esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Matching FINN-Prescribed Burning Records with Permits  
FINN is a satellite-derived product and estimates the burned area with the help of fire radiative power detected from MODIS and VIIRS. The approach has several limitations due to a lack of information on combustion completeness or fuel loading [40]. Additionally, cloud cover and thick smoke due to large fires can affect radiative power detection by satellites [41]. Permit records are reported when land managers plan to execute prescribed burns and may not reflect the actual day a burn was accomplished. The burned area in permit records is estimated by land managers. To evaluate the disparity between FINN and permit estimates, we first removed agricultural burns from both datasets using an agricultural identification algorithm. Then, we applied a wildfire detection algorithm to remove any wildfires detected by FINN. Finally, we compared the prescribed burning burned area in permits and FINN using three types of matching: statewide, fire-to-fire, and grid-based.
## 2 Study Area and Data
### Research Data and Processing  
The remote-sensing data used in this study primarily include JL1 LF01A and Sentinel-1 data. Additional geological data and known datasets will be described in subsequent sections. Table 1 provides descriptions of the main remote-sensing data.
## 8 Introduction to Proprietary and Standards-based Solutions
### Proprietary Technologies  
#### 8.1.1 Sigfox  
Sigfox [17] is a proprietary ultra-narrowband LPWAN technology that uses a slow modulation rate to achieve longer range. Initially, Sigfox supported only unidirectional uplink communication, that is, between the sensor devices to the aggregator with differential binary phase shift keying (DBPSK) modulation. The later releases support bi-directional communication, where Gaussian frequency shift keying (GFSK) modulation is used for downlink. This ultra-narrowband feature of Sigfox allows the receiver to only listen in a tiny slice of the spectrum so that the effect of noise can be mitigated [18]. Like LoRa, Sigfox also uses unlicensed ISM bands. Sigfox has inexpensive sensor devices and cognitive SDR-based access stations to manage the network and for Internet connectivity. Sigfox supports very a low data rate compared with other LPWAN technologies. To provide reliability, Sigfox transmits the message multiple times, resulting in high energy consumption. One of the main differences between Sigfox and LoRa is business distinction. Sigfox is deployed by network operators, and the users need to pay the subscription charges, whereas LoRa can be deployed as an own independent network with no subscription charges. Sigfox gateway can handle up to a million connected objects, with a coverage area of 30-50 km in rural areas and 3-10 km in urban areas [19].
## 3 LVT Finite Element Statics Analysis
### Model Establishment  
The finite element model of the LVT structural system includes rails, fasteners, support blocks, rubber boots, steel bars, track bed slabs and base slabs. For better verification with the full-scale model test, the established track structure parameters are the same as the full-scale test model.  
(1) Rail  
The rail is 60 kg/m with the modulus of elasticity of 210,000 MPa, Poisson's ratio of 0.3, and density is 7800 kg/m\({}^{3}\). To accurately measure the lateral displacement and flip angle of the rail, the solid element simulation was used and the rail section was drawn according to the actual size (Figure 17).  
(2) Fasteners  
The fasteners are elastic strip VII fasteners at a spacing is 600 mm. The fasteners were simulated using spring elements connecting the rail and the bearing block, while the rail was restrained by vertical and lateral and torsional stiffness. The vertical, lateral and longitudinal stiffness of the fastener is 140 kN/mm, 100 kN/mm, and 15 kN/mm respectively  
Figure 17: Finite element model of rail.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Support Block Number** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline TLVT & 3\% & 8\% & 19\% & 40\% & 19\% & 8\% & 3\% \\ ILVT & 3\% & 7\% & 18\% & 44\% & 18\% & 7\% & 3\% \\ \hline \end{tabular}
\end{table}
Table 3: Law of longitudinal sharing of transverse load.  
The bearing block settings are designed with two sizes of TLVT and ILVT (Figure 18), and the parameters of C50 concrete are adopted. The elastic modulus is 34,500 MPa, the Poisson's ratio is 0.2, and the density is 2500 kg/m\({}^{3}\).  
Simulated by solid elements, the thickness of the elastic boots is 7 mm, and the thickness of the pad under the bearing block is 12 mm.  
The track bed slab has a block structure with a width of 2800 mm, a thickness of 393 mm, and a length of 6580 mm. The C40 concrete parameters was adopted for the track bed slab with elastic modulus of 32,500 MPa, Poisson's ratio of 0.2, and density of 2500 kg/m\({}^{3}\)[15,16].  
