks, hyperparameters are predetermined values chosen before model execution, significantly influencing performance.  
The subsequent sections will provide a detailed discussion of each of the four forecasting models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multicolumn{4}{c}{**Level Factor**} \\  & **Fe\({}_{3}\)O\({}_{4}\) (g)** & **CNT (g)** & **MLG (g)** & **MT (\({}^{\circ}\)C)** \\ \hline Low (\(-1\)) & 0.4 & 0.8 & 0.7 & 40 \\ Central (0) & 0.6 & 1 & 0.9 & 60 \\ High (1) & 0.8 & 1.2 & 1.1 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Levels and coding of four factors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{4}{c}{**Influencing Factors**} \\  & **Fe\({}_{3}\)O\({}_{4}\)** & **CNT** & **MLG** & **MT** & **Hysteresis (\%)** & **Sensitivity (kPa\({}^{-1}\))** \\ \hline
1 & 1 & 1 & 0 & 0 & 5.68 & 0.982 \\
2 & 0 & 1 & \(-1\) & 0 & 5.556 & 0.848 \\
3 & 0 & \(-1\) & 0 & 1 & 11.654 & 0.614 \\
4 & \(-1\) & 0 & 0 & 1 & 11.594 & 0.904 \\
5 * & 0 & 0 & 0 & 0 & 6.165 & 0.985 \\
6 & 1 & \(-1\) & 0 & 0 & 6.131 & 0.761 \\
7 * & 0 & 0 & 0 & 0 & 5.551 & 0.978 \\
8 & 1 & 0 & 0 & 1 & 9.541 & 0.838 \\
9 & \(-1\) & 0 & 1 & 0 & 7.343 & 1 \\
10 & \(-1\) & 0 & 0 & \(-1\) & 7.63 & 0.829 \\
11 & \(-1\) & 0 & \(-1\) & 0 & 9.23 & 0.742 \\
12 & 0 & 0 & 1 & \(-1\) & 8.874 & 0.879 \\
13 * & 0 & 0 & 0 & 0 & 5.683 & 0.922 \\
14 & 0 & 1 & 0 & 1 & 12.623 & 0.852 \\
15 & 1 & 0 & \(-1\) & 0 & 5.211 & 1.077 \\
16 & 1 & 0 & 1 & 0 & 6.621 & 0.865 \\
17 & 0 & \(-1\) & 0 & \(-1\) & 8.31 & 0.507 \\
18 & 0 & 1 & 0 & \(-1\) & 6.356 & 0.876 \\
19 & \(-1\) & 1 & 0 & 0 & 6.854 & 0.926 \\
20 * & 0 & 0 & 0 & 0 & 6.34 & 0.927 \\
21 & 0 & 0 & \(-1\) & -1 & 5.7 & 0.764 \\
22 & 1 & 0 & 0 & \(-1\) & 5.015 & 1.059 \\
23 & 0 & \(-1\) & \(-1\) & 0 & 9.526 & 0.631 \\
24 * & 0 & 0 & 0 & 0 & 6.111 & 0.891 \\
25 & \(-1\) & \(-1\) & 0 & 0 & 7.884 & 0.663 \\
26 & 0 & 1 & 1 & 0 & 9.1 & 0.889 \\
27 & 0 & \(-1\) & 1 & 0 & 6.188 & 0.628 \\
28 & 0 & 0 & \(-1\) & 1 & 13.536 & 0.799 \\
29 & 0 & 0 & 1 & 1 & 11.481 & 0.856 \\ \hline \hline \end{tabular}
\end{table}
Table 9: A multifactorial experimental program was developed using the BBD approach.