esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Model Assessment and Validation  
This section assesses and validates the robustness of our system model by comparing it with existing work done in incorporating NOMA in TI wireless networks.  
To the best of our author's knowledge, no existing works have focussed and analysed mathematically derived SINR, achievable sum rate and fair PA coefficients altogether to meet the TI user's stringent requirements. Most of the existing work done in [8; 12; 16; 20; 26] have focused on and shared primitive analyses on downlink PD SISO-NOMA, considering mostly two users in a scenario. The NOMA performance has not been rigorously evaluated and analysed on varying network parameters, such as PA coefficients and \(\eta\), to observe users' performance effects. Moreover, the comparison between SISO-NOMA and traditional OMA concerning BER, sum rate and latency trends with varying parameters mentioned earlier with a two-user and a three-user scenario has not been well studied in the networking literature.  
Nonetheless, the fair PA analysis in terms of outage probability presented in our work has focussed on deriving PA coefficients to meet the stringent target rates of users under TI communication. Therefore, the PA coefficients can be precisely calculated and tuned to meet a specific SISO-NOMA user's target rate requirement. In addition, the achievable sum rate and latency with calculated PA coefficients are compared with fixed PA coefficients, which was also missing from previous works. Along similar lines, the performance of 4 \(\times\) 4 MIMO-NOMA incorporating zero forcing-based beamforming and a round-robin scheduling process are compared with SISO-NOMA in terms of achievable sum rate and latency, which is not exclusively mentioned in previous work.  
However, this research did not focus on machine intelligence with imperfect SIC conditions, which can be considered for future research directions.
# A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space-Time Block Code
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} STBC & Space time block code \\ O-STBC & Orthogonal STBC \\ STBC-O & STBC from orthogonal design \\ QO-STBC & Quasi orthogonal STBC \\ BER & Bit-error-rate \\ MMSE & Minimum mean-square-error \\ ZF & Zero forcing \\ SNR & Signal-to-noise ratio \\ \end{tabular}
## 2 Preliminary
### KL Divergence  
KL divergence is often used to measure the difference between two probability distributions in the same event space and is widely used for optimization tasks in machine learning [28; 29]. The KL divergence between two continuous probability density functions (PDFs) \(f_{1}\) and \(f_{2}\) is defined as follows [30]:  
\[I(f_{1}\|f_{2})=\int f_{1}(x)\log\frac{f_{1}(x)}{f_{2}(x)} \tag{5}\]  
Since the above definition does not satisfy symmetry and may result in \(I(f_{1}\|f_{2})\
eq I(f_{2}\|f_{1})\), an improved form of KL divergence referred to as symmetric KL divergence is presented as follows [21]:  
\[KLD(f_{1}\|f_{2})=I(f_{1}\|f_{2})+I(f_{2}\|f_{1}) \tag{6}\]  
Let \(n_{1}\), \(m\) be the numbers of samples and variables of on-line data matrix \(Y\in R^{n_{1}\times m}\). The main processes of the conventional incipient fault detection methods based on PCA and KL divergence are as follows [21; 22]:  
1. The standardized normal data \(\overline{X}\) are projected on each PV \(v_{j}\) and the reference PDF of the normal data is obtained after projection on \(v_{j}\) as \(f_{j}\) and the corresponding detection threshold is \(\epsilon_{j}\), \(v_{j}\in V,j\in[1,m]\).  
Each column \(y_{k}\) of the on-line data matrix \(Y\) is standardized with \(\overline{y}_{k}=\frac{y_{k}-E(x_{k})}{\sigma(x_{k})}\), \(k\in[1,m]\).
3. The standardized on-line data \(\overline{Y}\) are projected on each PV \(v_{j}\) to obtain the PDF \(g_{j}\) of the on-line data.
4. Equation (6) is used to calculate the KL divergence \(KLD\big{(}f_{j}\big{|}\big{|}g_{j}\big{)}\) between the PDF \(g_{j}\) and the reference PDF \(f_{j}\) for each PV \(v_{j}\).
