esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Set-up  
A VFF-HFF biochar filter was installed and filled with biochar originating from a mixture of pine and spruce wood biomass obtained from Vindelkol AB (Vindelkol, Umea, Sweden). The HFF and VFF sections comprised of two 70-L boxes, each measuring 74 cm \(\times\) 29 cm \(\times\) 40 cm (height \(\times\) width \(\times\) depth), placed on top of each other. In the VFF, a 3-cm drainage layer of coarse biochar (8-16 mm particle diameter) with slope 2.5% was installed at the bottom. The section was then packed up to 30 cm with biochar with particle size 2.5-5.0 mm. A second 3-cm layer of coarse biochar was placed on top of the main filter, to function as a distribution layer and prevent clogging on the surface by wastewater solids. The HFF biochar filter was prepared by filling the box with coarse biochar (25-40 mm particle diameter) in two 10-cm layers at the inlet and outlet sides. The remaining 54 cm of the section was filled with fine biochar (1.6-2.5 mm diameter). The depth of biochar in the HFF was 30 cm and the outlet was located 2 cm lower than the inlet. Before the start of the experiment, the filter was gently washed with distilled water.  
The specific area of the biochar was determined according to [PERSON] [24] and found to be 170-220 m\({}^{2}\) g\({}^{-1}\). The internal structure and topography of the packed biochar were studied using elemental scanning electron microscopy (SEM). SEM micrographs and energy dispersive X-ray spectrographs (EDS) of the samples were obtained using a HITACHI TM-1000 scanning electron microscope equipped with an Oxford Instruments EDX detector. The SEM image of the biochar showed longitudinal hollow tubes with pore size ranging from 30 to 60 microns (Figure S1 in Supplementary Information).  
The filters were fed with raw wastewater, which was brought in from Uppsala wastewater treatment plant (Kungsungswerket) on a weekly basis and stored refrigerated (4 \(\pm\) 2 \({}^{\circ}\)C) before use. Prior to each feeding, the refrigerated wastewater was homogenized and the required doses were then pumped from the storage container to distribution containers kept at room temperature (20 \({}^{\circ}\)C). After acclimatization to room temperature for 20 min, the wastewater doses were pumped from the distribution containers to the filters, using a peristaltic pump. When removal of _Salmonella_ spp. was investigated, the cultivated _Salmonella_ was added to the distribution containers while the wastewater was acclimizing.  
Figure 1: Combined aerobic vertical flow filter (VFF) and anaerobic horizontal flow filter (HFF) system for nitrogen removal from wastewater. The material in the filter was biochar made from pine and spruce wood biomass.
## 3 Methods
### Geographically Weighted Regression Model  
Many empirical studies have examined the influencing factors of life expectancy by using various methods including econometric model, principal component analysis, Granger causality test, and random forests [49; 50; 51]. Despite the suitability of these methods for the research purposes of these quality works, they have commonly \"averaged\" the parameters. However, if spatial autocorrelation exists in the dependent and independent variables, the assumption of independence of the residuals in the traditional regression model is violated, and the ordinary least squares (OLS) method is no longer applicable. Tosolve this problem, the geographically weighted regression (GWR) model considers spatial non-stationarity for parameters estimating, so it performs local estimation not uniformly. Thus, it is generally used when assuming the relationship between independent variable and dependent variables variates with spatial position [52]. Thus, this paper attempts to explore the spatially heterogeneous influencing factors of life expectancy extension by employing the geographically weighted regression (GWR) model.  
Following is the regression model:  
\[Y_{i}=\beta_{0}(u_{i},v_{i})+\sum\beta_{k}(u_{i},v_{i})x_{ik}+\varepsilon_{i} \quad i=1,\,2,\ldots n \tag{2}\]  
where \((u_{i},v_{i})\) represents the spatial coordinate of \(i\)-th point, and \(\beta_{k}(u_{i},v_{i})\) is estimated value of the function \(\beta_{k}(u,v)\) at \(i\)-th point. Insignificant fluctuation of \(\beta_{k}(u_{i},v_{i})\) value indicates spatial stationarity; \(\varepsilon_{i}\) refers to the random error of \(i\)-th point.  
Compared with the OLS model, the GWR model has the following advantages: (1) when dealing with spatial data, it can solve the spatial autocorrelation problem that cannot be solved by OLS model, and the parameter estimation and statistical test of the model are more reliable and have smaller residuals; (2) each sample spatial unit corresponds to a specific coefficient value, which makes the model results more reflective of the local situation and can restore the local characteristics of the relationship between variables that is ignored by the OLS model; (3) the parameter estimates of the model can be reflected on the map through using GIS software, which facilitates further construction of geographic models and exploration of spatial variation characteristics and spatial patterns.  
