esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications
### Ecology  
Traditionally, data collected from field survey are the main source for ecological studies. However, field data can hardly cover large areas and over long time periods [(89; 90)]. Fortunately, many studies suggest that satellite images, in particular images generated by spatiotemporal data fusion, have a great potential for ecological applications [(14; 91; 92)], such as, ecological variables estimation (e.g., vegetation index and biomass), ecosystem dynamics monitoring (e.g., phenology of forest, grassland, dryland and wetland) and disturbances detecting (e.g., wildfires and tree disease) in ecosystems.  
The improvement of using spatiotemporal data fusion has been demonstrated by several studies. For LAI modeling, the R\({}^{2}\) and RMSE between LAI derived from fused MODIS and Landsat images and ground measurement can reach 0.977 and 0.1585 m\({}^{2}\)m\({}^{-2}\) (\(p<\) 0.005), and such results are more accurate than MODIS LAI standard products [(93)]. For biomass estimation, synthetic NDVI time series from fusing MODIS-Landsat by STARFM can obtain accurate grassland biomass (R\({}^{2}\) = 0.77, RMSE = 17.22 g/m\({}^{2}\)). It is more accurate than using NDVI from MODIS alone (R\({}^{2}\) = 0.73, RMSE = 30.61 g/m\({}^{2}\)) [(94)]. For vegetation phenology detection, the synthetic EVI time series through fusing MODIS and Landsat images during 2005-2009 by STARFM can well characterize dryland vegetation phenology in Arizona, USA [(95)]. For forest disturbances detecting, data fusion also shows its superiority. To map forest disturbance, Landsat and MODIS reflectance data were fused using STAARCH. Compared with validation datasets, the spatial precision can reach 93% in Canada [(27)] and 89-92% in mixed forests of USA [(96)].
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 5 Performance Study
### Simulation Setup  
The simulated communication network considers one primary and seven secondary substations as depicted in Figure 1b. The simulation experiments are performed using the network simulator 2 (ns2) version 2.35, which implements the MPTCP protocol according to the IETF RFC 6824. Each substation has an MCU that is facilitated with two network interfaces, one of which is a WiFi connection and the other is a 3G/4G connection. It is assumed that all the distribution substations fall within the communication range of one another. The WiFi interface is configured according to Table 1, where all the configuration parameters are defined in [34]. The 3G/4G interfaces are simulated as dedicated wireless links between all the substations. The settings follow the peak data rates of different high speed packet access 3G/4G releases as listed in Table 2[35].  
A variety of scenarios and configuration parameters are examined. As a worst-case scenario, we investigate seven secondary substations simultaneously transmitting data packets to a primary substation for processing and analysis. Data transmission is managed by the MPTCP in order to distribute data packets over the two networks of the HWN. Data packets are 100 bytes in size. MPTCP adds a header of 60 bytes. For every received packet, the primary substation returns 40-byte ACK packets to every source. We evaluate the impact of various network configuration parameters, namely,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Parameter** & **Value** \\ \hline Basic Rate & 6 Mbps \\ Data Rate & 54 Mbps \\ PHY & 20 \(\upmu\)s \\ PLCP & 24 bits \\ MAC Overhead & 246 bits \\ \(T_{ACK}\) & 22.3 \(\upmu\)s+ PHY \\ \(T_{RTS}\) & 30.3 \(\upmu\)s+ PHY \\ \(T_{CTS}\) & 22.3 \(\upmu\)s+ PHY \\ Slot Time & 9 \(\upmu\)s \\ SIFS & 16 \(\upmu\)s \\ DIFS & 34 \(\upmu\)s \\ \(CW_{min}\) & 32 \\ \(CW_{max}\) & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IEEE 802.11 System parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**3 GPP Release** & **Downlink Speed (Mbps)** & **Uplink Speed (Mbps)** \\ \hline Rel 6 (3G) & 14.4 & 5.7 \\ Rel 9 (4G) & 84 & 23 \\ Rel 11 (4G) & 336–672 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 3G/4G maximum speeds.  
the 3G/4G uplink speed, the maximum congestion window size, the amount of data to be collected from each substation, and the number of substations involved in the data transmission as listed in Table 3. The simulation results are mainly discussed in terms of the achievable throughput and the time duration to successfully collect a volume of data simultaneously from a number of substations.