5. Whether the \(KLD\big{(}f_{j}\big{|}\big{|}g_{j}\big{)}\) is greater than the corresponding detection threshold \(\varepsilon_{j}\) is determined for each PV \(v_{j}\). It is considered to be faulty when at least one of the \(m\) units of \(KLD\big{(}f_{j}\big{|}\big{|}g_{j}\big{)}\) exceeds the corresponding detection threshold.  
\[\left\{\begin{array}{c}H_{0}:\forall v_{j},v_{j}\in V,KLD\big{(}f_{j}\big{|} \big{|}\big{|}g_{j}\big{)}\leq\varepsilon_{j},\text{fault-free}\\ H_{1}:\exists v_{j},v_{j}\in V,KLD\big{(}f_{j}\big{|}\big{|}\big{|}g_{j}\big{)} >\varepsilon_{j},\text{faulty}\end{array}\right. \tag{7}\]
## 2 Results and Discussion
### Volatile Composition of Investigated Fruits and Vegetables
#### 2.1.2 Carrot  
Regarding carrot varieties, 71 volatile metabolites were identified in white carrots, whilst in orange carrots, 59 were identified (Figure 1). The volatile profile of both carrot varieties is mainly composed of terpenic compounds. Orange carrot is very rich in terpenic compounds with 31 being identified, which account for 58.1% of the total volatile fraction of this variety. In white carrot, terpen are less dominant with 22 volatile metabolites being identified, which represent 61.3% of the total volatile fraction.  
The characteristic flavor of fresh carrots is attributed essentially to mono- and sesquiterpenes. Monoterpenes are undoubtedly the most abundant in both varieties, with terpinolene (89) and \(\gamma\)-terpinene (75) being the major volatiles found in white carrot (32.0%) and orange carrot (27.2%), respectively. [PERSON] et al. [38] demonstrated that incubating plasma with \(\gamma\)-terpinene extends the resulting lag phase in the copper-mediated formation of conjugated dienes in LDL by the factor 1.5 or 2.7, respectively. These results agree with those obtained by [PERSON] et al. [39], who observed that \(\gamma\)-terpinene can be enriched in LDL by pre-incubating human blood plasma with \(\gamma\)-terpinene. In addition, the same researchers proved that the subsequently isolated LDL shows a high resistance against copper-induced oxidation [39]. \(\alpha\)-pinene (25) was also identified in both varieties, with percentages of around 4.3% in the orange variety and 1.6% in the white variety. According to [PERSON] et al. [40], this compound is primarily responsible for the antimicrobial and antifungal properties of carrots. In addition to these bioactive compounds, D-limonene (59), \(\beta\)-myrcene (51), \(\beta\)-pinene (40) and \(\alpha\)-phellandrene (50) were also identified in low quantities.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 1 Introduction
The authors presented super-symbols as a combination of 8 QAM and 16 QAM and showed that, as expected, hybrid modulations perform in between the two even when propagating through heavily filtered channels. In this paper, we present a new software-based method that uses existing HW blocks in a typical coherent Rx, to reduce the Required-OSNR (ROSNR) of an optical link in the presence of ROADMs filtering. The source of filtering in optical links is detailed in Section 2. The models used for the filtering  
Figure 1: (**a**) ROADMs (reconfigurable optical add drop multiplexings) in typical networks configuration. (**b**) Signal with root-raised cosine (RRC) pulse shaping, as well, the two models used in simulation/experiment showing reduction in bandwidth due to cascaded 50 GHz ROADMs. and architecture of typical dual-polarization coherent Rx are illustrated in Section 3. In Section 4, we will show the mathematical derivation of the proposed gradient-descent based method. The channel model, simulation environments and results are described in Section 5. In Section 6, the method is validated through a lab experiment. In Section 7, we discuss the feedback channel, present system advantages of our method and compare simulation to a lab experiment.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 2 OAI-Based 5G mmWave Network
### Time-of-Arrival and Power Measurements  
The ToA estimation is here based on a threshold-based correlation of the PRS, i.e., ToA for the maximum peak of the PRS correlation above a certain threshold. This is implemented with a least-squares channel estimate in the frequency domain from the PRS, which is then converted to the time domain using an Inverse Discrete Fourier Transform (IDFT) to determine the ToA.  