The HRB400 grade reinforcing bars was adopted for the simulations. The reinforcing bars include 20 mm diameter upper longitudinal reinforcement, upper lateral reinforcement, lower longitudinal reinforcement and lower lateral reinforcement, 12 mm diameter bearing block stirrup and erecting reinforcement (Figure 19). The elastic modulus, poisson's ration and density of the bar is 210,000 MPa, 0.3, 7800 kg/m\({}^{3}\) respectively.  
The base plate is made of C20 concrete and has elastic modulus of 25,500 MPa, Poisson's ratio of 0.2, and density of 2500 kg/m\({}^{3}\)[18]. The support block and the rubber boots, the rubber boots and the track bed plate are in frictional contact, and the friction coefficient is taken as 0.3.  
Combining the above parameters, a finite element calculation model for the long track of the LVT track is established (Figure 20).  
Figure 19: Finite element model of reinforcing bars.  
Figure 18: Finite element model of TLVT and ILVT bearing block. (a) TLVT elastic support block. (b) ILVT elastic support block.
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 4 Conclusions  
In this correspondence, we have constructed three classes of LHZ FHS set. The first two are designed by any \(q\)-ary \(m\)-sequence of degree \(n\), including binary \(m\)-sequences, and the last one is designed by the \(d\)-decimated sequence of \(q\)-ary \(m\)-sequence of degree \(n\), \(q\
eq 2\). The results show that the first construction includes some known constructions as special instances, and all new classes of LHZ FHS set exhibit optimal maximum periodic Hamming correlation properties, good randomness properties and complexity, etc. Furthermore, new LHZ FHS sets have flexible parameters. A comprehensive comparison of our results with similar constructions reported in the existing literature is presented in Table 4. Constructing FHS sets based on new sampling patterns of \(q\)-ary \(m\)-sequences of degree \(n\) is worthy of further investigation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Parameters** & **Parameters** \\ \hline \multirow{8}{*}{this paper} & (171, 4, 7, 57–84, 24) & (171, 6, 7, 42–56, 24) \\ \cline{2-3}  & (171, 8, 7, 34–41, 24) & (171, 10, 7, 28–33, 24) \\ \cline{2-3}  & (171, 12, 7, 24–27, 24) & (171, 14, 7, 21–23, 24) \\ \cline{2-3}  & (171, 16, 7, 19 or 20, 24) & (171, 18, 7, 17 or 18, 24) \\ \cline{2-3}  & \(\cdots\) & \(\cdots\) \\ \cline{2-3}  & (171, 84, 7, 3, 24) & (171, 114, 7, 2, 24) \\ \hline \multirow{2}{*}{[15]} & (171, 3, 7, 56, 24) & (171, 57, 7, 2, 24) \\ \cline{2-3}  & (171, 9, 7, 18, 24) & (171, 19, 7, 8, 24) \\ \hline \multirow{2}{*}{[14]} & (171, 6, 7, 56, 24) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: LHZ FHS sets with optimal MPHC constructed by Example 2 and existing ones. Figure 2: The MPHCC distribution and the MPHC distribution of \(\mathbf{S_{c}}\) for \(0\leq\tau\leq 170\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.
## 2 RFM Optimization
### Application to Push-Frame Images
#### 2.2.1 SkySat  
The images are acquired by 3 staggered sensors (Figure 2). The overlap of consecutive scenes from the same sensor and within different sensors is very small, as well as the baseline. Thus rays between tie points are almost collinear, which can lead to high uncertainty and singularities during the refinement of the RFM. To avoid this issue, the tie points are constrained using a reference DEM [24]. Since the SkySat imagery is acquired in a tri-stereo mode, we perform the refinement in two steps: (1) we subdivide the triplet image scenes into three groups according to the imaging sensor. Next, we perform the RFM refinement for each group separately. (2) We estimate a 3D transformation using the images from the central sensor as a reference (sensor 2 in Figure 2).
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 2 Modeling of the Proposed System  
The stand-alone hybrid system considered in this paper consists of PV/WT/Biomass/Bat units. The layout of the suggested hybrid model is illustrated in Figure 1. This section explains in detail the description of the major system units and the optimization methodology of the suggested hybrid model.  
