esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.5 Extracting Crop Phenology Using RS Data  
Emerging (EM) and harvest (HV) dates of each pixel in each year were retrieved from the 8-day and 1 km time-series NDVI data using TIMESAT3.3. The Savitzky-Golay filter was used to denoise the NDVI time series. The EM and HV dates were estimated on the basis of the denoised NDVI time series, and they corresponded to the start and end of the season (SOS and EOS, respectively) defined in TIMESAT. The key parameters required were set as follows:  
Figure 3: An example of deriving \(\text{LAI}_{\text{P}}\) from RS-LAI and \(\text{C}_{\text{REF}}\). \(\text{LAI}_{\text{P}}\) on the right panel (**b**) was derived by fitting the RS-LAI on the left panel (**a**) to \(\text{C}_{\text{REF}}\) using the least-square method.  
Window size = 64 days,
* Start of season method = relative amplitude, and
* Season start/end value = 0.1/0.2,  
where \"window size\" means the total nearest days that are used to denoise the current data; \"relative amplitude\" indicates that the SOS or EOS was estimated as the time when NDVI increases or decreases to a given proportion, as specified by the season start or end value, of the relative amplitude of NDVI time series during a specific growing season.
## 3 Unit Cell Analysis
### 1 \(\times\) 2 Array  
The geometry of the 1 \(\times\) 2 array of the proposed MTM unit cell is shown in Figure 4a. Figure 4b shows the magnitude of effective permeability. The frequency range of negative permeability (\(\upmu\)) is from 7.9 GHz to 12.3 GHz. The negative permittivity (\(\varepsilon\)) is exhibited from 2.8 GHz to 4.85 GHz and 7.985 GHz to 9.986 GHz, as shown in Figure 4c. Figure 4d depicts the magnitude of the refractive index over frequency. The negative refractive index (\(\upmu\)) is shown from 7.950 GHz to 14.50 GHz, covering a bandwidth of 6.55 GHz. The configuration is an open array and in the region between 7.98 GHz and 9.986 GHz all the electromagnetic properties show negative peaks.  
Figure 3: Unit cell characteristics (**a**) Surface current distribution at 8.4 GHz (**b**) Permeability (**c**) Permittivity (**d**) Refractive index.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 3 Test Analysis
### Choosing of Constellations and Ground Monitoring Stations
#### 3.1.3 Coverage Analysis of Multi-Source Observation Links  
For the constellation with 24 BDS-3 MEO satellites and 60 LEO satellites, the coverage capability of the multi-source observation links was evaluated.  
The test results showed that:  
1. As shown in Figure 8, the coverage ratio of more than one satellite-to-ground observation link was less than 40%.
2. As shown in Figure 9, one MEO satellite could be constantly tracked by the ground monitoring stations and six LEO satellites. The six LEO satellites were located in six orbital planes, respectively. The maximum coverage number was 12.
3. As shown in Figure 10, the number of observation links could be effectively improved by adding six more LEO satellites. The minimum number of observation links was six, meeting the requirements of precise orbit determination.  
Figure 6: Distribution of the chosen stations (simulated data).  
Figure 7: Distribution of the chosen stations (real data).
## 6 Experimental Verification
### Parameter Identification at Fixed Speeds  
Before the temperature estimation was conducted, the parameters of the state equation were identified. The motor was operated under varying load conditions at 0, 1000, 2000, 3000, 4000, 5000, and 6000 rpm. According to the theoretical analysis in Chapter 4, the parameters of the state equation were identified at each fixed speed based on the multiple linear regression algorithm. By linearly fitting these six sets of parameters, the thermal network model parameters at 7000 rpm were obtained as shown in Equation (31):  
\[\beta=\left[\begin{array}{cccccc}-0.0060&0.0021&-0.0030&0.0102&5.5674\times 1 0^{-4}&0\\ -2.5496\times 10^{-4}&-0.0024&0.0030&0&0&2.6862\times 10^{-4}\\ 0.0014&4.5603\times 10^{-5}&-0.0058&0.0051&0&0\end{array}\right]. \tag{31}\]  
At 7000 rpm, based on the temperature estimation algorithm of the state equation, the stator, rotor and end cap temperatures were estimated by linear fitting using the thermal network model parameters  
Figure 7: Experimental platform: (**a**) Temperature sensor distribution; (**b**) temperature acquisition equipment; (**c**) wireless transmitting and receiving devices.  
at 7000 rpm. The experimental conditions are shown in Table 2. The result of the fitting parameter estimation was compared with the actual measured result from the sensor, and the comparison is shown in Figure 8.  