## 3 Modelling of the Bus Transportation Network
### Bus Network Modelling
#### 3.5.1 GTFS Data  
The GTFS format was initially developed by Google and has since become a de facto standard. Open data in GTFS format are generated by urban transport organizing authorities. They are composed of several tables gathering information about bus schedules and line topography [52] via various text files.  
The major advantages of GTFS files are that they are open access and the data are provided in the same format for the transportation network of different cities. However, they must be combined with a description of bus services to simulate the operation of buses. The location information (i.e., latitude and longitude) of bus stops can be found in GTFS data but the distances and slopes between stops must be computed with the post-treatment as in the following section.
## 4 LTC Architecture
### WiFi-Enabled RFID Door Entry Nodes  
Door entry nodes are a vital component of the LTC system, and they are used to track users' current locations while they move inside the enterprise. In this work, we use the Radio Frequency Identification (RFID) [32] technology to track the users' mobility while they enter or exit the rooms. The door entry node, depicted in Figure 5, mainly consists of an RFID reader and a Wi-Fi module. The door entry node is meant to be simple, cheap, and easy to install and integrate under different circumstances without potential infrastructure modification. The proposed door entry node uses NXP's PN532 Near Field Communication (NFC) controller for RFID functionalities and Espressif's ESP32 Microcontroller Unit (MCU) for Wi-Fi connectivity. When a user carrying an RFID tag passes by a door entry node, the door entry node (MQTT publisher) reads the RFID tag and publishes the tag number and the reader ID to the MQTT broker (the PoT gateway). The web server (MQTT subscriber), which subscribes to all users' topics by default, receives the update from the PoT gateway and updates the MongoDB database accordingly. The MongoDB database is a dynamic mapping of the nearest extension number to a specific user based on his current location. The nearest extension number is determined by the current door entry checkpoint, the previous door entry checkpoint, and the preconfigured table created at the system startup. This preconfigured table maps the checkpoints' sequence to the location, based on the floor plan of the premise. Section 5 discusses the implemented algorithm to determine whether a user enters or exits a room using a single door entry checkpoint for each room.  
It is worth noting that the emergence of BLE/LoRa tags and beacons also proposes suitable and cost-effective indoor positioning candidates for LTC applications. Moreover, LTC can take advantage of the wireless distribution system (WDS) in an enterprise to track people's location with no further infrastructure requirements. This is believed to reduce the system cost; since the LTC system would only need the PoT-enabled gateway to function. This also enhances the organizations' workflow; since the system would neither require the employees to tap their RFID tags every time they enter or exit a place nor need expensive long-range RFID readers to be utilized by the door entry checkpoints.
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 2 Antenna Design
### Parametric Modeling  
The proposed antenna resonance was achieved in a number of steps by careful modelling of several parameters. All the parameters play an important role in resonating the antenna at the desired frequency response. The ground slot length and width and the main hook-shaped resonator strip lengths were parametrically analyzed and observed step by step.  
Figure 2 shows the detailed parametric analysis of the proposed antenna system. The first parameter that was analyzed was h. The parametric value of h was set from 7.5 to 9.5 mm. It was observed that at an optimal width of 8 mm, the proposed antenna showed the best resonance response, and with a further increase in the value resulted in impedance mismatch. After this, as shown in Figure 2b, the parameter i was analyzed from 12 mm to 16 mm. It was observed that with initial lower values, the resonance frequency was ahead of the desired frequency, and with a further increase in the parameter length the resonance response moved backwards. The optimal length achieved was 14 mm, at which the desired frequency response was achieved. The length and width of the ground slot played an important role in shifting and matching the antenna impedance.  
Figure 1: Proposed antenna design: (**a**) Front (**b**) Back.  
Figure 2: _Cont._The role of parameter g in the resonance is shown in Figure 2c. The role of the length of g is prominent in the proposed antenna resonance. With a lower value, the resonance is moved away from the desired levels, and the optimal length of the parameter achieved was 3.5 mm. The role of parameter f, as seen in Figure 2d, can be described as most critical in the resonance frequency of the proposed antenna. The f parameter of the proposed antenna was parametrically analyzed from 7 to 9 mm at 5 intervals of 0.5 mm. It was observed that even with a 0.5 mm increase or decrease in value, the resonance frequency shifted rapidly. The resonance frequency shifted forward with ascending values, and the desired resonance was achieved at 8 mm.
