esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It is possible to apply ray-tracing-based algorithms to selectively assign more CBFs in those parts of the geometry more prone to having a stronger contribution to the final radiated field [28], or discard those parts of the geometry with negligible contributions [29]. Domain decomposition (DD) [30] approaches offer an additional strategy for the reduction of the computational burden imposed by large problems that consist of the separation of different domains and analyze each domain separately, after which it is necessary to transfer information related to the results computed in an iterative fashion. This work describes a full-wave analysis technique focused on the analysis of complex multilayered radomes containing FSS, by means of a dual domain decomposition scheme where the antenna and radome domains are analyzed using a combination of the CBFM and the MLFMA. Special considerations are taken into account for fast generation of the CBFs, including the option of limiting near and far field interactions between CBFs and neighboring blocks, as well as introducing a sparse approximate inverse (SPAI) preconditioner specifically tailored to the CBFM [31]. The authors in [32] described a numerical technique focused on the analysis of radomes considering the MoM for the analysis of the antenna, and introducing macro-basis functions to describe the current distribution on the radome. In the present work, we generalize this technique including domain decomposition with two non-overlapping domains, one defined by the antenna analyzed using MLFMM with subdomains and the other the radome (including FSS) analyzed using MLFMM with CBFs. In addition, in the present work, we introduce a preconditioner for each domain problem. In the example included, we analyze the effect of the coupling between all the CBFs of the radome domain and considering only the coupling between neighboring CBFs. This paper is organized as follows. Section 2 addresses the fundamentals of the developed approach. A test case including a complex radome is described and simulated in Section 3. Some conclusions and thoughts derived from the work presented in this document are finally presented in Section 4.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results
#### 6.2.2 Number of Parent Change  
Figure 6 depicts the number of parent changes over time. The parent change values indicate network stability. If the network has fewer parent changes during the simulation, it can have more stability and also the chance of packet losses is very low. The parent change values of EDADA-RPL, LA-RPL, and RPL for a 1-h simulation are 0.07, 0.10, and 0.20, respectively. The proposed protocol is introduced the compressed sensing theory to aggregate the data from its child node. It is a hybrid aggregation technique, which reduces the data losses from the source to the destination. Thus, it reduces the number of redundant data transmission and path breakages. So, it maintains route stability during the data transmission.  
Figure 5: Average end-to-end delay vs. number of hops.
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## Abbreviations  
NR No reference  
TLLFD Two low-level feature distributions  
JP2K JPEG2000  
WN White Noise  
GB Gaussian Blur  
FF Fast Fading  
IQA Image quality assessment  
FR Full Reference  
RR Reduced-Reference  
HVS Human Vision System  
CNN Convolutional Neural Network  
BLS Broad Learning System  
SIFT Scale Invariant Feature Transform  
LBP Local Binary Patterns  
HOG Histogram of Oriented Gradient  
SVM Support Vector Machine  
RF Random Forest  
LR Logistic Regression  
SVR Support Vector Regression  
SSIM Structural SIMilarity  
FSIM Feature SIMilarity  
GMSD Gradient Magnitude Similarity Deviation  
CLBP Completed LBP  
DMOS Difference Mean Opinion Score  
SROCC Spearman Rank Order Correlation Coefficient  
KROCC Kendall Rank Order Correlation Coefficient  
PLCC Pearson Linear Correlation Coefficient  
RMSE Root mean Squared Error  
MOS Mean Opinion Scores  
NIQE Natural Image Quality Evaluator  
ILNIQE Integrated Local NIQE  
BIQ Blind Image Quality Indices  
DIIVINE Distortion Identification-based Image Verity and INtegrity Evaluation  
BLIINDS2 Blind Image Integrity Notator Using DCT Statistics  
BRISQUE Blind/Referenceless Image Spatial QUality Evaluator  
GMLOG Gradient Magnitude Map and the Laplacian Of Gaussian  
NFER
## 2 Experiment Setup
### Vibration Measurement Working Process  
Figure 4 shows the working process of the vibration analyzer system. The sleeve supporting the rotor shaft is used as the installation point for the acceleration transducer. Three single-axis acceleration transducers are installed in the positive direction of the X, Y, and Z axes. The three acceleration transducers are uniformly installed on the outer edge of the shaft sleeve. The acceleration transducers are connected to the dynamic signal analyzer through a customized cable. The dynamic signal analyzer is connected to the computer via a USB communication line. The Hall-effect speed sensor is installed on the fuselage and connected to the R7008 SB receiver through a specific data line. The motor is mounted on the fuselage and connected to the ESC through a special data line. The ESC is powered by the battery to transmit data to the R7008 SB receiver. The pitch is measured by the electronic pitch gauge and fixed by the hub bolts. The throttle lever of FUTABA T14 SG is adjusted to control the motor speed. FUTABA T14 SG transmits the signal to the R7008 SB receiver, and  
Figure 3: Rotor test stand.  
