esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 4 Jamming Utilization Method Based on IMSFH
### IMSFH Receiver Framework  
The IMSFH receiving process is illustrated in Figure 4. To address the dynamic variations of reactive jamming signals, a training stage is introduced to learn these change rule. Therefore, the reception of user signal is divided into two stages: a communication stage and training stage, both of which share the same multi-sequence frequency-hopping signal generation method. Similarly, the user signal is divided into communication signals and synchronization signals. We assume that the synchronization signal is known in advance, so it serves as the training sample for the network. The specifics of these two stages are as follows: a training stage is introduced to learn these changing patterns in order to cope with the dynamic changes in jamming signals. The user signal is divided into two stages:  
1. Training stage: During this stage, the synchronization signal is preprocessed and presented to the neural network in the form of a spectrum waterfall. Next, the neural network learns the reactive jamming rule in the spectrum waterfall using a supervised learning approach. Specifically, during the training stage, the frequency points of the user at each moment are clearly known. However, the received signal may consist of a composite signal that includes both the user signal and various jamming signals. The primary function of the neural network is to extract the features of reactive jamming from these composite signals and identify their correlation with the user's signal frequency. These correlations encompass the reaction time needed  
Figure 3: IMSFH communication framework.  
for reactive jamming and the spectral characteristics of the reactive jamming signals. As the training continues to converge, in the communication stage, we will determine the user's communication frequency based on the correlation between the reactive jamming signal features extracted during the training stage and the currently received composite signal, thereby achieving effective utilization of reactive jamming. Finally, the trained network is deployed during the communication stage;
2. Communication stage: At this stage, the receiver feeds the received complete communication stream as a spectrum waterfall into the trained network for frequency detection. In the frequency-detection process, due to the fact that the frequency-detection network has already learned the reactive jamming rules in early training, and the reactive jamming signal is correlated with the user's communication frequency, the frequency-detection network outputs the user's communication frequency after using the spectrum waterfall as input. Therefore, reactive jamming signals can be used to improve the performance of frequency detection. Finally, the obtained frequency information is compared with two dual channels 0 and 1 to obtain bit information.
## 5 System Analysis
### Security Analysis  
#### 5.2.1 User's Privacy Protection Requirements \(<L_{(k,s)},Q_{l}>\)  
**User's privacy protection requirements in a single query:** In a single query, the user submits a query request \(\mathit{Req}=((L_{1},C_{1}^{{}^{\prime}}),(L_{2},C_{2}^{{}^{\prime}}),\cdots,( L_{k},C_{k}^{{}^{\prime}}))\) to the LBS server. If the user wants the attacker to identify the user's real location with a probability that is not greater than \(\frac{1}{k}\), and the user's organization name is identified with a probability that is no greater than \(\frac{1}{s}\), the user's location privacy protection requirement is called \(L_{(k,s)}\). If the user wants the attacker to identify the user's actual query content with a probability that is not greater than \(\frac{1}{t}\), the user's query privacy protection requirement is called \(Q_{l}\).  
**User's location privacy protection requirements in continuous queries in the same location:** The user submits queries continuously in the same location. If the following conditions are met:  
1. \(\mathit{Pr}\{L_{real}=L_{11}|\mathit{Req}_{i},i=1,2,\cdots,n\}\leq\frac{1}{t}\);
2. \(\mathit{Pr}\{L_{real-s}=L_{11}|\mathit{Req}_{i},i=1,2,\cdots,n\}\leq\frac{1}{s}\);
3. \(\mathit{Pr}\{C_{real}^{{}^{\prime}}=C_{11}^{{}^{\prime}}|\mathit{Req}_{i},i=1, 2,\cdots,n\}\leq\frac{1}{t}\)  
The proposed scheme meets the user's privacy requirements \(<L_{(k,s)},Q_{l}>\) in a continuous query request scenario in the same location.
## 3 Background
### Other Key Components Selection
#### 3.5.4 Data Processing Sub-System  
Data processed by the payload and the CubeSat's Sub-System must be processed by an on-board computer which then directs it to the transceiver to be broadcast by the antenna and fed back to ground control on Earth at a specified frequency in the UHF/VHF range. The on-board computer is also capable of decoding incoming transmission from ground control, and processing these into a format suitable for Sub-System, e.g., remotely controlling the propulsion system to perform orbital maneuvers.  