It can be seen from the experimental results that with the torque changes, the result of the fitting parameter estimation keeps the same trend as the measured result of the sensor, indicating that the thermal model can accurately predict the motor temperature change.
# Assisted Migration Field Tests in Canada and Mexico: Lessons, Limitations, and Challenges
## 7 Mitigating Risks
### Outbreeding Depression  
Genetic risks of assisted migration outside of range include outbreeding depression, disruption of local adaptation by non-climatic factors, and population replacement [40]. Outbreeding depression is only likely in wind-pollinated tree species when individuals are moved between long-diverged populations, e.g., subspecies or varieties. If outbreeding depression exists but is weak, natural selection will remove deleterious immigrant alleles over time, allowing mean fitness to recover and surpass that of the local population, according to population simulation modelling [40]. Genetic incompatibilities causing outbreeding depression are unlikely in widespread forest tree species such as those discussed here. Field trials and good knowledge of the biology and silvics of species can provide information about local adaptation to environmental factors such as soil type, pests, pathogens, or mutualists. If translocated individuals have higher fitness than the resident population, and if the population is naturally regenerating rather than being planted, over time the genetic composition of the population may gradually be replaced by the introduced genotypes. As this will increase the average fitness of the local population, this replacement is more likely a benefit than a risk.  
Furthermore, to reduce ecological risks, care should be taken to avoid unintended introductions of species hitch-hiking with planted seedlings and their soil, as hitch-hikers, non-pathogenic in their native environment, may become pathogenic on naive hosts in their new environments.
## 1 Introduction
Moreover, despite a very limited number of studies comparing seawater salt stress to other salinity stress [24], the tolerance to seawater seems to be remarkably higher compared to that for NaCl solutions at comparable electrical conductivity (EC) [22]. Despite such fundamental differences, most available scientific literature still considers seawater to have the same detrimental effects of a salt solution, thereby underestimating the potential above-mentioned benefits. In fact, nonconventional water sources (i.e., saline water) have been successfully used in agriculture, especially in the MENA region, showing a potential in rehabilitating the marginal and degraded lands for meeting the food fodder demands [25]. Since, as stated above, a screening considering the role of different salt types on determining specific salinity stress conditions for plants has rarely been conducted, the current study was carried out with the aim of evaluating the differences in salt tolerance in one halophyte and one glycopre species grown with two different types of saline media (i.e., NaCl solution and diluted seawater). The New Zealand spinach (_Tetragonia tetragonioides_ ([PERSON]) was chosen among the edible halophytes because it is already consumed in several regions, although cultivated on a very small scale [26], and, thus, is able to become a new staple food for saline environments [11]. Lettuce (_Lactuca sativa_ L.) was chosen because is one of the most cultivated horticultural crops, focusing in particular on the largely diffused variety 'Canasta'. In the current trial, we tested the hypothesis that due to different mineral composition, seawater would actually result in a less harmful growing medium for both crops than NaCl.
# Customized Convolutional Neural Networks Technology for Machined Product Inspection
## 5 Conclusions  
This research combines industrial machine vision and deep learning, and deploys the machined metal defect detection for smart manufacturing and reduced production costs. The three main contributions are as follows. First, we provided a quick layer structure solution by feeding the initial dataset to some well-known small, medium, and large CNN layers with only one epoch simulation. This sped up the selection of the number of CNN layers with an appropriate CNN backbone size. Second, the customized light source with a shadowless ring light used in this experiment to illuminate the metal workpiece successfully solved the problem of easy reflection on the metal surface without affecting the feature extraction of the measured object with deep learning technology, where the traditional AOI algorithm did not fully meet the needs of this detection. Thirdly, we used a self-built custom convolutional neural network model, replacing pooling layers and fully connected layers with convolutional layers only. The proposed CNN minimized the number of parameters while maintaining extremely high performance and retained remarkable accuracy. The experimental results showed that, after comparing the customized convolutional neural network model designed in this study with the ResNet-34, VGG-19, DarkNet-19, and LeNet models, our model achieved 99.36% accuracy, which was better than that of other models. Additionally, the Grad-CAM module validated the last layer of the convolution result. The highest accuracy could effectively improve the recognition rate of defective and non-defective samples, and achieve the effect of reducing training costs. The results of this paper contribute to the technological development of automatic optical inspection, and also contribute to intelligent manufacturing. These findings for how to customize a CNN are expected to enable the development of visual inspection techniques with high adaptability  
Figure 9: Grad–CAM-visualized results with NG images for (**a2–c2**), where the (**a1–c1**) are the originally captured images.  
to overcome the bottlenecks of current image processing techniques and advance the advancement of manufacturing processes.  