Figure 1: The architecture of the proposed stand-alone hybrid system.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Performance Metrics  
Packet Delivery Ratio (PDR): PDR is used to identify whether all the data packets sent from the sensor nodes reached the sink or not. PD represents the rate at which the data reach the sink node. For successful communication, PDR is 100%. PDR is calculated simply from Equation (9).  
\[\text{PDR}\ =\frac{\text{TPR}}{\text{TPS}\times 100} \tag{9}\]  
TPR--Total number packets received  
TPS--Total number packets sent  
Latency: Latency is calculated based on the congestion in the network. It is the time taken between the time of the initial data packet sent (\(T_{0}\)) and the time of the last acknowledgment received from the sink (\(T_{n}\)). Latency is computed from Equation (10).  
\[\text{Latency Delay}\ (\text{s}) =\ \frac{(T_{n}-T_{0})\times 100}{2\times 128} \tag{10}\]  
Power Consumption (PC): For forest networks, all the sensor nodes are non-rechargeable. The power of the node decides the lifetime of the node. In the case of E-RPL, the energy of the node in both the CPU and LPM mode is taken. While calculating the energy consumption, both the mode powers are considered along with transmitting and listening power and are expressed in Equation (11). Average power consumption is calculated for all the nodes in the network and given by Equation (12).  
\[\text{Total Power Consumption} =\sum_{j=1}^{\text{sn}}(\text{P}_{\text{LPM}}+\text{P}_{\text{CPU}}+ \text{P}_{\text{RL}}+\text{P}_{\text{RT}}) \tag{11}\]  
\[\text{P}_{\text{LPM}} =\text{Amount of power consumption during low power mode  
\[\text{P}_{\text{CPU}} =\text{Amount of power consumption during normal CPU mode}\] \[\text{P}_{\text{RL}} =\text{Amount of power consumed for radio listening}\] \[\text{P}_{\text{RT}} =\text{Amount of power consumed for radio transmission}\] \[\text{sn} =\text{Total number of sensor nodes in the network}\] \[\text{Average Power Consumption} =\frac{\sum_{j=1}^{\text{sn}}(\text{P}_{\text{LPM}}+\text{P}_{ \text{CPU}}+\text{P}_{\text{RL}}+\text{P}_{\text{RT}})}{\text{sn}} \tag{12}\]
# Generation of Beam Tilt through Three-Dimensional Printed Surface
## 5 Conclusions  
In this paper, we have presented three-dimensional printed antenna prototypes for beam tilting at frequencies from 10 to 15 GHz. The geometrical complexity of the 3D printed structures is easier to be realized. The BDS prototype is able to show the elevation degree of 22.5\({}^{\circ}\) and could rotate the beam along with its rotation in a counter-clockwise direction. The four-layer cube arrangement is able to address the need for full phase values, which is applied to have the beam tilting phenomenon. The radiation patterns are consistent across the frequency range of interest, and the side lobe levels are less than \(-5\) dB. The voltage standing wave ratio of the designed prototype shows the value is less than 2 and it is well-matched within the operating frequency band. The research could be further carried out by proposing a structure that could show a conical rotation of the tilted beam. The electric motor could be used to demonstrate the automatic rotation of the BDS prototype, where the system can be operated remotely. The radiation patterns of the analytically designed antenna are numerically simulated using the CST microwave studio, which demonstrates broad bandwidth in the operating frequency band, covering the Ku-band, and are in good agreement with measured radiation patterns.  
[PERSON], [PERSON], [PERSON], A.K., B.M. and M.A. equally contributed to the methodology, performance analysis and manuscript draft. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Australian Research Council (ARC) and in part by the Macquarie University International Research Excellence Scholarship (iMQRES).  
We want to extend thankfulness to CSIRO, Marsfield, NSW, Australia, and Macquarie University for providing support for measurements of the antenna prototype.  
The authors declare no conflict of interest.
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Number of Control Packets  
Reducing the number of control packets during the network setup phases is a challenging task to design a cost effective routing model. The proposed quasi-static nature of RC nodes election reduces a significant number of control packets throughout the network lifetime. In order for nodes to be elected as the CHs, the 3D EADC requires the collecting of information about their residual energy at each round. This encourages transmitting and receiving an additional number of packets and specifically, the number will increase with the higher number of neighbor nodes within a range \(R_{\text{c}}\). Figure 4 shows the number of control packets transmitted in the network. EDR reduces the control packets 64.65% and 91.53% throughout the network lifetime of 3D LEACH and 3D EADC, respectively.  
Figure 4: Number of control packets transmitted in the network.  