The chosen On-Board computer is developed by ISIS (Innovative Solutions in Space) an illustrated in Figure 37[58]. This is a flight proven system incorporating a 400 MHz ARM9 processor. The product also possesses 64 MB of SDRAM and has a 16 GB data storage capacity of 2 \(\times\) 8 GB SD cards [50]. Both Current and Voltage measurements are taken to allow current surge protection, as well as data measurement and logging of local component temperature. The operating temperature range of this device is \(-25\)\({}^{\circ}\)C to 65 \({}^{\circ}\)C. The average power consumption is 400 mW peaking at a value of 550 mW with a 3.3 V supply. The dimensions of the product are 96 \(\times\) 90 \(\times\) 12.4 mm with a total mass of 94 g [58].  
Figure 36: piNAV-L1 Operational Block Diagram [57].  
Figure 37: ISIS On-Board Computer [58].
## 2 Contact Analysis of Rough Surface
### Porosity of Rough Sealing Surface after Loading  
Under the action of contact pressure, the asperities on the rough surface will deform (i.e., produce the compression \(w^{\prime}\)), which will change the porosity of the sealing interface. Taking the calculation unit of sealing interface (the total volume is \(V_{0}=L\times L\times H\), \(L\) is the length and width of the calculation unit, and \(h^{\prime}\) is the height of the calculation unit, namely the height of red area in Figure 2) as the research object, after loading the height of the sealing interface will change from \(h^{\prime}\) to \(h^{\prime}-w^{\prime}\). Then, the volume of the calculation unit changes from \(V_{0}=L\times L\times h^{\prime}\) to \(V_{0}^{{}^{\prime}}=L\times L\times(h^{\prime}-w^{\prime})\). According to the principle that the skeleton volume remains unchanged before and after deformation, the skeleton volume is still \(V_{\rm s}=(1-\phi_{0})\times V_{0}\) and the pore volume is \(V_{\rm p}{}^{{}^{\prime}}=V_{0}{}^{{}^{\prime}}-V_{\rm s}\), then the porosity of sealing surfaces becomes:  
\[\phi=\frac{V_{\rm p}{}^{\prime}}{V_{0}{}^{\prime}}=\frac{L\times L\times(h^{ \prime}-w^{\prime})-(1-\phi_{0})\times L\times L\times h^{\prime}}{L\times L \times(h^{\prime}-w^{\prime})}=\frac{\phi_{0}h^{\prime}-w^{\prime}}{h^{\prime} -w^{\prime}} \tag{7}\]  
where \(\phi_{0}\) is the initial porosity of the sealing interface.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 3 Results
### TX Performance  
Figure 7 shows the irradiance at a distance of 12.5 mm, measured from the TX aperture. Table 2 lists the detailed merits of both profiles. Figure 7a shows the donut-like shaped profile of the LD without optics. The off-axis placement of the LD is directly observable as profile displacement along the x-axis. Figure 7b shows that the hybrid TX-RX lens is able to remove the displacement and homogenize the profile. The effective transmitter efficiency \(\eta_{\text{TX eff}}\) is 57.6 % with a minimum irradiance of \(117\,\upmu\text{W}/\text{mm}^{2}\) at \((x,y)=(1.5\,\text{mm},1.5\,\text{mm})\). \(\eta_{\text{TX}}\) and \(\eta_{\text{TX eff}}\) are both reduced by \(\zeta_{\text{TX M}}=24.7\) % and \(\zeta_{\text{TX F}}=12.1\) %. Moreover, \(\eta_{\text{TX eff}}\) is lowered by another \(\zeta_{\text{TX H}}=5.6\) % due to inhomogeneity within the FOV.  
Figure 7c shows the irradiance for the system with fully separated TX and RX as described in Section 2.3. Due to the profile displacement, \(\zeta_{\text{TX M}}=36.0\) % of the power misses the FOV. Moreover, the spherical lenses are not able to correct the donut-shaped profile resulting in a high \(\zeta_{\text{TX H}}=38.6\) %. As a result, the irradiance is very inhomogeneous within the FOV and it drops down to 25.9 \(\upmu\text{W}/\text{mm}^{2}\) at the right side. This corresponds to \(\eta_{\text{TX eff}}=12.8\) %.  