Figure 2: Local view of the belt transmission, Hall-effect speed sensor, and acceleration sensor.  
the R7008 SB receiver transmits to the ESC. The ESC controls and adjusts the motor speed, and the motor drives the rotor to rotate.  
The acquisition frequency of the dynamic signal analyzer is set to \(0\,\sim 200\) Hz. The rotor speed is measured by the Hall speed sensor in order to adjust the stable rotor speed. The Hall speed sensor transmits the signal to the R7008 SB receiver, and the R70-08 SB receiver transmits the signal to the FUTABA T14 SG, and the speed is displayed on the FUTABA T14 SG interface. According to the displayed speed, the throttle stick is adjusted to achieve a stable rotor speed. The acceleration data are transmitted to the data acquisition and analyzer instrument, and the data analyzer transmits the signal to the computer, and the acceleration is displayed on the computer screen. After the rotor speed is stable, the acceleration data are collected and stored. The detailed connection diagram can be seen in Figure 5.  
Figure 4: Vibration measurement flow diagram.  
Figure 5: Connection diagram of vibration measurement system.
# The Attachment of Carbon Nanotubes to Atomic Force Microscopy Tips Using the Pick-Up Method
## 3 Results
### Techniques to Determine CNT Attachment to AFM Tips
#### 3.1.2. AVD Curves  
Figure 2 shows a typical AVD curve when a CNT has been attached to an AFM tip. The figure shows sections A, B and C of the AVD curve. Section A shows where the AFM tip approaches the sample surface but has yet to contact it, therefore, the amplitude of cantilever oscillation is approximately zero and the curve is flat. Section B is where the tip begins to tap the surface and as the surface moves closer to the tip the amplitude of oscillation decreases steadily. Once the tapping force reaches a critical value the CNT begins to bend or buckle and the cantilever oscillation increases again and this is highlighted in section C. The amplitude and duration of the CNT buckling signature depends on a number of factors including the diameter of the CNT and the attached length [62; 63; 64; 65]. This mode of operation is in-built in the majority of commercial AFMs and is relatively easy to implement with the primary drawback being that in the process of acquiring AVD curves the CNT can be dislodged if it is not securely attached. AVD curves have also been used to study experimentally and theoretically the compression, bending and overall mechanical behaviour of CNTs.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
## 4 The Multi-Modal Minimal Scheduling Function
### TSCH Mode Abstraction
The TSCH mode abstraction uses a 3-bit representation, as we use the same (currently reserved) protocol bits used by [PERSON] et al. [16], i.e., the three MSBs of the CellOptions field contained in certain 6P requests (see Section 4.5) and the three MSBs of the Link Options fields (one per minimal supercell) contained in TSCH EBs. Since the amount of information one can encode with 3 bits is limited, we made (reasonable) assumptions about the hardware capabilities of an average 6 TiSCH node. More specifically, we assumed all devices to have at most two interfaces (which operate in different frequency ranges or the communication modes of which are otherwise orthogonal between them), each supporting at most two unique communication modes. In addition, all network devices must either own the same set of interface-modes or otherwise be aware of the superset of interface-modes that exist throughout the network (presumably through some mechanism that is beyond the scope of this paper). Barring two special values, the 3-bit TSCH mode encoding follows an _m.ii_ dot notation format (MSB-first), where _ii_ denotes the index of an interface in an ordered list of interfaces known to all nodes and \(m\) denotes the index of the communication mode in one of two ordered lists (i.e., the one corresponding to the interface with index _ii_), both of which are known to all nodes. Table 2 lists all TSCH mode encodings.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 2 Theoretical Method for Determining Prestress Loss of BFRP Tendons
### Prestress Loss Due to Concrete Shrinkage and Creep  
The concrete shrinkage refers to the volume change caused by concrete hardening and it has nothing to do with the load on the pavement structure, while the concrete creep refers to the phenomenon that the strain or deformation of the concrete structure increases with time when the structure is subjected to a long-term load. Both of the concrete shrinkage and concrete creep result in the increase in concrete deformation, which causes redistribution of stress in the reinforced concrete section, and therefore theprestress loss is produced. The calculation of the prestress loss caused due to concrete shrinkage and creep is defined in Equation (6) [25].  