The least-squares estimate of the PRS channel \(h[k]\) at the \(k\)-th subcarrier can be obtained from the frequency-domain received signal \(y[k]\) as  
\[h[k]=y[k]\cdot s^{*}[k], \tag{1}\]  
Figure 1: OAI mmWave PRS resource allocation within (**a**) 5G NR radioframe; (**b**) 5G NR slot.  
where \(k\in[0,K-1]\) and \(K\) is the total number of subcarriers per PRS symbol. The frequency domain channel estimates can be represented in vector form as  
\[\mathbf{h}=[h[0],h[1],\ldots,h[K-1]]\in\mathbb{C}^{K\times 1}. \tag{2}\]  
Then, \(\mathbf{h}\) is zero-padded by an up-sampling factor \(K_{\text{n}}\) represented as \(\mathbf{h}_{\text{ZP}}\in\mathbb{C}^{K_{\text{n}}\cdot K\times 1}\) to increase the time resolution when converted to the time domain. The ToA estimate from \(\mathbf{h}_{\text{ZP}}\) is finally obtained by taking an IDFT of size \(K\cdot K_{\text{n}}\) as  
\[\hat{\tau}=\frac{1}{F_{\text{s}}\cdot K_{n}}\cdot\text{argmax IDFT}\{\mathbf{h}_{\text{ZP}}\}, \tag{3}\]  
where \(F_{\text{s}}\) is the sampling rate.  
The RSRP in the digital domain (measured in dBFS) is estimated from the received signal \(y[k]\) (which is in Q1.15 fixed-point format) as follows:  
\[\text{RSRP}[\text{dBFS}]=10\cdot\log_{10}\left(\frac{1}{K}\sum_{k=0}^{K-1}|y[ k]|^{2}\right)-10\cdot\log_{10}2^{30}, \tag{4}\]  
where \(|\cdot|\) denotes a modulus of a complex number and \(2^{30}\) corresponds to the power level at full scale of 30 bits. Furthermore, to convert RSRP from dBFS to dBm, the RSRP in dBm is obtained by  
\[\text{RSRP}[\text{dBm}]=\text{RSRP}[\text{dBFS}]-G_{\text{RX}}, \tag{5}\]  
where \(G_{\text{RX}}\) is the receiver gain, which is calibrated using a signal at a known input level.
## 3 State-of-the-Art
### Agricultural Residual Biomass  
#### 3.2.1 Sugarcane Bagasse  
Sugarcane bagasse is a type of biomass that originated from residues of sugarcane cultivation widely available around the world [21]. For this reason, it is also one of the most studied forms of biomass, mainly due to the fact that this feedstock is associated with the production of bioethanol, a biofuel used to replace gasoline with wide dissemination, e.g., in Brazil and India [52; 53; 54]. The need to increase the efficiency and productivity of processes related to the recovery of sugar as much as possible from sugarcane bagasse has become a top scientific priority reflected by the huge number of published studies. For example, [PERSON] et al. (2017) presented a paper on biorefineries for the production of bioethanol, methane, and heat from sugarcane bagasse [55], while [PERSON] et al. (2012) carried out a technical-economic evaluation of the production of second generation bioethanol from sugarcane bagasse [56], which is in line with the study presented by [PERSON] and [PERSON] (2015) that addressed the topic of increasing productivity using dual salt on the pretreatment of sugarcane bagasse with hydrogen peroxyde [57]. However, perhaps the form of energy recovery most associated with sugarcane bagasse is direct combustion and conversion to charcoal [21]. In this perspective, there are several studies that characterize samples of sugarcane bagasse from different sources, highlighting the works originating in Brazil, such as those by [PERSON] et al. (2017), [PERSON] et al. (2015), or [PERSON] et al. (1999) [58; 59; 60], works originating in India, such as those by [PERSON] and [PERSON] (2017), [PERSON] and [PERSON] (2007), or [PERSON] et al. (2019) [61; 62; 63], or even works originating from other sources, such as the Caribbean or Southeast Asia region, such as the works by [PERSON] and [PERSON] (2018), by [PERSON] et al. (1998), [PERSON] and [PERSON] (2003), or [PERSON] et al. (2000) [64; 65; 66; 67].
# BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
## 3 Results
### Satellite- and Receiver-Induced Code Biases and Modeling
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Differences with Wanninger and Beer’s Model (m)**} \\ \cline{2-5}
**Elevation (\({}^{\circ}\))** & \multicolumn{2}{c}{**IGSO**} & \multicolumn{2}{c}{**MEO**} \\ \cline{2-5}  & **B1-Wa** & **B2-Wa** & **B1-Wa** & **B2-Wa** \\ \hline
0 & \(-\)0.17 & 0.26 & 0.05 & \(-\)0.01 \\
10 & \(-\)0.05 & 0.06 & 0.01 & 0.01 \\
20 & 0.00 & 0.02 & \(-\)0.03 & \(-\)0.08 \\
30 & 0.01 & 0.02 & \(-\)0.01 & \(-\)0.02 \\
40 & 0.03 & 0.04 & \(-\)0.06 & \(-\)0.08 \\
50 & 0.03 & 0.03 & \(-\)0.09 & \(-\)0.09 \\
60 & 0.04 & 0.03 & \(-\)0.15 & \(-\)0.12 \\
70 & 0.05 & 0.04 & \(-\)0.14 & \(-\)0.09 \\
80 & 0.01 & 0.03 & \(-\)0.03 & \(-\)0.08 \\
90 & 0.01 & 0.02 & \(-\)0.03 & \(-\)0.06 \\ AVG & 0.00 & 0.06 & \(-\)0.05 & \(-\)0.06 \\ RMS & 0.06 & 0.09 & 0.08 & 0.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The differences between the derived values and Wanninger and Beer’s model. Figure 10: The final derived elevation-dependent MP model. Figure 11: Comparison between the final derived model values and those of [PERSON] and Beer. Figure 12: An example of MP1 variations after application of the GNOS-receiver-induced (rec-ind) and BDS satellite-induced (sat-ind) bias corrections for different BDS satellite types.
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 3 Motivation  
In this section, we first analyze the advantages and disadvantages of the spin-polling mechanism and batch processing mechanism. Then, we use some experiments to explore the characteristics of the forwarding latency and throughput of the software data plane under these mechanisms.
# Mental Map-Preserving Visualization through a Genetic Algorithm
## 2 Materials and Methods
### Genetic Algorithm  
#### 2.5.1 Fitness Function  
The input of our GA algorithm is a graph G = (V, E), where V is the set of nodes, and E is the set of edges. Each candidate solution is encoded as a vector containing coordinates (\(x_{i}\), \(y_{i}\)) and its previous position (\(x_{i}\)', \(y_{i}\)'), if it existed, to calculate the mental map preservation. We use several aesthetic criteria that will be minimized, and they are composed by a weighted sum. A user will define these weights to adjust the graph drawing.  
