esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Benchmarking the Proposed Methodology with an Existing State-of-the-Art Method  
We compared the best results of the proposed DL-MajMulVot algorithm to those of various prior works in this area. Section 2 of the literature review discusses the studies being compared. Our proposed DL-based MajMulVot approach significantly outperformed the other available techniques.
## 2 Explanation of Variable Pitch Concept
### New VP-Approach  
The new VP-strategy proposed in this paper focuses mainly on improving the performance by widening the range of the largest local effective AoA, instead of enlarging the maximum value. In the new VP-approach, the local effective AoA is kept below the stall angle and maintains a constant value in a wide zone during a rotation cycle; the curve is shown as a solid line in Figure 6. During the design of the new approach, four key points are adequately considered.  
1. How to deal with the zero AoA at the azimuthal angles of 0\({}^{\circ}\) and 180\({}^{\circ}\): Since the positions of the 0\({}^{\circ}\) and 180\({}^{\circ}\) angles are the transformation points between the positive and negative AoAs, the design of the new VP-approach cannot avoid the zero value AoAs. The pitch angles in these two positions remain at the zero value.
2. How to set the value of the constant effective AoA in Figure 6: At the rated TSR, the largest local AoA in a cycle should be designed as the optimum AoA corresponding to the largest lift-drag ratio, rather than the static stall AoA. Thus, if the pitch curve of the new approach is designed for a blade operating at the rated TSR, the constant value in Figure 6 should be equal to the maximum geometric AoA in a FP-VAWT, since both a larger and a smaller value would contribute to a smaller torque. Consequently, the pitch angles are also kept at zero at the two inflection-point locations, namely the azimuthal angles of 90\({}^{\circ}\) and 270\({}^{\circ}\).
3. Is the approach physically and mechanically feasible to implement? The amplitude of the change of the pitch angle is shown in Figure 6 as a dotted curve. In this curve, the pitch angle changes smoothly and continuously, and eliminates the drawback of the design in Figure 5b. The whole variation process of the pitch is illustrated in Figure 7, which is easily accomplished mechanically, such as a specially designed cam. As shown in Figure 7, the pitch angles varies in each 1/4 circle (0\({}^{\circ}\)-90\({}^{\circ}\), 90\({}^{\circ}\)-180\({}^{\circ}\), 180\({}^{\circ}\)-270\({}^{\circ}\), and 270\({}^{\circ}\)-360\({}^{\circ}\)), and returns to zero at four positions (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)). Here, we defined the presence of a positive pitch angle when the blade rotates counterclockwise around its own axis, and the leading edge of the blade points toward the inner side of the circular path. In contrast, the angle is negative when the blade rotates and the leading edge points in the opposite direction.
4. What is the effect of the new VP-approach on the performance of the blade? After the mechanical feasibility of the approach is validated in theory, the following study focuses on the investigation of the effect of the new VP-approach using the DMST model.  
Figure 6: Curve of pitch angle in new approach.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 2 The KMALL Data Format
### MWC Datagrams  
Multibeam Water Column (MWC) datagrams contain information about the water column. They are usually stored in.kmwcd files, although they can also be stored in.kmall files together with the rest of the datagrams (remarkably, MRZ). Each MWC datagram is composed of several elements or data structures, as can be seen in Figure 1.  
We now describe the information contained in each element. The _Header_ contains information such as the MWC datagram size in bytes, the type of datagram (#MWC), the echosounder ID and a time stamp. The _Partition_ element has two fields: number of datagrams and datagram number. They are used if some datagram is bigger than 64 kB and it needs to be split for the transmission. Note that a.kmall file will always have both fields equal to one. In the _CmnPart_, there is information about the transmitter and the receiver such as the number of pings and the swaths per ping. _TxInfo_ contains the number of transmit sectors and the size in bytes of each sector. In _SectorData_, there are as many elements as transmit sectors. Each element contains information about the angle and frequency of each sector. In _RxInfo_, we find information about the beams such as the number of beams and a flag telling if there are high-resolution beam phase data, low-resolution beam phase data or only beam amplitude samples. Finally, _BeamData_ contains the number of samples in the beam and a pointer to the beam samples. Amplitude samples (see Figure 2) are integer values, coded as signed 8-bit samples in our case and with a 0.5 dB precision. Phase samples may be stored as 8-bit integers (low resolution) or 16-bit integers (high resolution). Depending on this, _phaseMode_ can be zero (no phase data), one (8-bit) or two (16-bit).  