# Research on Hybrid Relay Protocol Design and Cross-Layer Performance Based on NOMA
## 3 Cross-Layer Performance Analysis of Two-Hop Relay System Based on NOMA
### Two-Hop Transport Protocol Design Based on NOMA  
The two-hop random access scheme in this paper is based on the CSMA protocol of IEEE 802.11 and IEEE 1901 standards. Only when the packet of the first hop is successfully transmitted to the R terminal, the transmission process of the second hop will occur. Considering the method of using a predetermined power level [20], NOMA does not require complete channel state information, but only receives signal strength information [21]. R periodically sends a beacon signal to the source site for synchronous transmission. When the coherence time with time-sharing duplex mode is long enough--that is, the channel gain remains constant within the frame--each station can use the beacon signal as a pilot signal to estimate its channel coefficient before the start of the frame based on channel reciprocity [20], thus selecting the transmit power itself.  
Figure 2 shows the time slot diagram of the two-hop transmission scheme based on NOMA. In this scheme, NOMA Clear Send (N-CTS) and NOMA acknowledgement frame (N-ACK) are defined, both of which add a receiver address field [21]. The station of source S uses CSMA contention strategy, and the station that successfully competes obtains channel occupancy sends a data packet to R with different power levels. After receiving the data packet, R sends N-ACK to the source station for confirmation and transmits it to the two destinations through the power line channel after ideal SIC decoding. After SIC decoding at the destination end, R is answered with N-ACK1 and N-ACK2, respectively, to achieve acknowledgement. By introducing NOMA into the CSMA protocol, the system can simultaneously transmit two data packets without contention for the channel for each data packet, avoiding frequent collisions between stations, thereby improving the throughput and reliability of the system, reducing the delay, and saving a lot of transmission-signaling overhead. The proposed scheme can meet the reliable and stable transmission requirements of multiple users under high load.
## 1 Introduction
Furthermore, [PERSON] and [PERSON] [21] suggested that a higher H\({}_{2}\)/CO ratio in reformed fuel resulted in a shorter combustion duration and lower heat release during the reactivity-controlled compression ignition (RCCI) combustion process. These research findings indicate that the performance as well as the combustion and emission characteristics of an engine are influenced by changes in fuel composition. The studies mentioned above have primarily focused on compression ignition (CI) engines fueled with diesel-hydrogen mixtures and diesel-steam reformed gas mixtures. However, there is limited research on CI engines fueled with a blend of diesel and methanol dissociation gas (DMG). In our previous investigation [16], the hydrogen content in DMG was between 60.7% and 64.8%, the CO content was between 19.1% and 23.1%, the methanol content was between 8.3% and 10.9%, and there was a small amount of by-products (carbon dioxide, methane, dimethyl ether, etc.) once the reaction temperature of 380-400 \({}^{\circ}\)C, the space velocity of 1.2-1.5/h, and a copper-based catalytic reactor were adopted. The changes in fuel composition and operation parameters may have had an impact on the engine working process. Consequently, in this study, we conduct a numerical investigation on a CI engine fueled with a blend of diesel and DMG at various substitution ratios and exhaust gas recirculation (EGR) rates. The objective is to explore the combination effects of DMG substitution ratio and EGR rate on the engine's performance, combustion, and emission characteristics.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 2 Research on the Calibration Method
### Frequency Domain Correction Method  
After the parameter estimation and verification of the identification model of the TIADC system is completed, we obtain an identification model with dynamic performance close to the actual hardware system. To improve the dynamic performance of the TIADC system, eliminating the dynamic error of the system is the first issue to be considered. When the transfer function of the measurement system is \(G(s)\) and the Laplace transform of the measured signal is \(X(s)\), the dynamic error of the measurement results is as follows:  
\[\Delta Y(s)=[G(s)-G(0)]X(s) \tag{41}\]  
Equation (41) shows that the dynamic error of the system is related to the dynamic characteristics of the measurement system and the spectrum structure of the measured signal. The frequency domain correction method calculates the spectrum \(X(j\omega)\) of the measured signal from the frequency characteristic \(G(j\omega)\) of the test system and the spectrum \(Y(j\omega)\) of the measured signal, and then uses the inverse Fourier transform to obtain the estimated value of the measured signal, which can be expressed as:  
\[\hat{x}(t)=IFFT[Y(j\omega)/G(j\omega)] \tag{42}\]  
Next, the discrete transfer function of the TIADC system is used to directly recover the measured signal from the measured results using the deconvolution method, and good results can be obtained. The calculation of this algorithm is relatively small, and most of the calculations can be carried out in advance before the experiment. Therefore, it can be used in online test corrections or test data processing systems.  
