esa-sceva/satcom-chunk-collection huggingface dataset:
# Bent and Branched Microstrip-Line Antennas for Circular Polarization
## 3 Branched Line Antenna
### Original Antenna  
Figure 1c shows an antenna configuration. We create the antenna using the bent line antenna in Section 2: Each loop gap is chosen to be \(\delta\) = 0 (the two segments of length \(L_{\rm s}\) are replaced with a single one), and a stub of length \(\Delta\ell\) is added to each loop [11]. The antenna is again designed for a CP beam in the +\(z\)-axis direction.  
Figure 5 shows the simulated beam direction and axial ratio versus \(L_{\rm s}\) for \(N\) = 12 and (\(d\), \(P\), \(\Delta\ell\)) = (1.00\(\lambda_{0}\), 0.90\(\lambda_{0}\), 0.12\(\lambda_{0}\)). It should be noted that beam direction remains unchanged at \(\theta\) = 0\({}^{\circ}\) regardless of the variation in \(L_{\rm s}\). It is also found that the minimum axial ratio is obtained at \(L_{\rm s}\) = 0.30\(\lambda_{0}\). These two facts make the antenna design more straightforward when compared with that in Section 2 (see Figure 3).  
We calculate the axial ratio versus periodic number \(N\), as in Section 2. The result is shown with dots in Figure 4. It is emphasized that the axial ratio is almost constant even when the periodic number becomes small, e.g., \(N\) = 4.  
Figure 4: Simulated axial ratio versus periodic number \(N\).  
Figure 5: Simulated beam direction and axial ratio versus segment length \(L_{\rm s}\) for a branched line antenna with \(N\) = 12.  
The simulated radiation pattern for \(N=4\) is shown in Figure 6. Radiation decomposed into right (\(E_{\text{R}}\)) and left-hand (\(E_{\text{L}}\)) CP wave components, which are shown with solid and dotted lines, respectively. It is observed that the antenna radiates a CP beam in the +z-axis direction. The half-power beamwidth (HPBW) and gain are 13\({}^{\circ}\) and 14.6 dBi, respectively. In order to increase the gain, we modify the antenna in the next section.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 4 Experiments  
### Experiment Setting  
The locating experiments were conducted in the natural environment. The test area was open with a size of 150 \(\times\) 150 m\({}^{2}\), and there were no tall reflectors along the boundary of the measurement domain. According to the empirical sound speed formula, the velocity of sound propagation was 343 m/s for an air temperature of 21 \({}^{\circ}\)C. During the experiments, wind speed was very low and the localization range was about 100 m, such that the influence of wind can be assumed negligible. Since it's difficult to keep an LPV going straight and travel at a constant speed, a simulated sound source with a smaller size was used to replace the vehicle noise.  
The simulated source consisted of a 0.1 kW loudspeaker and a power amplifier. The biggest noise sources of LPV were the exhaust system and track system. The track noise was random and nonstatistical. So, the actual measurement of periodic exhaust noise was the sound signal that was collected during the running of the vehicle engine with rotating speed \(r\) = 1200 rpm. The sound signal is shown in Figure 7.  
Figure 6: Distributions of the range error of \(L\).  
Figure 7: Sound signal for moving source.  
The moving sound source travelling at a constant speed was achieved by dragging the loudspeaker with a fixed pulley at a constant rotating speed. The source was traveling along a straight line. By monitoring the distance travelled as a function of time, the speed of motion of the source was obtained.  
In Figure 8, two five-element cross arrays were set according to Figure 4. The distance \(L\) between two central sensors was 10 m, and 2 m for array size \(D\). A NI-PXI system with 10 channels and a sample rate of 248 kS/s was the testing instrument. The array microphones used G.R.A.S with the sensitivity of 40 mv/pa.  
The sound source started moving from point A (\(-\)28.8, 97.92) to B (28.8, 97.92) in the X-Y plane, and then returned back to point A.
## 3 The Carrier-Launcher System Performance: Preliminary Analyses and Results  
This section describes the analysis techniques and the results obtained in the research regarding the carrier-launcher system's overall performance. This investigation is essentially conceptual in nature. Consequently, during the calculations, certain simplifications were applied, and, in certain instances, some data were predetermined to ensure that the outcomes were both conservative and coherent from an engineering standpoint.