Figure 3: The CH/RC nodes changed at different rounds.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic
### IEEE 802.15.4e TSCH  
In an IEEE 802.15.4e TSCH network, each node maintains a matrix-formatted schedule, in which the number of columns represents the number of time slots within one slotframe [17]. This slotframe is repeated over time and its size is typically application-dependent. The number of rows of the schedule represents the number of available physical channels. One element in the schedule is called a cell and is denoted by (_timeOffset, channelOffset_) with, respectively, the time and channel offset in the schedule. As such, each node is time-synchronized, with time being split up into fixed-duration time slots, which are indexed by the time offset and at every slot a node can use one of the available channels, which is calculated based on the channel offset. A default time slot is typically long enough to send a packet of 127 bytes and receive an acknowledgement (ACK), using the predetermined physical layer applied by all the nodes in the network. To define absolute time, the TSCH MAC layer uses the so-called Absolute Sequence Number (ASN), which is 0 at the very first time slot and is incremented by one at every time slot that follows.  
By telling a node exactly what to do in each slot, the TSCH MAC layer is capable of very low-power operation. The different possible actions of each slot are transmitting, receiving or sleeping. When a scheduled cell is marked for transmission, the node will transmit the first packet in the queue. In a receiving scheduled cell, a node listens for an incoming packet. A _dedicated_ cell between two nodes is a cell reserved only for those two nodes. When more nodes are allowed to use a cell for transmission, this cell is called a _shared_ cell in which the collision probability is kept to a minimum by using a back-off algorithm. To bootstrap the TSCH network and allow all nodes to disseminate network and routing information across the network, each TSCH schedule typically has a so-called _minimal shared cell_[18]. To achieve very reliable communication, TSCH applies channel hopping (which helps to avoid external interference and destructive multi-path fading effects) by taking into account the ASN of the cell together with its channel offset, to select the frequency channel of the transmission in a pseudo-random manner.  
Figure 1 shows an example of a TSCH schedule. More specifically, node X illustrates a traditional TSCH reservation of two regular time slots for its data transmissions to the root node. Using PHY1, the transmission of the frame and the reception of the acknowledgment fits in one time slot of 10 ms.  
Figure 1: A TSCH schedule with regular slots of 10 ms length. Node X uses physical layer PHY1 that only requires one regular time slot of 10 ms. For accommodating physical layer PHY2 on the link from node Y to the root, three slots are bonded together to a 30 ms slot. For the minimal shared cell, the most robust PHY2 is used.
## 3 Coupled Cavities Filter in Rectangular Waveguide
In this case, using three points was enough for high accuracy results. Figure 4 compares the discrete and fast frequency sweep responses of the whole filter with \(R=9\) cavities. As expected, the fast frequency response was highly accurate with \(P=3\). The same comparison was made for different number of filter orders. The results are shown in Figure 5 and 6. It can be observed that the fast sweep required approximately 16% of the computation time required by the discrete sweep, even for high filter orders. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(P\) & **Fast (s)** & **CPU Time Fast/Discrete** & **Error** \\ \hline
2 & 0.4179 & 14.8841\% & \(7974.2\times 10^{-6}\) \\
3 & 0.4292 & 15.2847\% & \(76.4445\times 10^{-6}\) \\
4 & 0.4472 & 15.9268\% & \(1.6391\times 10^{-6}\) \\
5 & 0.4459 & 15.8801\% & \(0.2448\times 10^{-6}\) \\
6 & 0.4498 & 16.0170\% & \(0.0901\times 10^{-6}\) \\
7 & 0.4558 & 16.2319\% & \(0.0378\times 10^{-6}\) \\
8 & 0.4594 & 16.3597\% & \(0.0190\times 10^{-6}\) \\
9 & 0.4635 & 16.5045\% & \(0.0099\times 10^{-6}\) \\
10 & 0.4731 & 16.8472\% & \(0.0054\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error and CPU time evolution for the all-metallic filter (\(R=4\) cavities) as the number of frequency points for the fast sweep (\(P\)) increases. Discrete sweep used 500 points and took 2.808 s. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(P\) & **Fast (s)** & **CPU Time Fast/Discrete** & **Error** \\ \hline
2 & 0.8272 & 15.6154\% & \(10,507.4\times 10^{-6}\) \\
3 & 0.8486 & 16.0180\% & \(98.2219\times 10^{-6}\) \\
4 & 0.8817 & 16.6428\% & \(2.2199\times 10^{-6}\) \\
5 & 0.8709 & 16.4387\% & \(0.2460\times 10^{-6}\) \\
6 & 0.8858 & 16.7192\% & \(0.1199\times 10^{-6}\) \\
7 & 0.8992 & 16.9728\% & \(0.0550\times 10^{-6}\) \\
8 & 0.9294 & 17.5429\% & \(0.0239\times 10^{-6}\) \\
9 & 0.9370 & 17.6849\% & \(0.0100\times 10^{-6}\) \\
10 & 0.9411 & 17.7639\% & \(0.0051\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error and CPU time evolution for the all-metallic filter (\(R=9\) cavities) as the number of frequency points for the fast sweep (\(P\)) increases. Discrete sweep used 500 points and took 5.298 s.  