Suppose \(x(t)\) is the input analog signal, and \(y(t)\) is the measurement result of the TIADC system. According to the bilinear transformation method, the transfer function of the TIADC system can be discretized and transformed into the following form:  
\[G\Big{(}z^{-1}\Big{)}=\frac{b_{0}+b_{1}z^{-1}+b_{2}z^{-2}+\cdots+b_{m}z^{-m}}{ 1+a_{1}z^{-1}+a_{2}z^{-2}+\cdots+a_{n}z^{-n}} \tag{43}\]  
Figure 6: Flow chart of parameter estimation of the system transfer function.  
When Equation (43) is described by the difference equation, it can be expressed as:  
\[x(k)=\frac{1}{b_{0}}\left[y(k)+\sum_{i=1}^{n}a_{i}y(k-i)-\sum_{i=1}^{m}b_{i}y(k-i)\right] \tag{44}\]  
In Equation (44), \(x(k)\), \(y(k)\) are sampled values of \(x(t)\), \(y(t)\), respectively.  
Equation (44) shows that the measured signal can be recovered from the measured signal with the discrete transfer function of the TIADC system. Among them, the sampling interval \(\Delta T\) of \(y(k)\) should be consistent with the switching period \(\Delta T_{0}\) (i.e., the sampling interval of the analog/digital converter) of the discrete system \(G\left(z^{-1}\right)\). Therefore, before the experiment is conducted, every sampling interval \(\Delta T\) is selected, and the discrete transfer function must be calculated by the continuous transfer function \(G(s)\).
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Transport Layer
#### 5.4.3 Transmission Windows  
A number of transmission windows \((w)\) with their corresponding e2e ACK are included in each uplink data transmission phase to ensure correct packet reception. Within these windows, not all STAs remain awake, but only the ones directly involved in the transmission process. Before the start of a new transmission window, all STAs evaluate whether they shall stay awake or go to sleep.  
This decision takes into account whether the STA has been previously _poisoned_ by one of its children as well as several other restrictions according to the decision flowchart from Figure 9. Whenever an STA decides to go to sleep, it will remain in this state until the next _primary beacon_.
# A Novel Optimization Layout Method for Clamps in a Pipeline System
## 3 Results and Discussion
### Validation of the Optimization  
An experimental platform was set up to verify the optimization results. Due to the limitation of the experiment conditions, only two sets of optimization results with first-order frequency difference maximization and stress response minimization were verified, and the position of acceleration sensor was consistent with the simulation. Simulation results show that the maximum stress is in the clamp band, and it's impossible to mount strain gauge there. In this experiment, strain gauges are mounted along the horizontal and vertical direction near the bend. The clamp-pipe system before optimization is shown in Figure 17.  
Adjust the position of the clamps according to the values of L1, L2 and L3 in Table 1 respectively as shown in Figure 18, the first-order frequency and stress peak-peak value of the optimized pipeline system is obtained in Table 2.  