Figure 1: MWC datagram as described by [PERSON] (410224 Revision H). Sizes of data elements not to scale.  
After describing the structure of MWC datagrams, it is clear that \(N_{B}\) from Figure 1 is the number of beams contained in RxInfo, and \(N_{S}\) is the number of samples contained in BeamData. We remark that the number of samples per beam changes along the ping, which means that typical image compression algorithms cannot be easily applied to this kind of data. In Figures 2 and 3, we illustrate, respectively, a raw representation of water column amplitude samples and the variability of the number of samples per ping.
## 2 Materials and Methods
### Extract Analyses  
Apart from the determination of the extraction yield, further analysis was performed for the MAE, SLE, and SFE extracts. All the applied methods mentioned below are adequately described in previous work [25].  
In brief, the total phenolic content (TPC) was determined through the Folin-Ciocalteu assay at 765 nm and expressed as the gallic acid equivalent mass of the extract (mg\({}_{\text{GA}}\)/g\({}_{\text{extr}}\)), according to [PERSON] et al. [28]. The total chlorophyll (CHL) and carotenoid (CAR) contents were determined spectrophotometrically at 480, 510, 630, 647, and 664 nm, according to the equations derived from [PERSON] et al. [29; 30] (equations are provided in Appendix A), and expressed in the mass ratio of the corresponding compound to extract (mg/g\({}_{\text{extr}}\)). The antioxidant activity was determined through the DPPH\({}^{\star}\) scavenging assay at 515 nm, according to [PERSON] et al. [31]. The indicator of half-maximal inhibitory concentration was expressed in the mass ratio of the extract to the DPPH free radical (mg\({}_{\text{extr}}\)/mg\({}_{\text{DPPPH}}\)). All the above spectrophotometric assays were performed in a Shimadzu UV-1900 UV-Vis Spectrophotometer (Shimadzu Corporation, Kyoto, Japan) using quartz cuvettes of 1 cm length.  
Finally, selected carotenoids, namely astaxanthin, lutein and \(\beta\)-carotene, were determined through reversed-phase high-performance chromatography (RP-HPLC), according to [PERSON] et al. [32], in a corresponding system consisting of a Jasco PU-1580 HPLC pump (Jasco Inc., Easton, MD, USA), a Jasco LG-1580-04 gradient unit (Jasco Inc., Easton, MD, USA), a Rheodyne 7125 injector (Rheodyne Europe GmbH, Bensheim, Germany) with 20 L loop, a Jones 7955 column chromatography heater (Jones Chromatography Limited, Wales, UK) and a Shimadzu SDP-M20A Diode Array Detector (DAD; Shimadzu Corporation, Kyoto, Japan). The stationary phase was immobilized in a YMC C30 reversed-phase column, 5 \(\upmu\)m, 250 \(\times\) 4.6 mm I.D. (YMC Co., Ltd., Kyoto, Japan). The mobile phase consisted of methanol, MTBE and aq. Phosphoric acid, 1% \(v/v\), the column temperature was maintained at 35 \({}^{\circ}\)C and the flow rate at 1 mL/min. The linear gradient was adjusted according to Table 1. All the injected external carotenoid standards and _C. vulgaris_ extracts were dissolved in ethyl acetate. The particular content (sel. CAR) was expressed in the mass ratio of the selected carotenoids to extract (mg/g\({}_{\text{extr}}\)).
## 3 Optimal Sizing of Distributed Generators (DGs): Formulation, Constraints and Algorithm
### TCO Minimization  
The minimization of the TCO is the first objective function of the optimum design problem. It is calculated based on the initial investment cost \(\mathrm{C}_{\mathrm{inv}}\), the O&M cost \(\mathrm{C}_{\mathrm{O\&M}}\), and the replacement cost \(\mathrm{C}_{\mathrm{Rep}}\) of the DG. In addition, as generated electricity can be used locally, sold to the grid, or stored in the battery, the TCO considers the cost of energy purchasing \(\mathrm{C}_{\mathrm{P-on-P}}\) and selling \(\mathrm{C}_{\mathrm{S-on-P}}\) at peak time, and the cost of energy purchasing \(\mathrm{C}_{\mathrm{P-off-P}}\) and selling \(\mathrm{C}_{\mathrm{S-off-P}}\) at off-peak time. Assuming that the DG power output in each time segment remains constant, the first cost function can be determined, as follows:  
\[\mathrm{Min}\,\mathrm{f}_{1}(\mathrm{x})=\mathrm{Min}\big{(}\mathrm{C}_{ \mathrm{inv}}+\mathrm{C}_{\mathrm{O\&M}}+\mathrm{C}_{\mathrm{Rep}}+\mathrm{C} _{\mathrm{P-on-P}}+\mathrm{C}_{\mathrm{P-off-P}}-\mathrm{C}_{\mathrm{S-on-P}}- \mathrm{C}_{\mathrm{S-off-P}}\big{)} \tag{1}\]  
Figure 3: Hourly solar radiation throughout the year received on a horizontal surface in Brussels, Belgium.  