\[\sigma_{\text{I5}}=\frac{55+300\frac{\sigma_{\text{pc}}}{f^{\prime}_{\text{cu}}} }{1+15\rho}\cdot\frac{E_{\text{fp}}}{E_{\text{p}}} \tag{6}\]  
where \(\sigma_{\text{pc}}\) is the normal compressive stress of the concrete at the resultant point of prestressed tendons; \(E_{\text{fp}}\) and \(E_{\text{p}}\) are the elastic modulus of the prestressed BFRP tendons and the steel strands, respectively; \(f^{\prime}_{\text{cu}}\) is the compressive strength of the concrete; and \(\rho\) is the reinforcement ratio of BFRP tendons.  
Given a specific physical project, material properties of the pre-stressed BFRP tendons and design parameters of the cross-tensioned pavement can be obtained, and therefore the corresponding pre-stress loss of BFRP tendons can be calculated with the proposed methods, as shown in Equations (1)-(6). With the accurate predicted pre-stress loss, an appropriate design of BFRP tendons can be achieved to avoid both excessive and insufficient pre-stress, which can result in invert arch and premature cracking in cross-tensioned concrete pavement, respectively.
## 2 Methods
### Selection of Vegetation Indices (VIs)
The EC flux datasets were downloaded from the FluxNet project ([[http://daac.ornl.gov/FLUXNET/fluxnet.shtml](http://daac.ornl.gov/FLUXNET/fluxnet.shtml)]([http://daac.ornl.gov/FLUXNET/fluxnet.shtml](http://daac.ornl.gov/FLUXNET/fluxnet.shtml))) for the JP-Mase site, from AmeriFlux ([[http://public.ornl.gov/ameriflux](http://public.ornl.gov/ameriflux)]([http://public.ornl.gov/ameriflux](http://public.ornl.gov/ameriflux))) for the US-Dk1 site, from FluxNet Canada ([[http://www.fluxnet-canada.ca](http://www.fluxnet-canada.ca)]([http://www.fluxnet-canada.ca](http://www.fluxnet-canada.ca))) for the CA-Let site, and from AsiaFlux ([[http://asiaflux.yonsei.kr/](http://asiaflux.yonsei.kr/)]([http://asiaflux.yonsei.kr/](http://asiaflux.yonsei.kr/))) for the JP-TMK and TH-SKR sites. For the JP-FJY site, original measurement data were downloaded from the Forestry and Forest Products Research Institute (FFPRI) [63]. Additionally, we used the level-2 product or original measurement data of AmeriFlux data to reduce errors that may arise in the gap-filling process. The downloaded data were the same parameters as those used for the canopy scale for JP-TKY. The time interval of the data was 30 min. All data for the year 2003 were used. To examine relationships between VIs and Pmax_capacity, 8-day composited level-3 global reflectance data from TERRA/MODIS (MOD09A1.005) with 500-m resolution were used. The datasets were downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC; [[http://daac.ornl.gov/MODIS/](http://daac.ornl.gov/MODIS/)]([http://daac.ornl.gov/MODIS/](http://daac.ornl.gov/MODIS/))), which provides subsets of MODIS Land Products for any tower site in the global FluxNet network by area (from one pixel up to 201 \(\times\) 201 km) and time period. From the subsets, one center pixel corresponding to the flux tower location was used.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 1 Introduction
large number of slave nodes (sensors and actuators) that take part in bidirectional communication with the controller and are spatially distributed over the entire site. This paper presents cost, scalability, latency, reliability, range and energy consumption evaluation of wireless technologies with promising range and latency potential, including LoRa, IEEE 802.11 ah (Wi-Fi HaLow), Narrowband-IoT (NB-IoT), WirelessHART, ISA100.11a, Bluetooth Low Energy (BLE) and 802.15.4g physical layer with 802.15.4e TSCH on data-link layer. These technologies offer the possibility of a dense heterogeneous wireless network deployment able to serve both actuators and sensors in critical applications, as well as provide an infrastructure for supervisory traffic. Along with the practical limitations of each technology with respect to the existing trade-offs between latency, throughput, coverage and scalability, a direct projection of the aforementioned wireless technologies to their key performance indicators is made, aiming to enable adequate network design in particular industrial applications. The remainder of this article is organized as follows. The requirements and challenges that industrial networks must comply with are summarized in Section 2. Section 3 presents a general discussion of the key trade-offs in wireless network design in the context of the requirements, while in Section 4 the discussed trade-offs are quantified for each particular technology. Overall discussion and the experimental evaluation of energy consumption is presented in Section 5. Finally, Section 6 presents the conclusions.