## 3 Results and Discussion
### Light-Induced Scattering  
The change curve in Figure 4 demonstrates a negative correlation between the transmitted light intensity and the duration of illumination. As the exposure time increases, there is an initial rapid decline in transmitted light intensity, followed by a gradual leveling off. The increase in Zn\({}^{2+}\) doping concentration can be observed to significantly prolong the stabilization time for transmitted light intensity, particularly when the doping concentration reaches 7 mol%. At this level of doping, the time required for the curve slope to approach zero is extended by an order of magnitude. According to the analysis of the crystal scattering ratio in Figure 5, it can be observed that the square root of the crystal scattering ratio exhibits a positive correlation with exposure time and demonstrates a  
Figure 3: Infrared transmittance spectra of Zn:Cu:Fe:LiNbO\({}_{3}\) crystals.  
According to the Li-site vacancy model, the intrinsic defects Nb\({}_{\rm Li}^{4+}\) and V\({}_{\rm Li}^{-}\) in LiNbO\({}_{\rm 3}\) crystals serve as photorefractive sensitive centers, thereby compromising the resistance ability of light-induced scattering [38; 39; 40]. The enhanced resistance ability to light-induced scattering of Zn\({}^{2+}\) ions is mainly formed into Zn\({}_{\rm Li}^{+}\) by substituting Nb\({}_{\rm Li}^{4+}\), which reduces V\({}_{\rm Li}^{-}\) in order to maintain the charge balance. The non-radiative compound effect of the intrinsic defect on the electron is thus significantly attenuated, thereby enhancing the resistance ability to light-induced scattering.  
With the increase in the Zn\({}^{2+}\) ion concentration, Nb\({}_{\rm Li}^{4+}\) is almost completely replaced, the exposure energy of sample Zn5 reaches 469.71 J/cm\({}^{2}\), and the resistance ability of light-induced scattering was greatly improved. When the concentration of Zn\({}^{2+}\) ions increases to 7 mol%, Zn\({}^{2+}\) ions continue to occupy the normal Li position, and part of Fe\({}^{3+}\) and Cu\({}^{2+}\) ions are crowded out from the Li position to the Nb position. The defective structures of Fe\({}_{\rm Li}^{2+}\) and Cu\({}_{\rm Li}^{+}\) transform into Fe\({}_{\rm Nb}^{2-}\) and Cu\({}_{\rm Nb}^{3-}\), which have weak electron trapping ability; the corresponding photoconductance in the crystal increases sharply, thereby significantly increasing the exposure energy. Due to the high concentration of Zn\({}^{2+}\) ions within the crystal, self-compensating defect clusters of Fe\({}_{\rm Nb}^{2-}-\) Cu\({}_{\rm Nb}^{3-}-\) 5 Zn\({}_{\rm Li}^{+}\) are formed within the crystal, thereby significantly enhancing the resistance ability to the light-induced scattering of Zn:Cu:Fe:LiNbO\({}_{\rm 3}\) crystal. Sample Zn7 is the preferred material for mitigating the light-induced scattering effect, having an Er value of 786.55 J/cm\({}^{2}\), which is approximately 416 times higher than that of Zn1.
## 2 System Model  
In this paper, a road intersection with segments \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\), is considered for route planning, as shown in Figure 1, where each segment is covered by RSU located at the beginning of each segment. The length of each segment is large enough so that to predict early congestion from frequent arrivals to the segment. It is also considered that each segment has alternate routes to a particular destination.  