Moreover, in the regression of each point, the GWR model considers the interrelations between local and surrounding points, which demands a spatial weight matrix among sample regions. Adaptive bandwidth of the quadratic kernel function and the modified AIC were employed for regression modeling in this study. The estimation results and coefficient distribution map were processed by MGWR 2.0. The impact direction and degree of each variable on the change in life expectancy of different regions could be reflected by the symbol and magnitude of estimated regional coefficients.
## 2 Feasibility Analysis and Modeling of Energy Storage in Transmission Network Expansion Planning  
### Analysis of the Non-Wires Alternative Effect of Energy Storage  
As a crucial regulatory resource in future power systems, energy storage can reduce the system's peak-valley difference through charge and discharge behaviors. This can delay the upgrade and renovation of transmission lines, increase the acceptance capacity of renewable energy, and improve equipment utilization. In this paper, energy storage resources that achieve these effects are defined as grid alternative energy storage. Grid alternative energy storage, as a non-wires alternative (NWA) solution, is coordinated with transmission network planning to improve transmission line utilization and increase new energy consumption capacity.  
As an important function of alternative energy storage in the grid, the demonstration of the role of deferred transmission line construction is important for reducing system costs. Under traditional planning methods, the transmission and distribution network needs to invest a large amount of power assets to expand or upgrade lines with the purpose of meeting peak load demand periods. However, peak loads occur for only a short period each year, leading to low utilization of the newly built parts. Additionally, the expansion or new construction process is constrained by various factors such as the environment and transmission corridors. Configuring energy storage devices can reduce the peak load at this point, thereby delaying the upgrade of grid lines and improving the utilization rate of existing equipment. The principle is shown in Figure 1.  
From Figure 1, it can be observed that the system's peak load is gradually increasing. Assuming the maximum transmission capacity of this node is \(\mathit{l}_{1}\), expansion planning will be required in \(\mathit{y}_{2}\). When energy storage is deployed at this node in \(\mathit{y}_{2}\), the peak load of the node decreases from \(\mathit{l}_{1}\) to \(\mathit{l}_{2}\), ensuring that the peak load is below the maximum transmission capacity of the line. After \(\Delta n\) years, the peak load will return to \(\mathit{l}_{1}\). The schematic diagram of the role of energy storage in delaying transmission line upgrades shows that the value of grid alternative energy storage in delaying transmission line upgrades is essentially the time value of delaying the capital needed for equipment construction. The benefits of grid alternative energy storage devices in delaying transmission line upgrades are one of the important indicators that need to be quantified in the project planning process.
## 6 COTS Hardware Components  
The CHP2 system is implemented using COTS components and a custom transmit-receive switching board. This TR board was already available and was used for convenience, but could easily be replaced by various off-the-shelf alternatives or by simply using different paths for the transmit and receive antennas. A block diagram of the hardware platform is depicted in Figure 6.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Amble Length & 128 & chips \\ Payload Length & 8192 & chips \\ Nav Sequence Length & 1000 & chips \\ \hline Bandwidth & 10 & MHz \\ Sampling Rate & 40 & MHz \\ Carrier Frequency (US) & 915 & MHz \\ Carrier Frequency (EU) & 783 & MHz \\ Transmit Power & \(<\)30 & dBm \\ \hline Amble Code & Gold Code, (\(\chi^{7}+x+1\)) & – \\ Spread Code & [1,\(-\)1,\(-\)1,\(-\)1,\(-\)1,\(-\)1,\(-\)1,\(-\)1] & – \\ Pulse Shaping Filter & Raised Cosine, \(\beta=0.25\), \(N=16\) & – \\ \hline Amble Modulation & BPSK & – \\ Payload Modulation & BPSK & – \\ Navigation Modulation & MSK & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: CHP2 prototype waveform parameters.  
Figure 5: Depiction of the transmissions sent from each antenna for a single transmit frame. The current implementation only uses one antenna for the communications tasks but future implementations will use all available antennas to enable MIMO capabilities. The navigation sequences are transmitted from each antenna following a simple tim-division strategy.