Conceived, [PERSON] and [PERSON]; wrote the paper, [PERSON]; methodology, [PERSON], [PERSON]-[PERSON], [PERSON] and [PERSON]; software, [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
MOST 109-2221-E-005-082-MY2 and MOST 110-2218-E-005-018.  
Not applicable.  
We thank the Ministry of Science and Technology for supporting Project No. MOST 109-2221-E-005-082-MY2 and MOST 110-2218-E-005-018 for this research.  
The authors declare that there is no conflict of interest regarding this publication.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Traffic Priority Effects on the HSN  
#### 5.3.1 Simulation  
In this section, the primary goal is to validate the servicing of various traffic priorities. The number of backbone network nodes and access network nodes, along with the traffic buffers, are set to identical values, as specified in Section 5.2.1. The difference from the  
Figure 12: Performance comparison between the HSN and FC.  
previous section is that each transmitting node is configured with four types of traffic with traffic priorities ranging from 1 to 4.
## 2 Experimental Study
### Frequency Domain Analysis  
Figure 8 shows the frequency spectra of ten test runs conducted on each of the two conditions. In condition A, all the spectra are similar, and a dominant frequency of approximately 690 Hz is observed. The amplitudes increase steadily between 0 and 500 Hz. In condition B, larger differences among the tested spectra are observed. The dominant frequency remains the same (\(\sim\)690 Hz) as that of condition A. Besides, the amplitude fluctuates between 0 and 500 Hz with two additional peaks between 300 and 500 Hz. For each condition, the average frequency spectra of these test runs are calculated, except for the incoherence test runs (test 1 of condition A and test 4 for condition B), as shown in Figure 8c. The  
Figure 6: Number of boxes with side length \(\delta\).  
Figure 7: The fitting lines of (\(-\)lg\(\delta-\)lgN).  
amplitude of condition B is higher than that of condition A in the frequency range of 800 Hz-1100 Hz. There are more dominant frequencies in condition B.  
The dynamic stiffness was also used to study the influential differences between the two conditions. It expresses the resistance to deformation under the impact force [16]. In this test, dynamic stiffness reflects the supporting performance of the tunnel-soil structure. The impact force \(F(t)\) and the obtained acceleration response \(a(t)\) can be transformed into the frequency domain, using Fourier transform, as \(F(f)\) and \(a(f)\), respectively. The acceleration response is expressed as the superposition of a series of simple harmonic vibrations, so the amplitude of velocity can be represented as:  
\[V(f)=\frac{a(f)}{2\pi f}, \tag{5}\]  
The velocity admittance is defined as:  
\[G_{\mathrm{v}}=\frac{V(f)}{F(f)}, \tag{6}\]  
Then, the dynamic stiffness can be calculated by:  
\[K_{\mathrm{d}}=\frac{2\pi f}{G_{\mathrm{v}}}=\frac{2\pi f\cdot F(f)}{V(f)}, \tag{7}\]  
Figure 9 compares the \(K_{\mathrm{d}}\) of the two conditions. The \(K_{\mathrm{d}}\) in condition B is lower than condition A. The reason is that the wave propagation in condition B is approximated as guided by a free-free boundary condition while the wave in condition A is guided by a free-constrained boundary condition. This phenomenon shows that the void existence can significantly reduce the dynamic stiffness in most frequencies, which means it reduces the ability of the tunnel structure to resist the load.  
Figure 8: Fourier spectra for two conditions.