Equation (2) is our fitness function f, where \(w_{n}\) is our weight for each term, \(d_{ij}\) is the distance between node \(i\) and node j using the Euclidean distance, \(d_{ic}\) is the distance between node \(i\) and center c using the Euclidean distance and _size_ is the number of nodes inside a merged node. Every term will be normalized by dividing it by its first calculated value; hence, a lower value will be below one.  
\[f=w_{1}\sum_{(vi,vj)\in V,\ i\
eq j}1/(d_{ij}^{2}\ /size)+w_{2}\sum_{(vi,vj)\in E }\frac{\left(l_{ij}-d_{ij}\right)^{2}}{|E|}+w_{3}\sum_{(vi)\in V}d_{ic}^{2}\ +\ w_{4}\ \textit{gradability}\ (V,E) \tag{2}\]  
By maximizing the distance among nodes, they can spread evenly. We added _size_ into consideration, so the larger a node size, the greater the distance between the node and the other nodes; then, a bigger size node can have enough space when a user wants to expand it to see more detail.  
The next term is the uniform edge length, where all edges supposedly have a uniform length to achieve a better aesthetic result. A similarity value is also used between them so that the more similar two nodes are, the shorter the length is. We defined \(l_{ij}\) to be the shortest paths between node \(i\) and \(j\) based on its similarity value. Our third term is basically used to pull every node to the center of a screen instead of letting them stay along the screen's boundary. The last criteria used is the greadability.js to measure our graph readability, where the input is our graph (V and E), and the output includes four global graph readability metrics [14]. All of these metrics are between [0, 1], and higher numbers indicate better layouts, so the average of all the metrics is our third term in the fitness  
Figure 5: Transition animation procedure.  
function. We consider these three terms sufficient to draw a good layout, as they cover most of the aesthetic criteria.
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 3 Range Determination for a Specular Target
### Simplified Algorithm
#### 3.2.2 Coherence Estimation  
Coherent summing only makes sense if there is significant coherence between echoes. The coherence in the burst of \(2K+1\) echoes should be checked as a preliminary step. We employed two types of coherence measurements and found both equally useful. The first one measures coherence between echoes, with a lag of m echoes. This is called the \(m\)-lag magnitude squared coherence (MSC) metric and is defined as follows:  
\[|\gamma|^{2}=\frac{\left|\sum_{k}z_{n+k,r}z_{n+k+m,r}^{*}\right|^{2}}{\sum_{k} \left|z_{n+k,r}\right|^{2}\sum_{k}\left|z_{n+k+m,r}\right|^{2}} \tag{8}\]  
where again the sums are over the \(2K+1\) burst. The 1-lag is used in subsequent references to the MSC, but higher lags can be used in more demanding tests for coherency.  
Another coherence metric is defined between the echo data and the predicted Doppler:  
\[|\gamma|^{2}=\frac{\left|\sum_{k}z_{n+k,r}e^{-i\omega k}\right|^{2}}{\left(2K +1\right)\sum_{k}\left|z_{n+k,r}\right|^{2}} \tag{9}\]  
Figure 7: (**a**) Doppler velocity estimation for \(J=1\) lag; and (**b**) Doppler velocity estimation for \(J=5\) lags.  
Both metrics result in values between 0 and 1, with values >0.8 indicating very high coherence. In the examples shown in this paper the typical MSC is >0.95 in the main lobe. In near sidelobes we measured still significant levels of 0.7-0.8.
## 4 Results and Analysis
### RBF and TLBO Algorithms to Optimize Class' Weight  
After weighting each factor using SWARA, these weights were smoothed using a fit-curve method of the medians based on RBF and TLBO (Figure 5). As can be seen, the quality of the weight assignment process was dramatically improved by eliminating the jumps among the classes and in the vicinity of the class' weights.
# Future Directions for Electric Propulsion Research
## 4 Challenges for Electric Propulsion Development
### Low-Power Thrusters
#### 4.1.3 Electrosprays  
For low-power electric propulsion, electrospray arrays are perhaps the most well developed and successful technology that can fulfill the niche of arbitrarily low power for near-Earth spacecraft. Unlike Hall thrusters and gridded ion thrusters that have poor geometric scaling, electrosprays have no increasing ionization cost with decreasing size - there is no ionization involved. For this reason, electrosprays can theoretically be scaled to microscopic sizes and thus perform well at low power. Reflecting this fact is the s-curve in Figure 3, in which electrosprays are moderately mature in the present day due to sustained research to take advantage of these perceived scaling benefits.  