Figure 6: Render of the cross-section through the optical system with TX rays (blue) and RX rays (green). Both devices are placed on the rotation axis (black dotted line) with no misalignment. Only RX rays that hit the detector are shown. (**a**) Hybrid TX-RX lens (H TX-RX). (**b**) Fully separated TX and RX (S TX-RX) in worst-case orientation, i.e., TX lens facing TX lens and RX lens facing RX lens.  
Figure 7: Irradiance over \(z=12.5\) mm. (**a**) For the LD without any optics (LD). (**b**) For the LD with the hybrid TX-RX lens. (**c**) For fully separated TX and RX. The black circle highlights the anticipated TX FOV. Note the different color bar scales for the subfigures.
# Evaluation of Biosynthetic Pathway and Engineered Biosynthesis of Alkaloids
## 1 Introduction  
Alkaloids were originally defined as organic compounds of plant origin that possess strong bioactivities and exhibit basicity that is attributed to the presence of nitrogen [1]. However, harmful nitrogen-containing compounds, such as tetrodotoxin and saxitoxin (Figure 1), that are isolated from animal or microorganism sources are frequently categorized as alkaloids recently. Similarly, compounds that do not show basicity due to conversion of an amine into an amide in the molecule as seen in colchicine (Figure 1) are also generally identified as alkaloid-type compounds. Even synthetic compounds, such as procaine (Figure 1), that have similar chemical structures or biological properties to plant alkaloids can be referred to as alkaloids. As such, it has become commonplace nowadays to define alkaloids as nitrogen-containing organic compounds that can produce pronounced physiological responses, with notable exceptions being amino acids, nucleic acids and certain natural nitrogen-containing compounds, such as tetracycline (polyketide) and kanamycin (aminoglycosoide). The alkaloid class of natural products has been very important for humanity as a valuable source of pharmaceutical products, because they show a variety of biological activities at a very low dosage. Therefore, alkaloids have been frequently employed as lead compounds for drug discovery. On the other hand, during the past decade, development of new DNA sequencingtechnologies, in particular next-generation sequencing, has enabled us to easily explore \"the master plan of alkaloid production\"; that is, the genes responsible for the biosynthesis of alkaloids. By studying the master plan, we started to gain deep insight into \"the implementation of the master plan\", meaning the mechanism of the biosynthesis of alkaloids. Furthermore, we are using the gained knowledge to look for new alkaloids and at the same time modify the \"master plan\" to engineer production of modified natural products. In this article, we focus on five successful examples of reconstitution of alkaloid biosynthetic pathways in heterologous microbial hosts. Those five pathways are responsible for the formation of dityptophanamine (dimeric diketopiperazine alkaloid), saframycin (tetrahydroisoquinoline alkaloid), strictosidine (monoterpene indole alkaloid), ergotamine (ergot alkaloid) and opiates (benzylisoquinoline and morphinan alkaloid). Discussion of the use of microbes as catalysts for the bioconversion of advanced alkaloid intermediates can be found elsewhere [2]. Similarly, readers are referred to other review articles for the recent efforts toward engineering plants for improved production of alkaloids and their analogs [3,4]. We will also discuss the recent progress in the development of methodology for generating various compounds through heterologous reconstitution and engineering of biosynthesis pathways, especially those involved in the bioproduction of opioids.
###### Abstract  
Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a highly valuable remote sensing technique for probing the Earth's atmosphere, due to its global coverage, high accuracy, long-term stability, and essentially all-weather capability. In order to ensure the highest quality of essential climate variables (ECVs), derived from GNSS signal tracking by RO satellites in low Earth orbit (LEO), the orbit positions and velocities of the GNSS transmitter and LEO receiver satellites need to be determined with high and proven accuracy and reliability. Wegener Center's new Reference Occultation Processing System (rOPS) hence aims to integrate uncertainty estimation at all stages of the processing. Here we present a novel setup for precise orbit determination (POD) within the rOPS, which routinely and in parallel performs the LEO POD with the two independent software packages Bernese GNSS software (v5.2) and NAPEOS (v3.3.1), employing two different GNSS orbit data products. This POD setup enables mutual consistency checks of the calculated orbit solutions and is used for position and velocity uncertainty estimation, including estimated systematic and random uncertainties. For LEOs enabling laser tracking we involve position uncertainty estimates from satellite laser ranging. Furthermore, we intercompare the LEO orbit solutions with solutions from other leading orbit processing centers for cross-validation. We carefully analyze multi-month, multi-satellite POD result statistics and find a strong overall consistency of estimates within LEO orbit uncertainty target specifications of 5 cm in position and 0.05 mm/s in velocity for the CHAMP, GRACE-A, and Metop-A/B missions. In 92% of the days investigated over two representative 3-month periods (July to September in 2008 and 2013) these POD uncertainty targets, which enable highly accurate climate-quality RO processing, are satisfied. The moderately higher uncertainty estimates found for the remaining 8% of days (\(\sim\)5-15 cm) result in increased uncertainties of RO-retrieved ECVs. This allows identification of RO profiles of somewhat reduced quality, a potential benefit for adequate further use in climate monitoring and research.  