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 3 Results and Discussion
### Lettuce Growth  
The increased salinity level of irrigation water significantly affected the length and width of leaves among the various treatments (\(p<0.05\)) (Figure 3). In both the first and second phases of the experiment, the lowest values of leaf length and width were observed in BS#04. The effect of saline water irrigation on lettuce growth increased with time, as significant differences in leaf length and width were identified in BS#03 during the second phase of the experiment (Figure 3). However, the number of leaves was less affected by saline irrigation water, presumably because the level of salinity, which affects the number of lettuce leaves, is higher than the salinity level that affects leaf length and width. Similarly, [PERSON] et al. [22] reported that the number of lettuce (cv. Vera) leaves was not affected by salinity treatments ranging between 0.80 and 4.72 dS m\({}^{-1}\). As shown in Figure 4, there were no significant differences in the number, length, and width of leaves among the treatments (\(p>0.05\)) that used desalinated irrigation water. This result suggests that the use of desalinated water does not negatively affect lettuce growth.  
Figure 3: Effect of saline irrigation water on (**a**) number of leaves, (**b**) leaf width, and (**c**) leaf length. Bars represent the mean \(\pm\) standard deviation, \(n=20\). Different letters indicate significant differences among treatments by [PERSON]’s honest significant difference test at \(p<0.05\). BS: before water treatment using the BWD system, i.e., saline water irrigation.
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
## 4 Verification on Benchmark Datasets
### Classification
We are unable to explain the cause of this inconsistency at present, which requires more research. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Algorithm** & **Correct Rate** & **Training Time/s** & **Testing Time/s** \\ \hline \multirow{3}{*}{Wine} & KELM & 95.56\% & 0.0006 & 0.0002 \\  & DKELMs & 97.78\% & 0.0002 & 0.0001 \\  & C-SVC & 93.33\% & 0.0007 & 0.0002 \\ \hline \multirow{3}{*}{Handwritten digits} & KELM & 98.15\% & 3.7564 & 0.3898 \\  & DKELMs & 98.51\% & 0.6444 & 0.2424 \\  & C-SVC & 97.42\% & 0.3605 & 0.2121 \\ \hline \multirow{3}{*}{Iris} & KELM & 94.74\% & 0.0008 & 0.0001 \\  & DKELMs & 97.37\% & 0.0004 & 0.0001 \\  & C-SVC & 92.11\% & 0.0005 & 0.0001 \\ \hline \multirow{3}{*}{Page} & KELM & 95.76\% & 0.6667 & 0.0931 \\  & DKELMs & 96.49\% & 0.3854 & 0.0500 \\  & C-SVC & 95.91\% & 0.1211 & 0.0212 \\ \hline \multirow{3}{*}{Segment} & KELM & 96.71\% & 0.1071 & 0.0210 \\  & DKELMs & 97.05\% & 0.0644 & 0.0184 \\  & C-SVC & 96.88\% & 0.0419 & 0.0067 \\ \hline \multirow{3}{*}{Vehicle} & KELM & 84.36\% & 0.0109 & 0.0023 \\  & DKELMs & 84.83\% & 0.0090 & 0.0010 \\  & C-SVC & 83.89\% & 0.0553 & 0.0027 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of distributed KELM on classification benchmark datasets. Figure 4: Comparison on correct rate and testing time in classification benchmark datasets. (Red, blue and green respectively represent the original KELM, C-SVC and DKELMs algorithms. Different legend shapes represent different datasets). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Attributes** & **Classes** & **\#Train** & **\#Test** \\ \hline \multirow{3}{*}{Handwritten digits} & 13 & 3 & 134 & 45 \\  & 16 & 10 & 8255 & 2751 \\ Iris & 4 & 3 & 113 & 38 \\ Page paper & 10 & 5 & 4105 & 1368 \\ Segment & 19 & 7 & 1733 & 577 \\ Vehicle & 18 & 4 & 635 & 211 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of the classification dataset.
## 2 Determination of Intrinsic Parameters of Lily Tubers and Establishment of Discrete Element Model  
### Triaxial Size Determination of Lily Tubers  
According to the requirements of discrete element simulation, the basic physical parameters and contact parameters of the lily tubers need to be provided. The basic physical parameters include the triaxial size, mass density, elastic modulus, Poisson's ratio, and moisture content of lily tubers. Contact mechanic parameters are composed of static friction coefficient, rolling friction coefficient, and collision recovery coefficient. Therefore, the triaxial dimensions of lily tubers should be determined first. In this study, the Longya lily planted in Longhui County, Shaoyang, Hunan Province was used as the research object. The five-point sampling method was used to sample Longya lily in Longhui County, Shaoyang to accurately establish a three-dimensional model of lily tubers., 300 pieces were randomly selected, and a 111-101 type vernier caliper (precision 0.01 mm) was employed to measure their three-axis dimensions (maximum length L, maximum width W, maximum thickness T), the result being the averages collected. The three-axis size measurement of Longya Lily is illustrated in Figure 1.  