As can be seen from Table 2, the first-order frequency difference before and after optimization increased from 49.4 Hz to 70.2 Hz, which is basically the same as the simulation results. The horizontal directional stress value at the bending point of the pipeline system is reduced from 9.82 MPa to 5.54 MPa, and the peak of the vibration stress response peak has a large degree of reduction, which means that it can significantly reduce the pressure of the pipe system by optimizing the clamp arrangement.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Optimization Goal** & **L1 (mm)** & **L2 (mm)** & **L3 (mm)** & \begin{tabular}{c} **Frequency** \\ **Difference (Hz)** \\ \end{tabular} & \
\begin{tabular}{c} **Displacement** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Stress** \\ **(MPa)** \\ \end{tabular} \\ \hline Before Optimization & 50 & 100 & 50 & 50.42 & 0.0502 & 29.56 \\ Maximizing the frequency difference & 40.3 & 50.7 & 41.7 & 68.41 & 0.0374 & 37.54 \\ Minimizing the displacement & 111.3 & 100.8 & 110.7 & 37.54 & 0.0338 & 20.75 \\ Minimizing the stress & 128.3 & 85.7 & 122.7 & 37.24 & 0.0849 & 18.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clamps layout before and after optimization.  
Figure 17: Clamp-pipe system before optimization.  
Figure 18: Clamp-pipe system after optimization: (**a**) Maximizing frequency difference; (**b**) Minimizing the stress.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 6 Simulation
### Simulation Scenarios and Parameters  
For a more objective comparison with Spray-and-Wait, we also set the initial number of message copies, \(L_{init}\), to 8. Based on \(L_{init}\) = 8, we found suitable values for the other parameters through several tests. All specific parameters are shown in Table 2. In this section, the simulation is performed for Vehicle Ad Hoc Network (VANET), a self-organizing network, and the map used is the city map of Helsinki, with a total of four groups of nodes with different attributes, and the attributes of each group of nodes are shown in Table 3. The nodes in group 1 and group 3 will be randomly assigned their points of interest on the map. The probability of nodes selecting their corresponding points of interest as new destination locations is higher than that of other locations on the map. After determining the destination location, the node will first calculate the shortest path based on the road and then follow the road to the destination. The probability of selecting different points of interest as the new destination location is also random and different. The nodes in group 2 and group 4 follow their respective fixed lines of movement. This is to simulate the different working attributes of different network nodes in the real DTN scene.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Default Value** & **Variation Range** \\ \hline Scenario Size (m\({}^{2}\)) & 4500 \(\times\) 3400 & - \\ Simulation Time (h) & 10 & - \\ Message Generation Interval (s) & 30 & 10–50 \\ Message Size (KB) & 1250 & 1000–1500 \\ Buffer Size (MB) & 15 & 5–25 \\ TTL (min) & 60 & 20–100 \\ Number of Nodes & 300 & 100–500 \\ \(L_{init}\) & 8 & - \\ \(\beta_{best}\) & 0.6 & - \\ \(\partial\) & 2 & - \\ \(M\) & 3 & - \\ \(T\) (min) & 30 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group Number** & **Parameter** & **Value** \\ \hline Group 1 (Pedestrians) & movement speed (m/s) & 0.5–1.5 \\  & communication rate (Kbps) & 250 \\  & communication range (m) & 30 \\ \hline Group 2 (Bus) & movement speed (m/s) & 2.7–13.9 \\  & communication rate (Kbps) & 250 \\  & communication range (m) & 30 \\ \hline Group 3 (Taxi) & movement speed (m/s) & 3–12 \\  & communication rate (Kbps) & 250 \\  & communication range (m) & 30 \\ \hline Group 4 (Tram) & movement speed (m/s) & 7–10 \\  & communication rate (Mbps) & 10 \\  & communication range (m) & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Node attributes.
## 2 Materials and Methods
### Physical Characterization  
The surface microstructure of the samples was investigated using FE-SEM (Gemini 500, Zeiss, Oberkochen, Germany) and TEM (Tecnai G\({}^{2}\) F20, FEI, Ames, IA, USA). The surface chemical composition was characterized using EDS (X-Max\({}^{N}\) 80, Oxford, UK), FTIR (Spiritt-T, Shimadzu, Chiyoda-ku, Tokyo, Japan), and XPS (ESCALAB 250 Xi, Thermo Fisher, Waltham, MA, USA). X-ray diffraction patterns were recorded using an XRD apparatus equipped with a Cu K\(\alpha\) radiation source (D8 Advance, Bruker, Karlsruhe, Germany). The water contact angles of the samples were measured using a contact angle meter (DSA100, KRUSS, Hamburg, Germany).