The arrival and departure of the vehicles to the segment follow a Poison distribution [38], while the speed of vehicles follows a Gaussian distribution [39]. Each vehicle is embedded with an onboard unit (OBU) equipment, which has the same transmission power and Received Signal Strength (RSS) for all vehicles. A Global Positioning System (GPS) unit and IEEE 802.11p radio equipment are embedded inside the OBU. Every vehicle disseminates beacons messages with each other as well as with RSU. The capacity \(C\) of each road segment refers to the maximum number of vehicles on a specific road segment, which is calculated as:  
\[C=\frac{S_{length}}{V_{length}+min_{gap}} \tag{1}\]  
where \(S_{length}\) and \(V_{length}\) are the lengths of the road segment and vehicle, respectively, the \(min_{gap}\) represents the safety distance between the two vehicles. To predict the status of a given road segment \(S_{i}\), the RSU calculates the occupancy rate of the segment as:  
\[O_{i}=\frac{K_{i}}{C_{i}} \tag{2}\]  
In Equation (2), \(O_{i}\) represents the occupancy rate of segment \(i\), while \(K_{i}\) and \(C_{i}\) are the current numbers of vehicles and capacity of segment \(i\), respectively. For congestion detection, RSU computes that if \(O_{i}>\delta\) where \(\delta\in[0,1]\), then segment \(i\) is classified as congested, and \(K\) vehicles such that \((O_{i}-K<\delta)\) will be rerouted using EG-Dijkstra [35; 36] to other routes.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Data Management  
In traditional agricultural and pastoral IoT systems, data storage and management face issues such as susceptibility to attacks, data isolation, and inefficiency. Blockchain technology offers an effective solution: it enhances data security and transparency through distributed ledgers, while the application of smart contracts improves the efficiency of business processes. This not only solves existing problems but also promotes the modernization and digital transformation of agriculture and pastoralism.
## 3 Research Model and Hypothesis Development
### Organizational Factors
#### 3.2.2 Technology Competence  
Technology competence, often referred to as technology readiness, encompasses two critical components: technology infrastructure and employee knowledge and skills [56; 105]. The technology infrastructure of a firm includes its existing hardware and software, which form the backbone for the implementation of Industry 4.0 technologies. A well-established technology infrastructure provides a solid foundation, allowing afirm to leverage existing resources and capabilities, thereby saving both time and money. For instance, technologies such asbig data and simulation require extensive data input. If a firm's current information system can readily provide the necessary data, the implementation process becomes considerably smoother.  
In addition to technology infrastructure, the competence of employees is paramount in the successful adoption of Industry 4.0 technologies [21; 47; 106]. Industry 4.0 demands advanced knowledge and skills from employees to effectively utilize and integrate new technologies into their work processes. Competent employees are better equipped to understand the intricacies of emerging technologies, adapt to changes more swiftly, and collaborate seamlessly with suppliers. Conversely, a lack of competence among employees can hinder the implementation process. Without the requisite knowledge and skills, employees may struggle to grasp the functionalities of new technologies, leading to inefficiencies and potential resistance.  
Furthermore, inadequate technology competence among employees can exacerbate fears of job displacement and resistance to change [62]. Employees may perceive new technologies as threats to their livelihoods, resulting in heightened apprehension and reluctance to embrace innovation. Therefore, fostering technology competence among employees is essential for mitigating resistance and ensuring smooth implementation.  
**Hypothesis 4 (H4)**.: _Technology competence positively affects the implementation success of Industry 4.0 technologies._
## 6 Simulation Results
### Transmission Through an AF Relay Scheme  
For comparison, we consider the operation of the system if \(R\) were an amplify-and-forward relay without any energy harvesting scheme. The SNR of the system for an AF relay scheme is shown in Figure 5 and the drainage of battery for a single cycle (The complete transmission of data from \(S\) to \(R\), covering all the transmission points identified in Figure 4, is referred to as a single cycle. The system is assumed to cover one complete cycle from \((0,1)\) to \((5,6)\) by moving along the y-axes and then return to the initial position while completing the second transmission cycle) of operation is shown in Figure 6.  
Figure 4: Transmission points of \(S\) to \(R\) within the warehouse floor (each dot on the plane shows a transmission point).  
Figure 5: SNR at \(R\) and \(D\) when the switch is implemented with an AF Relay system.  
Through an AF relay-based transmission, the system would be capable of maintaining a higher SNR at \(D\) through the amplification of the signal, yet the energy requirement for amplification process once again creates considerable issues when maintaining system performance.  