Table 1 presents the average values of maximum length L, maximum width W, and maximum thickness T of a Longya lily tuber obtained. Their values are 90.29, 81.43, and 65.27, respectively, with standard deviations of 0.438, 0.445, and 0.472. The overall shape of the tuber is ellipsoidal.
# Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network
## 2 Sample Construction
### Hinge Four-Bar Mechanism Sample Construction  
Four-bar mechanisms are very common, consisting of four links connected by hinges to form a closed mechanism. Due to their simplicity and reliability, they find widespread applications in engineering and the field of mechanics [23].  
The four-bar mechanism is illustrated in Figure 3, where \(a,b,c,d,e\) represent the length of the corresponding link, \(\alpha\) represents the angle between links BC and BP, and point P is the trajectory point. The solid line in the figure represents the regular configuration, while the dashed line represents the inverted configuration. By varying the values of these six parameters, different four-bar mechanisms can be designed to obtain various trajectory diagrams for the mechanisms.  
Figure 3: Hinge four-bar mechanism model diagram.  
Figure 2: Trace diagram.  
Based on the parameters of the four-bar mechanism shown in Figure 3, formulate Equation (1) and solve for \(\theta\).  
\[\left\{\begin{aligned} & acos\varphi+bcos\theta=d+ccos\psi\\ & asin\varphi+bsin\theta=csin\psi\end{aligned}\right. \tag{1}\]  
The equation is calculated as follows:  
\[\theta=2 tan^{-1}(\frac{B_{0}\pm\sqrt{A_{0}^{2}+B_{0}^{2}-C_{0}^{2}}}{A_{0}+C_ {0}})\]  
\[A_{0}=2b(acos\varphi-d)\]  
\[B_{0}=2 absin\varphi\]  
\[C_{0}=a^{2}+b^{2}-c^{2}+d^{2}-2 adcos\varphi\]  
where the positive configuration is denoted by '\(-\)' and the inverted configuration is denoted by '\(+\)'. After solving for \(\theta\), the coordinates of the trajectory point P can be obtained as shown in Equation (2).  
\[\left\{\begin{aligned} x_{P}&=acos\varphi+ecos(\alpha+ \theta)\\ y_{P}&=asing+esin(\alpha+\theta)\end{aligned}\right. \tag{2}\]  
For the design of a planar four-bar mechanism, only the link length conditions need to be considered, requiring that the sum of the shortest link and the longest link be less than or equal to the sum of the other two links, i.e., \(a+b\leq c+d\); \(a+c\leq b+d\); \(a+d\leq b+c\).  
When designing a four-bar mechanism and setting values for its parameters, there are no specific constraints on the ranges of each parameter; they just need to satisfy the fundamental conditions for forming a four-bar mechanism. However, to prevent an excessive increase in the number of samples, the ranges of parameters are limited during the design of the planar four-bar mechanism, specifically as follows: \(0<(a,b,c,d,e)\leq 1\), \(0\leq\alpha\leq 2\pi\). Once the parameter ranges are determined, the coordinate axis of the image is set to \((-3,3)\) to ensure that the generated trajectory appears entirely within the image.
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
## 3 Experiment and Analysis of Results
### Brief Introduction to Station SC02  
The experimental data were obtained from the GPS station SC02, which is co-located with a TG and belongs to the Plate Boundary Observation (PBO) Network of the American Earth Scope Plan. As seen in Figure 5, station SC02 overlooks the sea and can receive reflected signals across a relatively large azimuth range. The station is located near the Friday Harbor Port in Washington State, USA (48.5\({}^{\circ}\)N; 123\({}^{\circ}\)W). It is situated at an altitude of \(-\)15.03 m with reference to ITRF08, and is equipped with a Trimble(r) NETR9 receiver and TRM29659.00 choke-ring antenna (Trimble Inc., Sunnyvale, CA, USA) with a hemispherical radome. However, it only provides GPS measurements which contain a pseudo-random code with a single frequency (C/A1), a precise code (P2), a carrier phase with two frequencies (L1 and L2) and the SNR data at two frequencies (S1 and S2).  