## 3 System Model and Problem Formulation
### The MDP Model  
In this study, our objective is to control the trajectories of drones so that they provide maximum coverage for the UEs and minimize the consumption of energy from the drones. The drones must dynamically adjust their positions based on the distribution of the rescuers and the environmental conditions. The drone makes a decision in each time slot, and the decision in time slot \(t\) only depends on the scenario information at time \(t-1\). This satisfies the Markov property, and the information observed by each drone is local; thus, we can model the problem as a partially observable Markov decision process (POMDP) [21]. The POMDP can be described as a tuple \(<\mathcal{U},\mathcal{S},\mathcal{O},\mathcal{A},P,\boldsymbol{\pi},R,\gamma>\); here, \(\mathcal{U}=1,2,\ldots,U\), \(\mathcal{S}\), \(\mathcal{O}=o^{1},\ldots,o^{U}\), and \(\mathcal{A}=\mathcal{A}^{1}\times\ldots\times\mathcal{A}^{U}\) are the set of corresponding drone agents, global state, the set of observations, and joint action, respectively. \(P\) represents the transition function, and \(\gamma\in[0,1)\) is the discount factor. At each time step, agent \(u\) receives observation \(o^{u}\) and provides action \(a^{u}\in\mathcal{A}^{u}\). The details of the fundamental elements of our problem are provided below.  
* Agents: The agents correspond to the drones. Each agent has an actor network, which determines the agent's action based on the input observation at each time step.
* Observations: The local observation information of each drone includes the position coordinates of the drone and the UEs it serves, the current energy level of the drone, and the system coverage value.
* States: There are many different input information modes to choose from [22]. In our study, the state fed into the algorithm consists of the local observations of all agents, which are combined into a global state representation.
* Actions: The action space of the drones is continuous, and this allows each agent to take actions in any direction and at any distance. The action of each agent is represented as a two-dimensional vector, \((\Delta x,\Delta y)\), which is determined at each time step by the actor network.
* Reward: The algorithm receives states, actions, and outputs rewards. Our study implements reward sharing, which implies that the total reward of all drones is used as the reward for each drone. The specific method for calculating rewards is introduced later.
* Policy \(\pi\): The policy \(\pi\) determines the actions to be taken by the drones based on the current state, aiming to maximize the cumulative reward over time.
## 2 System Model of GCI-DCSK SWIPT  
CI-DCSK SWIPT schemes [17] employ a one-to-one mapping between subcarrier activation patterns and index symbols, where the number of subcarriers \(n\) is chosen as \(2^{p_{1}}\) (\(p_{1}\) being the number of index bits) and the number of active carriers \(p_{2}\) can only be 1 or \(n-1\). Hence, both the performance and the flexibility of the system are limited. To overcome the disadvantages of CI-DCSK SWIPT, GCI-DCSK SWIPT which can flexibly adjust the number of index carriers and active carriers is studied. Moreover, the path loss and the conversion noise are also taken into consideration.
# Phase-Modulated Continuous-Wave Coherent Ranging Method and Anti-Interference Evaluation
## 1 Introduction
Moreover, we proposed a modified model for ranging precision. Furthermore, using the PhMCW as an example, we proposed two sets of anti-interference capability experiments against ToF and coherent LiDARs. Through an analysis of the differences in the measured precision with and without interference through the Ljung-Box method, we arrived at a quantitative conclusion about the anti-interference capability of LiDARs. The \(p\)-values of the PhMCW system were 0.0589 and 0.6327 with ToF and coherent LiDAR interference, respectively, demonstrating the excellent capability of the PhMCW against interference. Moreover, the proposed method can be applied to all types of lidar systems. To the best of our knowledge, this is the first quantitative method to evaluate the anti-interference capability of LiDARs.
## 1 Introduction
The researchers in [30] mentioned that Flip-OFDM constructions are time-compression types of classical Flip-OFDM. The model outcomes indicated that this time compression significantly enhances both efficiency bandwidth and DC electrical power. Moreover, in [31], the authors proposed a Flip-OFDM system employing channel coding to ameliorate the signal-noise ratio, while in [32], the researchers enhanced the system performance using an iterative receiver for the Flip-OFDM scheme. On the other hand, the authors in [33] examined the Flip-Wavelet Packet Modulation (Flip-WPM) as a substitutional method for Flip-OFDM to produce a unipolar signal. The study showed that Flip-WPM required less computational difficulty and was evenly power-efficient. Recent studies [34; 35; 36] have presented the conception of filtered OFDM (F-OFDM). F-OFDM divides the bands of the OFDM signal into several sub-bands, each configured with a specific configuration, and applies filtering to these sub-bands [35]. This approach offers advantages over traditional OFDM, including better OOB suppression, support for asynchronous transmission, PAPR reduction, enhanced efficiency spectral, and simplified designing [34]. This study aims to boost spectral efficiency, degrade system intricacy, and explore the effect of the PAPR by implementing a new scheme based on Flip-Filter-OFDM. This approach generates real signals corresponding with IM/DD systems without requiring the usage of Hermitian symmetry, thereby reducing the intricacy and consumption of power in the system while improving spectral efficiency. The rest of this article is structured as follows: Section 2 comprehensively describes the proposed Flip-Filtered OFDM scheme and its mathematical formulation, along with a comparison to the conventional technique. Section 3 presents and analyses the simulation results, focusing on bandwidth efficiency, signal-to-noise ratio (SNR), bit error rate (BER), computational complexity, and PAPR effect. Lastly, Section 4 reviews the main outcomes and suggests concluding remarks.