Figure 4: S-parameter comparison between fast sweep and discreet sweep for the nine-cavity filter. \(P=3\).
###### Abstract  
The two major problems that have plagued urban underground roads since their introduction are the harmful emissions caused by hot mix paving and vehicle exhaust accumulation during operation. In order to solve these two problems at the same time, a new asphalt mixture degrading automobile exhaust, which has the advantage of cold mix and cold-application, was presented and studied. A considerable amount of research shows that the use of titanium dioxide (TiO\({}_{2}\)) for pavements has received considerable attention in recent years to improve air quality near large metropolitan areas. However, the proper method of applying TiO\({}_{2}\) to asphalt pavements is still unclear. The new mixture presented in this article contains epoxy emulsified asphalt as the binder; therefore, how to apply TiO\({}_{2}\) in the special asphalt mixture proves to be the main focus. By experimental design, four influence factors on the photocatalytic effect, which are the nano-TiO\({}_{2}\) particle sizes, dosage, degradation time, and light intensity, have been investigated. The experimental results showed that the 5-nm particle size of TiO\({}_{2}\) is better than 10-15 nm for exhaust gas degradation, especially for HC and NO; with an increase in the amount of photocatalytic material, the degradation of CO and CO\({}_{2}\) in the exhaust gas did not increase obviously, while the degradation effects of HC and NO were remarkable; in the 4-h time extended degradation test, the experimental data show that the extended time has little effect on the degradation rate of CO\({}_{2}\) and CO, and the general trend is that the degradation of exhaust became significant with the extension of time; while setting a 2-h NO degradation rate as an indicator, to make the index more than 50% or 25%, the average illumination of the road surface cannot be less than 60 lx or 40 lx.  
nano titanium dioxide; epoxy emulsified asphalt; photocatalysis; exhaust gas degradation Article
## 5 Evaluation Measures
### Miscellaneous
#### 5.4.3. Intention to Use or Purchase  
The intention to use or purchase [26; 27; 30; 37; 56; 71] is also a theme addressed by some studies in our sample. [PERSON] [37], for instance, understood purchase intention as a subconstruct of persuasiveness, arguing that convincing explanations will increase user acceptance and, in turn, enhance purchase intentions. Therefore, her persuasiveness questionnaire includes this subconstruct, asking participants to rate statements such as _\"I would purchase the product I just chose if given the opportunity\"_ and _\"I will use the system again if I need a tool like that\"_. In contrast, [PERSON] et al. [71] consider it a subconstruct of trusting intentions, where trust in a recommender system is reflected by the user's intention to make a purchase, evaluating their willingness to buy something based on the AI's recommendation. A slightly different approach is found in [PERSON] et al.'s study. Like [PERSON] et al. [71], [PERSON] et al.'s [30] view is that the intention to use a system is an indication of genuine trust. During the interviews, they asked participants to rate how much they believed the system was ready for use. However, this question primarily served as a method to encourage participants to reflect on the system seriously. By asking participants to explain their rating, the authors aimed to analyze how the users understood the system.  
In other studies [26; 27; 47], the intention to use is considered an independent theoretical construct. This construct refers to an individual's subjective likelihood or willingness to use a specific technology or system in the future. It typically serves as a proximal antecedent to actual system usage based on their beliefs and perceptions about the technology. In [PERSON]'s study [47], the construct is operationalized by asking participants if they would allow the system to access their currently used social media accounts. Also, [PERSON] et al. [26] and [PERSON] et al. [56] operationalize the construct in terms of future usage, including statements like _\"I will use this recommender system again\"_ ([PERSON] et al. [26]) and _\"If I want to solve math exercises again, I will choose the system\"_ ([PERSON] et al. [56]) in their post-task surveys.