These devices were originally investigated in the 1960s, and although some of them were operated reportedly for thousands of hours, voltages as high as \(\sim\)10 kV were required to reach specific impulses competitive with gridded ion thrusters [29]. This was due to the use of solutions of ionic compounds as the propellant, which limited the electrospray operation to droplet extraction, requiring high voltages to break the propellant surface tension and adequately accelerate the heavy droplets. In the past few decades, interest in electrosprays has reignited with the increasing desire for fine thrust control, the push for low-power propulsion solutions for small satellites, and the realization that room-temperature ionic liquids or liquid metals allow for ion extraction, thus requiring lower voltages.  
An electrospray system has been successfully used on the ST-7/LISA Pathfinder mission, in which twin four-head field emission electric propulsion (FEEP) units were able to fulfill the control, noise, and duration objectives of the mission [30]. However, the need for continued development of this technology was made clear by the various anomalies that arose during the mission: one thruster had a sluggish response time and current spikes, and another thruster developed a terminal short after 1670 h of operation [30]. Electrospray systems have also flown on various small satellites, including the Massachusetts Institute of Technology's SiEPRO on multiple AeroCube spacecraft [31] and Emulsion's IFM Nano-Thruster [32].  
The largest challenge for electrospray thrusters is arraying, which itself is obstructed by the poorly understood stability and failure modes of single emitters. Typical single-emitter systems consume \(\ll\)1 kW, so scaling them to practical power levels (\(\sim\)100 W) requires fabricating and operating numerous emitters in an array. However, the manufacturing of these arrays can prove challenging, and the chance for terminal failure increases (examined in [33]), especially when the nature of the failure modes and the related instabilities are unclear.
###### Abstract  
Camelina (_Camelina sativa_ (L.) Crantz) is a valuable source of essential amino acids, especially sulphur-containing ones, which are generally lacking in leguminous crops, thus representing an alternative source of protein for both humans and farm animals. Rhizosphere soil samples from five experimental plots with mono- and mixed cultivations of three camelina cultivars, including two introduced varieties Cs1.Pro (Luna) and Cs2.Pro (Lenka) and one Bulgarian variety Cs3.Pro (local Bulgarian landrace) with variety 666 of vetch (_Vicia sativa_ L.) (Cs3-Vs.Pro) and variety Mir of pea (_Pisum sativum_ L.) (Cs3-Ps.Pro), were collected and analysed. The total DNA was isolated from the rhizosphere soils and the presence of the 168 rRNA gene was confirmed by amplification with the universal primer 16 SV34. In the present study, the structure of the soil bacterial community in five different plots (Cs1.S.Pro, Cs2.S.Pro, Cs3.S.Pro, Cs3.Vs.S.Pro, and Cs3.Ps.S.Pro) where camelina was grown alone and by being intercropped with pea and vetch was analysed via a metagenomic approach. The number of observed species was highest in the local genotype of the camelina Cs3 grown alone, followed by soil from the intercropped variants Cs3-Vs and Cs5-Ps. The soil bacterial communities differed between the sole cultivation of camelina and that grown with joint cultivation with vetch and peas, indicating that legumes considerably affected the growth and development of beneficial microorganisms by aspects such as nitrogen fixing, levels of antifiring bacteria, and levels of phosphorus-dissolving bacteria, thus helping to provide better plant nutrition. The \(\alpha\)-diversity indicated that bacterial communities in the rhizosphere were higher in soils intercropped with vetch and pea. The optical properties of cereals and legumes were determined by their energy structure, which includes both their occupied and free electronic energy levels and the energy levels of the atomic vibrations of the molecules or the crystal lattice.  
camelia sativa; intercropping with pea and vetch; mobile fluorescence spectroscopy; NGS; soil microbiome +
Footnote †: journal: Applied Sciences