Figure 2: Time sequence characteristic curve of the total load.  
The initial investment cost of PV \(\rm C_{inv_{PV}}\) is defined in this study as 1200 \(\rm\upxi\)/kWp. Moreover, \(\rm C_{O\&M_{PV}}\) and \(\rm C_{Rep_{PV}}\) are the O&M cost and the replacement cost of PV, respectively, being considered both at 47.44 \(\rm\upxi\)/kWp/year. \(\rm C_{Inv_{ESS}}\), \(\rm C_{O\&M_{BS}}\), and \(\rm C_{Rep_{ESS}}\) are the investment cost, the O&M cost, and the replacement cost of the BESS, respectively; their sum is taken to 350 \(\rm\upxi\)/kWh in this study. Furthermore, the lifetime of PV and battery systems is defined as 20 years and 10 years, respectively [28]. Thus, the first objective function can be determined, as follows:  
\[\begin{split}\rm min\ f_{1}(x)=&\rm min\left[A*N_{Houses}*\frac{(C_{Inv_{PV}}+C_{O\&M_{PV}}+C_{Rep_{PV}})}{20}+B_{Cap}*N_{Houses}\right.\\ &\qquad\qquad\left.*\frac{(C_{Inv_{ESS}}+C_{O\&M_{BS }}+C_{Rep_{ESS}})}{10}+C_{P-on-P}+C_{P-off-P}-C_{S-on-P}-C_{S-off-P}\right] \end{split} \tag{2}\]  
where A, \(\rm N_{Houses}\), and \(\rm B_{Cap}\) are the PV installed capacity (kWp), the number of houses, and the battery installed capacity (kWh), respectively. Note that 692 houses are considered in this study.
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Porosity (\%)** & **Inclination (\({}^{\circ}\))** & **Distance (cm)** & **Height (m)** & **Velocity (m/s)** & **Wind Reduction Ratio (\%)** \\ \hline  & \(-10\) (C) & 30, 60, 90 & 5.000 \(\pm\) 0.000 \({}^{\rm d}\) & 0 \\ \cline{2-5}  & 50 & 30, 60, 90 & 1.336 \(\pm\) 0.209 \({}^{\rm bc}\) & 73 \\ \cline{2-5}  & 100 & 30, 60, 90 & 1.151 \(\pm\) 0.015 \({}^{\rm abc}\) & 77 \\ \cline{2-5}  & 200 & 30, 60, 90 & 0.673 \(\pm\) 0.306 \({}^{\rm a}\) & 87 \\ \cline{2-5}  & 300 & 30, 60, 90 & 0.675 \(\pm\) 0.429 \({}^{\rm a}\) & 87 \\ \cline{2-5}  & 400 & 30, 60, 90 & 0.831 \(\pm\) 0.666 \({}^{\rm ab}\) & 83 \\ \cline{2-5}  & 500 & 30, 60, 90 & 1.224 \(\pm\) 0.466 \({}^{\rm abc}\) & 76 \\ \cline{2-5}  & 600 & 30, 60, 90 & 1.469 \(\pm\) 0.611 \({}^{\rm c}\) & 71 \\ \hline  & \(-10\) (C) & 30, 60, 90 & 5.000 \(\pm\) 0.000 \({}^{\rm c}\) & 0 \\ \cline{2-5}  & 50 & 30, 60, 90 & 1.568 \(\pm\) 0.070 \({}^{\rm b}\) & 69 \\ \cline{2-5}  & 100 & 30, 60, 90 & 1.208 \(\pm\) 0.059 \({}^{\rm ab}\) & 76 \\ \cline{2-5}
50\% & 200 & 30, 60, 90 & 0.678 \(\pm\) 0.316 \({}^{\rm a}\) & 86 \\ \cline{2-5}  & 300 & 30, 60, 90 & 0.768 \(\pm\) 0.316 \({}^{\rm a}\) & 85 \\ \cline{2-5}  & 400 & 30, 60, 90 & 0.773 \(\pm\) 0.372 \({}^{\rm a}\) & 85 \\ \cline{2-5}  & 500 & 30, 60, 90 & 1.036 \(\pm\) 0.618 \({}^{\rm ab}\) & 79 \\ \cline{2-5}  & 600 & 30, 60, 90 & 1.315 \(\pm\) 0.661 \({}^{\rm ab}\) & 74 \\ \hline  & \(-10\) (C) & 30, 60, 90 & 5.000 \(\pm\) 0.000 \({}^{\rm d}\) & 0 \\ \cline{2-5}  & 50 & 30, 60, 90 & 1.735 \(\pm\) 0.614 \({}^{\rm c}\) & 65 \\ \cline{2-5}  & 100 & 30, 60, 90 & 1.395 \(\pm\) 0.160 \({}^{\rm c}\) & 72 \\ \cline{2-5}  & 200 & 30, 60, 90 & 0.710 \(\pm\) 0.339 \({}^{\rm ab}\) & 86 \\ \cline{2-5}  & 300 & 30, 60, 90 & 0.613 \(\pm\) 0.306 \({}^{\rm a}\) & 88 \\ \cline{2-5}  & 400 & 30, 60, 90 & 0.628 \(\pm\) 0.432 \({}^{\rm a}\) & 87 \\ \cline{2-5}  & 500 & 30, 60, 90 & 0.749 \(\pm\) 0.397 \({}^{\rm ab}\) & 85 \\ \cline{2-5}  & 600 & 30, 60, 90 & 1.325 \(\pm\) 0.682 \({}^{\rm bc}\) & 74 \\ \hline \end{tabular} (C) represents the windward control point of wind speed. Wind velocity (m/s) is given with corresponding standard deviation values. Superscripts with different letters for wind velocity denote identical groups of the average wind velocity for each inclination of the wind barrier (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\)) classified using Duncan analysis (significant at \(p\) < 0.05).  
\end{table} 
Table 3: Case C—average wind velocity at 50% porosity and three different inclinations of wind barriers (90\({}^{\circ}\), 80\({}^{\circ}\), and 70\({}^{\circ}\)) for every measuring point.
## 4 Study on the Blast Resistance of Ultra-Wide Concrete Twin-Edge Box Girders Coated with Polyurea  
### Finite-Element Model of Ultra-Wide Concrete Twin-Edge Box Girder  
#### 4.1.1 Project Overview  
Based on the engineering background of the Hunan Road Bridge in Liaocheng, Shandong Province [36], this structure is a suspension bridge consisting of spans measuring 53 m + 112 m + 53 m, featuring twin towers and dual cable planes. The main girder consists of a prestressed concrete twin-box section, with the two box girders unified by internal cantilevers and crossbeams. The main towers feature an H-shaped configuration with solid rectangular cross-sections. A total of 37 pairs of suspension cables are employed across the bridge: 8 pairs for each side span and 21 pairs for the main span. The suspension cables are spaced at regular intervals of 5 m. The concrete has a specified strength grade of C50, while the reinforcing steel is rated at HRB400. The bridge's overall configuration is illustrated in Figure 21.  
The main girder of the Hunan Road Bridge is a prestressed concrete twin-edge box girder, cast in situ using C50 concrete. The half-width girder consists of a single-box, three-cell configuration, symmetrical along the road centerline, with a height of 2.8 m at the centerline. The thicknesses of the top plate, baseplate, and web are 24 cm, 22 cm, and 35 cm, respectively. The main girder is reinforced with transverse and longitudinal prestressing tendons. The tendons are composed of high-strength, low-relaxation steel strands, each comprising 12 wires of 15.2 mm diameter strands, with a standard tensile strength of 1670 MPa. The structural layout of the half-width girder is shown in Figure 22.  
Figure 21: Overall bridge layout (units: m).  
Figure 22: Structural layout of half-width main girder (Unit: cm).