## 5 Analysis of the Results
### Body Shadowing Component
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**868 MHz NB Channel**} \\ \hline
**MN LOCATION** & **SCENARIO** & **LOS/NLOS Conditions** & \(\mu\) **(dB)** & \(\sigma\) **(dB)** & \(\chi^{2}\) & \(r\) \\ \hline \multirow{4}{*}{HE\({}_{\rm R}\)} & APR & LOS & \(-0.3\) & 1.8 & 8.91 & 0.84 \\  & APR & NLOS & \(-0.4\) & 2.7 & 13.4 & 0.92 \\ \cline{2-7}  & \multirow{2}{*}{DEP} & LOS & \(-0.2\) & 1.3 & 2.94 & 0.96 \\  & & NLOS & \(-0.2\) & 1.6 & 6.92 & 0.98 \\ \hline \multirow{4}{*}{TO\({}_{\rm F}\)} & APR & LOS & \(-0.1\) & 0.8 & 0.78 & 0.37 \\  & APR & NLOS & \(-0.2\) & 1.9 & 15.4 & 0.70 \\ \cline{2-7}  & \multirow{2}{*}{DEP} & LOS & \(-0.3\) & 2.0 & 7.01 & 0.96 \\  & & NLOS & \(-0.2\) & 1.6 & 7.04 & 0.89 \\ \hline \multirow{4}{*}{AB\({}_{\rm L}\)} & APR & LOS & \(-0.2\) & 1.2 & 0.34 & 0.96 \\  & & NLOS & \(-0.3\) & 1.9 & 6.92 & 0.99 \\ \cline{2-7}  & \multirow{2}{*}{DEP} & LOS & \(-0.3\) & 1.8 & 7.35 & 0.97 \\  & & NLOS & \(-0.2\) & 1.5 & 4.59 & 0.97 \\ \hline \multicolumn{7}{c}{**6489 MHz UWB Channel**} \\ \hline
**MN LOCATION** & **SCENARIO** & **LOS/NLOS Conditions** & \(\mu\) **(dB)** & \(\sigma\) **(dB)** & \(\chi^{2}\) & \(r\) \\ \hline \multirow{4}{*}{HE\({}_{\rm R}\)} & APR & LOS & \(-0.2\) & 1.2 & 3.74 & 0.98 \\  & APR & NLOS & \(-0.2\) & 1.8 & 8.97 & 0.99 \\ \cline{2-7}  & \multirow{2}{*}{DEP} & LOS & \(-0.3\) & 2.1 & 18.88 & 0.96 \\  & & NLOS & \(-0.3\) & 2.2 & 17.59 & 0.91 \\ \hline \multirow{4}{*}{TO\({}_{\rm F}\)} & APR & LOS & \(-0.2\) & 1.3 & 5.20 & 0.67 \\  & APR & NLOS & \(-0.2\) & 1.7 & 10.74 & 0.74 \\ \cline{2-7}  & \multirow{2}{*}{DEP} & LOS & \(-0.2\) & 1.7 & 4.79 & 0.94 \\  & & NLOS & \(-0.3\) & 2.6 & 40.34 & 0.97 \\ \hline \multirow{4}{*}{AB\({}_{\rm L}\)} & APR & LOS & \(-0.3\) & 2.2 & 10.11 & 0.94 \\  & & NLOS & \(-0.3\) & 2.8 & 21.84 & 0.99 \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{DEP} & LOS & \(-0.3\) & 2.1 & 12.26 & 0.95 \\ \cline{1-1}  & & NLOS & \(-0.3\) & 2.8 & 20.34 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameters of lognormal PDF fitted to the empirical large-scale fading distributions.