Figure 4: Graphical illustration of the linear relationship between peak frequencies and antenna height from the model for the elevation range 5–12\({}^{\circ}\).  
From 1 January until 31 January 2015, several available satellite signals were used, at a sampling rate of 1 s, to estimate sea level change, and data from the co-located TG station were collected. The TG station is 359 m from station SC02, and consists of an Aquatrak(r) air acoustic sensor (Aquatrak Corp., Sanford, FL, USA) in a protective well. Sea level data at this TG are recorded at 6-min sampling intervals and were obtained from National Oceanic and Atmospheric Administration (NOAA) for comparison.  
To ensure that the signals used in the experiment were reflected by the sea surface, data with elevation angles in the 5-12\({}^{\circ}\) and 5-15\({}^{\circ}\) ranges and azimuth angles in the 90-150\({}^{\circ}\) range were selected. It was found that approximately 14 GPS satellites met the above azimuth and elevation angle criteria each day. However, some satellites did not provide high-quality data that could be used to retrieve reflector height. Ultimately, observations were obtained from satellites PRN 01, PRN 07, PRN15, PRN19, PRN23, PRN27 and PRN30 for use in data processing. This included L1/L2 carrier phases and their SNRs.
# Harnessing the Potential of Emerging Technologies to Break Down Barriers in Tactical Communications
## 4 Use Cases
### Use Case 7: Situational Awareness  
Situational awareness (SA) involves having a comprehensive understanding of the overall environment surrounding a combatant or military platform. Given the dynamic nature of battle scenarios, real-time SA is imperative. JSC plays a crucial role in enhancing information collection efficiency by utilizing the same signal for both radar and communication functions. The data are then swiftly processed by neuromorphic processors, significantly outpacing the capabilities of conventional processors. This enables the extraction of a larger volume of pertinent information for situational awareness. Additionally, AI can assist in streamlining computational complexity.
### Related Work
FD Cooperative NOMA (FD-CNOMA) shows better system fairness as compared to HD Cooperative NOMA (HD-CNOMA). However, the hybrid combination of switching between HD and FD communication is also a suitable way to maintain a balance between the interference problems [5]. Space-Time Block Coding NOMA (STBC-NOMA) is one of the approaches that are helpful for reducing SIC complexity in the system using Alamouti distributed STBC [27]. Modulation based NOMA (M-NOMA) is one of the techniques that offer reduced interference, less SIC complexity and is also energy efficient [28]. Depending on the available CSI, a singular value decomposition scheme [29] is proposed for solving security issues that arise using NOMA-MIMO implementation and secrecy rate analysis. The system analysis of a two-way cooperative relay NOMA communication [30] is observed with the joint effect of imperfect SIC and quadrature and in-phase imbalance and Rician fading channel. Performance degradation with an increased number of users is caused due to high SIC on each user's end [31], to analyse the system symbol error probability is derived and simulated for imperfect SIC and Space-Time Block Code (STBC) aided NOMA with timing offsets. A Matlab platform is designed [32] for system and link-level analysis and Long-Term Evolution (LTE) is used as a baseline technique for Bit Error Rate (BER) and capacity comparison. A two-hop relay selection NOMA for IoT communication system [33] is considered to enhance secrecy performance. Using a Tchebycheff approach [34], a system for a secure FD NOMA and SWIPT is studied with power minimization and multi-objective optimization problems. To permit an effective Dynamic Power and Channel Allocation (DPCA) in the DL multi-channel NOMA (MC-NOMA) systems [26], the optimization as the combinative problem, and offers three investigative solutions, i.e., two-stage Greedy Randomized Adaptive Search (GRASP), stochastic algorithm, and two-stage Stochastic Sample greeDy (SSD). For shortage of frequency band resources, a Terrestrial-Satellite Integrated Network (TSIN) for ground users in [35] is evaluated with NOMA. NOMA is an essential component for the upcoming 5G technology which will provide a promising SE. The allocation of subcarriers to the users of poor channel conditions affects the SE of the system in OMA. NOMA allows the use of the channel by all users together and offers user fairness, massive connectivity, low latency and diverse QoS. However, interference, system computational complexity and the required energy in NOMA still need thoughtful consideration. IoT requires the connection of everything, which seems very interesting but demands a lot. AI and ML together with FD and NOMA seem promising but challenging. Alongside, security is the biggest all-time issue. The security issue needs to be resolved more than the advancement of technology. The more connections amongst objects open, the more loopholes for security and privacy issues.