## 2 Background
### Outbound Telemarketing
The synthetic minority oversampling technique (SMOTE) [9] approach was also used to obtain more accurate results. Experimental resultsshowed that the long short-term memory model using SMOTE outperformed other models. [PERSON] et al. [5] proposed an ANN model for bank telemarketing prediction using 16 variables and compared the performance of traditional machine-learning classifiers. They found that, the Type II and Type I errors of their proposed model were higher and lower, respectively, than those of other models. The authors suggested that their proposed approach would be of benefit in decision-making processes in terms of understanding the probability of clients subscribing to term deposits. In comparison, our proposed model showed lower Type II errors compared to other models. We tried to minimize the Type II errors to reduce the frequency of unwanted contacts. Applying the same approach on other real data. Developing self-explanatory decision process systems or algorithms. [PERSON] [10] proposed a method to improve the performance of methods predicting probable paying customers using a stacked deep network. Additionally, [PERSON] applied hybrid sampling to balance the amount of data between categories. [PERSON] et al. [11] presented a mathematical model that can increase the success of telemarketing campaigns under limited currency budgets. They reduced marketing costs by determining the optimal number of calls for each chosen customer. In addition to the studies in Table 1, various architectures and methodologies have been developed to predict telemarketing success [12; 13; 14]. However, despite many related studies on predicting telemarketing success, several limitations remain. First, most studies on predicting telemarketing success use a Portuguese Bank dataset, a public dataset provided by the University of California Irvine. The Portuguese Banks dataset is a well-organized repository containing 45,147 instances with 17 attributes with no missing values. Because many variables used in actual business operations were not considered, variables with a significant effect on telemarketing predictions in practice could not be provided. In other words, many studies have suggested methodologies related to models, but very few studies have constructed datasets that can be applied meaningfully and universally in practice. Second, many studies related to telemarketing success have low performance for practical use, owing to their use of shallow models. In addition, various feature selection methods were applied to solve the data imbalance problem. In the process of feature selection, information loss occurs because data observations and variables are reduced compared to raw data. Third, many studies mentioned above focused more on performance rather than interpreting important variables. However, our study utilizes high-dimensional data consisting of more than 200 input dimensions without feature selection. In addition, we propose a DL model that emphasizes the explanatory power of key variables that influence the success of outbound telemarketing for insurance policy loans.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
###### Abstract
Fog computing paradigm arises to overcome high delay encountered when real time applications need the low latency to access data or things of smart cities. Cloudlets allow the low latency access to data and processing capabilities, which can be accomplished by dynamically building a local virtual machine (VM) near to UEs or goods. A fog computing-based architecture in [8] supports the handoff by the local VM migration. In addition, Mobile Edge Computing (MEC) research is proposed by realizing the Follow-Me Edge (FME) concept [9] to ensure that the service constantly follows the user and that the user is always serviced from the closest edge [9]. It is observed that Follow-Me Cloud (FMC) results do not have the property of the VM migration between cloudlets for the purpose of reducing the transmission latency. In contrast, Follow-Me Edge (FME) results do not keep the advantage of the service migration between data centers (DCs) for reducing the transmission latency. Efforts will be made to propose a follow-me cloud-cloudlet (FMCL) approach to keep the VM migration between cloudlets and service migration between data centers (DCs) to significantly reduce the transmission latency. With the assistance of edge/fog computing, this paper presents a mobility management using a proposed follow-me cloud-cloudlet (FMCL) approach in fog-computing-based radio access networks (Fog-RANs) for smart cites. The proposed follow-me cloud-cloudlet approach is an integration strategy of follow-me cloud and follow-me edge. The new proposed mobility management aims to reduce the total transmission time if some data packets are pre-scheduled and pre-stored into the cache of cloudlet if UE is switching from the previous Fog-RAN to the serving Fog-RAN. The main contributions of the proposed handover scheme with FMCL approach are summarized as follows. * We had proposed follow-me cloud-cloudlet (FMCL) approach, which is a integration strategy of follow-me cloud and follow-me edge to inherit the properties of FMC and FME and explore the cooperation between clouds and cloudlets. * We had proposed a new mobility management with using FMCL approach to reduce the total transmission time, upgrade the throughput, and reduce the probability of the packet loss. This is because that the transmission cooperation between the cloud and the cloudlet, while some packets can be pre-scheduled in the cache of cloudlets to reduce the total transmission time and upgrade the throughput. This is because that some pre-scheduled packets can be directly accessed from local cloudlet, and these pre-scheduled packets are avoided the long network transmission to further improve the probability of the packet loss. The rest of the paper is organized as follows. Section 2 describes the related work and motivation. Section 3 describes the system model, problem formulation and basic idea of our proposed scheme. Section 4 describes our proposed handover protocol with FMCL. Section 5 provides the performance analysis, and the conclusion is finally given in Section 6.