LEO satellites; precise orbit determination; radio occultation; satellite laser ranging; CHAMP; GRACE; Metop; uncertainty estimation; validation; climate applications  
29 February 2020; Accepted: 31 March 2020; Published: 7 April 2020
## 2 Materials and Methods
### Artificial Micro-Structure Generation: Voronoi Tessellation  
The accurate representation of material grain morphology is crucial for numerically simulating UGWs in poly-crystalline materials. Researchers use imaging methods such as optical and Scanning Electron Microscopy (SEM) to assess grain size, shape, and distribution. Furthermore, artificial micro-structure generation methods can also be used. Regular morphologies like cubic, rhombic, or truncated octahedral grains are suitable for overall material characterizations through homogenization procedures. However, to capture local grain features and predict flaws or local material properties, Voronoi tessellations (VTs) are preferred. VTs are topologically close to poly-crystalline materials (metals and ceramics) [13; 14; 15] due to their statistical characteristics. VTs represent real structures closely by considering the stochastic effect of each grain's crystal anisotropy within the entire aggregate. The grains are generated using completely random seed points, thereby exhibiting a log-normal distribution, as observed in real poly-crystalline materials [13; 14; 15]. A 2D quasi-random generator using [PERSON]'s sequence [37] was employed as a uniform random point generator. Figure 3 illustrates a randomly generated artificial micro-structure, which was produced using the previously mentioned method. Since the present work considers 2D problems, to maintain the random character of the generated micro-structure and the stochastic effects of each grain on the overall behavior of the system, three different cases were considered for each grain [13]. In Figure 3, each grain color represents the different orientation of each grain. A specific orientation was given randomly to each grain and was characterized by a counterclockwise angle \(\theta\), which was measured from the x axis, where \(0^{\circ}\leq\theta\leq 360^{\circ}\), and this rotates the local coordinate system of each grain to a new position \(x_{0}-y_{0}\). The number within each grain represents the grain ID number, hence the number 1 means grain 1 and so on.  
Figure 3: VT scheme and a micro-structure example.  
Figure 2: Multi-region approach.
## 5 Results and Analysis
### Qualitative Comparison of Results  
The visual outputs in Figures 1-3 support our quantitative findings. The numerous false positives produced by the Siamese model (Figure 3) when confusing background features with damage are consistent with its high-precision, low-recall profile seen in Table 4. In contrast, the difference-based model (Figure 2) provides the clearest segmentation with minimal noise, aligning with its more balanced, albeit imperfect performance metrics.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Category** & **Metric** & **Overlay** & **Difference** & **Siamese** \\ \hline \multirow{3}{*}{Localization} & Precision & 0.876 \(\pm\) 0.020 & 0.826 \(\pm\) 0.012 & **0.886 \(\pm\) 0.020** \\  & Recall & **0.864 \(\pm\) 0.023** & 0.704 \(\pm\) 0.024 & 0.553 \(\pm\) 0.075 \\  & IoU & **0.769 \(\pm\) 0.005** & 0.613 \(\pm\) 0.013 & 0.515 \(\pm\) 0.061 \\ \hline \multirow{3}{*}{Damage Assessment} & Precision & N/A & 0.638 \(\pm\) 0.016 & **0.770 \(\pm\) 0.041** \\  & Recall & N/A & **0.445 \(\pm\) 0.051** & 0.225 \(\pm\) 0.090 \\ \cline{1-1}  & IoU & N/A & **0.354 \(\pm\) 0.029** & 0.234 \(\pm\) 0.073 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed diagnostic metrics (mean \(\pm\) standard deviation). The best performance for each metric is highlighted in bold. N/A stands for “Not Applicable” as the Overlay model does not perform damage assessment.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Overlay** & **Difference** & **Siamese** \\ \hline Localization \(F1\) (\(F1_{b}\)) & **0.869 \(\pm\) 0.003** & 0.760 \(\pm\) 0.010 & 0.678 \(\pm\) 0.053 \\ Damage Assessment \(F1\) (\(F1_{d}\)) & N/A & **0.522 \(\pm\) 0.031** & 0.374 \(\pm\) 0.096 \\ Overall \(F1\) (\(F1_{s}\)) & 0.261 \(\pm\) 0.001 & **0.594 \(\pm\) 0.025** & 0.466 \(\pm\) 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison of model performance (F1-scores). The best performance for each metric is highlighted in bold. N/A stands for “Not Applicable” as the Overlay model does not perform damage assessment.
# Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT
## 2 System Model  
For the environment channel model, the ray-tracing method was used to compute the path loss and environment effect at the mm-Wave communication. The frequency response can be expressed as  
\[H(f)=\sum_{i=1}^{N}\alpha_{i}(f)e^{iph(f)} \tag{1}\]  
where \(N\) is the number of the total path, \(f\) is the frequency, \(i\) is the path index of the ray. \(\alpha_{i}\) is the \(i\)-th receiving magnitude which includes the intensity and the phase information. \(ph(f)\) is the phase shift according to the time delay with the contribution of frequency. The time domain impulse response of the equivalent baseband can be obtained by the inverse Fourier transform and can be expressed as follows:  
\[h(t)=\int_{-\infty}^{\infty}H(f)e^{i2\pi ft}df \tag{2}\]  
where \(h(t)\) is the impulse response. For the SWIPT system, the power-splitting with the splitting ratio 0.5 is used. The architecture is shown in Figure 1. Here, the energy harvester and information decoder are behind the energy splitter.  
The wireless harvested power is modeled as  
\[HP=\eta\int_{-\infty}^{\infty}h(t)^{2}dt \tag{3}\]  
Figure 1: The spitting architecture for the SWIPT system.  
where \(\eta\) is the portion of RF signals utilized for power gathering. The information decoder quality BER is used to investigate the inter-symbol-interference in our SWIPT system and it is computed as the following formula [8; 9]:  
\[BER=\sum_{s=1}^{2^{S}}P\bigg{(}\overset{\rightharpoonup}{d_{s}}\bigg{)}\cdot \frac{1}{2}erfc\bigg{[}\frac{V(t=sT_{d})}{\sqrt{2}\sigma}\cdot(d_{s})\bigg{]} \tag{4}\]  
where \(erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-y^{2}}dy\) is the complementary error function and \(\bigg{\{}\overset{\rightharpoonup}{d_{s}}\bigg{\}}=\{d_{1},d_{2},\ldots d_{S}\}\) is the binary sequence.  
The array factor is used to compute the array equivalent pattern and the relationship of the coordinate is shown in Figure 2.  
According to Figure 2, we can express the array factor as  
\[AF(\theta,\phi,f)=\sum_{n=1}^{M}W_{n}e^{\mathrm{j}ka[sin\theta_{n}cos(\phi- \phi_{n})+cos\theta_{n}cos\theta]} \tag{5}\]  
where \(\theta\) polar angle and azimuthal angle \(\phi\) are the spherical coordinates system. \(a\) is the distance from the origin to the antenna position. \(\theta_{n}\) and \(\phi_{n}\) are the spherical coordinates of the transmitting antenna. \(M\) is the total number of antennas in the antenna array. \(k\) is the wavenumber (\(k=\frac{2\pi}{\lambda}\), \(\lambda\) is the wavelength). The adjustment of the phase delay and antenna power can be expressed as  
\[W_{m}=I_{m}e^{iD_{m}(f)} \tag{6}\]  
where \(D_{m}(f)\) and \(I_{m}\) are the excitation current and the phase delay, respectively. The relation between feed length and \(D_{m}(f)\) can be expressed as  
\[D_{m}(f)=-\frac{2\pi f}{c}\sqrt{\varepsilon_{r}}\cdot\ell_{n} \tag{7}\]  
where \(\varepsilon_{r}\) is the relative permittivity of the feed line and \(\ell_{n}\) is the feed length of the transmission line which can be used to adjust the phase delay. c is the light speed.