## 4 Experimental Results
### Knowledge-Based Sensor Performance  
The knowledge-based sensor has been designed and developed for Sun SPOTs [31] sensors, which have the following main characteristics: they can execute Java programs (J2 ME), 180 MHz, 32-bit ARM920T CPU, 512 K RAM, 4M Flash, 3.7 V 720 mAh battery, three modes (awake: draw current 86 mA; shallow sleep: 24 mA; deep sleep: 32 uA), integrated sensors (3-axis accelerometer, temperature, light), several inputs and outputs (analog and digital), and a IEEE 802.15.4 compliant radio to perform wireless communication. Routing, meshing and fragmentation with the Sun SPOT stack are accomplished through the LowPAN protocol. Multi-hop connectivity is accomplished by AODV, a sophisticated routing protocol for _ad hoc_ networks. In the transport layer, the protocols that allow Sun SPOT applications to access the network are the RadioGram protocol and the RadioStream protocol.  
In order to evaluate the performance of the knowledge-based sensor, we utilized a testbed comprising a WSN with 10 Sun SPOTs, a base station (access point) and a personal computer connected to the Internet. Several experiments were conducted, including the use of a preliminary FRBS adapted to the Sun SPOT sensor [30] to study the relationship between the number of inferences  
Figure 3: A sensor with local and collaborative knowledge.  
per second and the number of rules in the KB, as well as the relationship between the number of inferences per second and the number of antecedent propositions per rule.  
In this work, the reaction time of the knowledge-based sensors was measured, taking into account that the KB we used has 25 rules, with two propositions in the antecedent per rule (Table 1); it is possible to reduce the KB to 14 rules because of redundant knowledge; the collaborative KB has 9 rules with two propositions in the antecedent per rule (Table 2). The reaction time of the sensor (_i.e._, the time to execute both KBs) was 5.7 ms, which agrees with previous work and is sufficient for the real-world application presented in this work.  
Taking into account the time to execute both KBs (5.7 ms in awake mode, with a current draw of 86 mA), the interval between inferences (30 min in deep sleep mode, drawn current = 32 mA) and the battery capacity (720 mAh), we estimate that the lifetime of the batteries in this system to be around 608 days.  
In addition, we carried out a battery consumption test on the Sun SPOT sensors of the testbed. First, the battery was completely charged. Next, the sensor followed a cycle composed of an inference of both KBs and an interval of 30 s between inferences in deep sleep mode. The capacity of the sensor battery was transmitted to the computer every 30 min for post analysis. The test ended when the battery was completely discharged. In these conditions, the average power consumption was 0.0218 mAh/cycle; the estimated lifetime of the battery is around 688 days, taking into account a cycle every 30 min.
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## Proposed Topology
### Voltage and Current Stress across Components  
The voltage and current stress across the switches and diodes are presented in Table 1. The voltage across the diode and switches is lower than the output voltage, which is not valid in the conventional quadratic boost converter. The selection of the lower rating components means a reduction in the manufacturing cost.
## 3 System Models and Assumptions
### Radio Energy Dissipation Model  
To analyze the performance of our proposed protocol, we need to know the amount of energy that is consumed by a sensor node. Therefore, we use a similar Radio Energy Model to the one proposed in [38]. According to [38], the radio energy dissipation model is illustrated as in Figure 2.  
In this model, both the free space (\(d^{2}\) power loss) and the multipath fading (\(d^{4}\) power loss) channel models are used. If the distance is less than a threshold \(d_{0}\), the free space model is used; otherwise, the multipath model is used. Thus, to transmit a \(k\) bit packet over a distance \(d\), the consumption energy is computed as [38]:  
\[E_{Tx}(k,d)=\ \left\{\begin{array}{ll}k\times E_{elec}+k\times E_{fs}\times d^{2 }&\text{if $d<d_{0}$}\\ k\times E_{elec}+k\times E_{mp}\times d^{4}&\text{if $d>d_{0}$}\end{array}\right. \tag{2}\]  
and to receive a \(k\) bit packet, the consumption energy is [38]:  
\[E_{Rx}(k)=k\times E_{elec} \tag{3}\]  
Figure 2: The radio energy model.  
where \(E_{elc}\) denotes the electronics energy, which depends on factors such as the digital coding, modulation, filtering, and spreading of the signal. \(E_{fs}\) is the free space power loss and \(E_{mp}\) is the multipath fading loss. The value of threshold distance \(d_{0}\) is given by \(d_{0}=\sqrt{\frac{E_{fs}}{E_{mp}}}\).  
From Equations (2) and (3), it is clearly shown that the energy consumed for data transmission increases as the packet size increases and the square or biquadratic of distance, while the energy consumed for reception is only proportional to the packet size [39].