The battery drainage model of a Li-ion battery is considered for the study, and within an approximate 5000 s of transmission, it can be seen that the battery drains approximately by 70 mAh. If the system were to transmit continuously, this drainage would create a considerable issue in maintaining the battery charge, since the system shows a drainage of 0.72% per hour. In the case of the proposed system, the battery losses for a single cycle as shown in Figure 6 is considerably low since the transmission takes place within a short time period. This massive drainage occurring through a very short time period also lays the foundation for the requirement of adapting energy harvesting mechanisms.  
The battery performance of the AF relay system demonstrates the inefficiency of using a simple relay model-based transmission scheme within the indoor environment, especially when major focus is put on energy efficiency and zero-energy device implementation, as the requirements of continuing operation with limited battery replacement is hindered in this case.
## 2 Multipath Error Principle and Mitigation Methods
### Generation Principle of Multipath Errors
Two methods for mitigating multipath errors, namely, sidereal filtering and the multipath hemispherical map, leverage the repeating characteristics of multipath errors.
## 1 Introduction
We have previously experimentally characterized the pump noise-induced impairments of an unrepeatered single polarization of a 28 Gbaud 16 QAM link using backward-pumped Raman amplification [22], and for a dual-polarization 28 Gbaud 16 QAM link [23] using bidirectionally pumped Raman amplification. In this paper we report on a systematic experimental characterization of both the amplitude and phase noise of the signal due to RIN transfer from the Raman pump and evaluate the transmission impairments of a coherent DP-16 QAM link using first-order pumped Raman amplification in a standard single-mode fiber (SSMF). We adopt a frequency domain representation approach to reconstruct the noise enhancement due to the Raman pump RIN transfer in both amplitude and phase. Regarding the transmission, we first evaluate the maximum reach, determined by the 7%-overhead hard-decision forward error correction (HD-FEC) threshold, of a backward pumping scheme. We subsequently investigate the transmission improvement one can achieve with the aid of additional forward pumping. For each pumping scheme, the phase noise and amplitude noise are measured as a function of the Raman pump power. Three different carrier phase recovery (CPR) algorithms are tested and compared for each transmission scenario: decision-directed phase-lock-loop (DDPLL) [24], two-stage feed-forward based on QPSK partitioning (QPSKP) with a sliding window average [25] and feed-forward based on blind phase search (BPS) [26; 27]. This paper is organized as follows. In Section 2, we present the experimental setup to evaluate DTRA-induced impairments. Then we show and discuss the obtained results in Section 3. The conclusions are finally drawn in Section 4.
## 3 Experimental Studies
### Data Collection and Preprocessing  
Within the scope of this study, there were LIB samples having tested 750 cycles. In total, there were 192 samples obtained from 21 different LIBs in the dataset. With the preprocessing steps, the first 300 cycles in the dataset were used for training, and cycles between 301 and 750 were omitted from the dataset. The dataset consisted of a total of 5 features and 45.699 data rows, each including the cycle index, temperature, voltage (V), current (A), and discharge capacity (in Ah). These data were divided into 80% training and 20% testing with the cross-validation method. In contrast, 38.845 data were employed for training purposes, while 6.854 data were utilized as a sample for testing. During the training of the network, 15% was reserved for validation purposes. Details on the LIB data used in the study are presented in Table 3.
## 5 P-SV Wave Scattering of Long Lined Tunnel  
### Influence of Seismic Wave Incidence Angle and Conversion Angle on Seismic Response of Tunnels  
The computational model is shown in Figure 16. The tunnel and site parameters are shown in Table 1. Kobe wave is selected for the incident seismic wave record. The acceleration time history curve and spectrum curve are shown in Figure 17. For the incident with the P wave and the SV wave, respectively, the incident angle \(\theta_{v}\) ranges from \(0^{\circ}\) to \(90^{\circ}\), and the conversion angle \(\theta_{t}\) ranges from \(0^{\circ}\) to \(90^{\circ}\). The increment interval between the incident angle and the conversion angle is \(15^{\circ}\). The external surface seismic responses of tunnel lining are calculated under different incident angles and conversion angles.  