## 3 Results and Discussion  
### Synthesis and Characterization  
In the first step, commercially available dimercaptolaneonitrile disodium salt and \(o\)-phtalonitrile were applied to [PERSON]'s macrocyclization reaction conditions, which led to magnesium(II) tribenzoporphyrazine with 4-(3,5-dibutoxycarbonylphenoxy)butylthio substituent **3** (Scheme 1) [26; 27]. This macrocycle was subjected to the reduction reaction with LiAlH\({}_{4}\), which led to magnesium(II) tribenzoporphyrazine with 4-[3,5-di(hydroxymethyl) phenoxy]butylthio substituent **4** [28]. Tribenzoporphyrazine derivatives **3** and **4** were obtained according to the previously published procedure and purified using flash column chromatography [20]. Macrocycle **3** was treated with trifluoracet acid, leading to the demetallated derivative [21] and then was subsequently remetallated with zinc(II) acetate toward zinc(II) tribenzoporphyrazine with 4-(3,5-dibutoxycarbonylphenoxy)butylthio substituent **6**. Next, Pz **6** was subjected to the reduction reaction with LiAlH\({}_{4}\), which allowed zinc(II) tribenzoporphyrazine with 4-[3,5-di(hydroxymethyl)phenoxy]butylthio substituents **7** to be obtained. The new zinc(II) tribenzoporphyrazines **6** and **7** were characterized using UV-Vis spectroscopy, various NMR techniques (\({}^{1}\)HNMR, \({}^{13}\)CNMR, \({}^{1}\)H-\({}^{1}\)H COSY, \({}^{1}\)H-\({}^{13}\)C HSQC, and \({}^{1}\)H-\({}^{13}\)C HMDC), and mass spectrometry. The NMR data concerning the analysis of chemical shifts and key observed correlations are included in the Supplementary Materials.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 3 Methodology
### Dimensioning  
#### 3.3.1 Coverage Dimensioning  
Following the selection of the path loss model, the next crucial step is conducting thorough coverage and capacity calculations for the base stations. These calculations ensure sufficient coverage and capacity to meet user demand. Coverage calculation begins with a detailed link budget analysis, accounting for signal propagation and attenuation factors such as body loss, penetration loss, foliage loss, and more. The Maximum Allowable Path Loss (MAPL) is determined using the link budget equation:  
\[MAPL=EIRP+AG(ReceivedPower(PRx)+BL+PL+FL+RIL+SLF+IM) \tag{11}\]  
where BL = Body loss, PL = Penetration loss, FL = Foliage loss, RIL = Rain/ice loss, SLF = Slow fading, RS = Receiver sensitivity, IM = Interference margin, and AG = Antenna gain.  
The MAPL represents the maximum permissible power level for successful communication. Using the MAPL and the 3 GPP 38.901 path loss model, the coverage radius of the base station is determined.  
Assessing the actual area helps calculate the total number of base stations required for complete coverage. Considering real-world conditions ensures effective network design tailored to specific coverage requirements [38].  
Eventually, the number of RRUs needed to cover the area of interest is expressed as follows:  
\[N_{Cov}^{i}=\left\lceil\frac{S}{S_{Cell}^{i}}\right\rceil\]  
where the symbol \(\lceil.\rceil\) denotes the ceiling function and \(S_{cell}^{i}\) is the surface of the cell with radius \(RBS^{i}\). For instance, \(S_{cell}^{i}=\pi*(RBS^{i})^{2}\) for a circular cell and \(S_{cell}^{i}=\frac{3\sqrt{3}}{2}(RBS^{i})^{2}\) for a hexagonal cell.