At dimensionless frequency \(\eta=0.132\), Figure 18 shows the variation of radial displacement when the P wave is incident at different incident angles and conversion angles. With the gradual increase of the incident angle, the position of the maximum radial displacement gradually shifted from the vault to the haunch. Except for vertical incidence, for the same incidence angle, the radial displacement of the tunnel lining increases gradually with the increase of the conversion angle. The radial displacement values of the SV wave at different incident angles are given in Figure 19. At this time, with the increase of the incident angle, the position of the maximum radial displacement is gradually shifted from the haunch to the arch bottom. For the axial displacement, Figure 20 shows that when the P wave is incident and the conversion angle is fixed, the axial displacement increases gradually with the increase of the incident angle. In the case of the same incident angle, the larger the conversion angle, the smaller the radial displacement. When the SV wave is incident and the conversion angle remains the same, as the angle of incidence increases, the radial displacement decreases gradually in Figure 21. Therefore, when the incident angle is large, the longitudinal seismic response of the tunnel should be given enough attention. With the  
Figure 16: The computational model.  
Figure 17: Kobe seismic wave (**a**) acceleration time history and (**b**) spectrum.  
change of the transfer angle, the position of the maximum seismic response of the tunnel in the cross section is changed.  
Figure 18: Radial displacement of lining surface under oblique incidence of P wave at 0–90\({}^{\circ}\).  
Figure 19: Radial displacement of lining surface under oblique incidence of SV wave at 0–90\({}^{\circ}\).  
Figure 20: Axial displacement of lining surface under oblique incidence of P wave at 0–90\({}^{\circ}\).
###### Abstract
Interrelated processes exert control over sediment transport and deposition: (1) tidal pumping, (2) baroclinic circulation, (3) particle trapping in the turbidity maximum, (4) flocculation, (5) the mangrove tidal prism, (6) physiochemical reactions that destroy cohesive flocs, and (7) microbial production of mucus. Sedimentation of particles is often rapid in quiescent mangrove environments, resulting in a global mean carbon sequestration rate of \(179.6\pm 16.4\) gC m\({}^{-2}\) a\({}^{-1}\)[3]. Over decades, after initial colonization of a mudflat, the forest develops and the floor builds up further, adjusting to sea-level, subsidence, and uplift, with the net result being several meters of soil [9]. Over time, these deposits are penetrated further by mangrove trees and their extensive root systems, various other flora (e.g., microalgae) and fauna, especially burrowing crabs, and highly abundant and productive microbial communities [10]. The forest floor is thus ordinarily pockmarked with mounds, burrows, tubes, cracks, fissures, coarse and fine living and dead roots and root hairs, and layered with decayed wood, litter, crawling epifauna, and various forms of micro- and macroalgae. Mangrove deposits contribute about 30% to carbon storage on the coastal margins in subtropical and tropical latitudes [11]  
Mangrove ecosystems similarly contribute a disproportionate share of various forms of carbon to the coastal ocean in low latitudes. Although they occupy only 1.5% of the world's subtropical and tropical coastal ocean area, mangroves account for about 5% of net primary production and 12% of ecosystem respiration. How much mangrove particulate carbon (i.e., litter) is exported to adjacent waters is well-known [7; 12], but how much mangrove ecosystems contribute to dissolved inorganic carbon (DIC) discharging into low latitude coastal waters is unclear [11], as are the amounts of carbon gases released from mangrove waters to the atmosphere. How much soil carbon is mineralized throughout the entire soil horizon is poorly understood, with the current knowledge based mostly on measurements of oxygen consumption and DIC and CO2 production at the surface soil-air/water interface. The functional link between these processes within the forest floor and the adjacent coastal zone has remained similarly unquantified until only very recently [13]. This paper critically examines the traditionally measured rates of soil respiration and compares these data with rates of belowground DIC production, highlighting any discrepancies to accurately estimate rates of CORG mineralization throughout the deep forest floor. This information is then compared with recent estimates of DIC export via subsurface pathways to determine its significance in mangrove carbon cycling and the functional links between the mangrove forest floor and the adjacent coastal ocean. A revised carbon mass balance for the world's mangroves is then presented to identify the major and minor pathways of carbon flow as well as what further empirical measurements are needed to improve our knowledge of carbon balance in these tidal ecosystems