esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 'One-Sample' Combinatory  
The combinatorial a-Si\({}_{1-x}\)Ge\({}_{x}\) samples with gradient composition ranging in \(0\leq x\leq 1\) were deposited onto 25 \(\times\) 10 mm\({}^{2}\) size Si slabs by DC magnetron sputtering. We followed the \"one sample concept\" combinatory in order to cover the entire composition range of a binary film within a single specimen. This is implemented by means of a scaled-up device originally constructed for the preparation of 3-mm diameter micro-combinatorial TEM samples [20]. The present arrangement (Figure 1) moves a shutter with a 2 \(\times\) 10 mm\({}^{2}\) slot in fine steps above the substrate while the power of the two magnetron sources is regulated in sync with the position of the slot.  
Figure 1: Set-up used for the combinatorial deposition of the a-Si\({}_{1-x}\)Ge\({}_{x}\) layer.  
Using the above method, a combinatorial sample is deposited in form of a 10 mm wide and 25 mm long layer track including a gradient section of 20 mm in length. The gradient part is enclosed in between 2.5 mm long sections of exclusively Si and Ge. The scheme in Figure 1 depicts the experimental arrangement and the construction of the combinatorial specimen.  
A Si-Ge combinatorial sample was deposited as follows: the vacuum system was evacuated to a base pressure of 5 \(\times\) 10\({}^{-6}\) Pa. The substrate was mounted in the combinatorial device and it was load-locked into the chamber. Ar sputtering gas of partial pressure of 2.5 \(\times\) 10\({}^{-1}\) Pa was introduced. The Si and Ge targets were cleaned, behind closed shutters, for five minutes applying 340 W and 100 W magnetron power, respectively. These values were selected by preliminary measurements as maximum (100%) of the regulated power of the individual sources, that provided, equally, 0.45 nm/s deposition rates. Subsequently, the slot's movement of the combinatorial device was started, the shutter of the Si source was opened and Si was deposited at 100% power through the moving slot onto the substrate. This provided the Si section of the combinatorial track. In due time, the power of the Ge source was set to zero and its shutter was opened. It was followed by the gradual increase of the power of the Ge source, simultaneously, with the decrease of the power of the Si source, so the binary, gradient section of the combinatorial sample was deposited. As the power of the Si source arrived to zero and, simultaneously, that of the Ge arrived to its maximum, the Si source was switched off and, finally, the Ge section of the sample was deposited at 100% power of the Ge source.
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM) Analysis  
Figure 4 shows the SEM micrographs of as-cast pure Mg and Mg/Mg-20 GMB hybrid composite. From the figure, the surface of pure Mg is smooth and free of microstructural defects (Figure 4a). The interface between the Mg-shell and the Mg-20 GMB core in the developed as-cast Mg/Mg-20 GMB hybrid composite is shown in Figure 4b. It can be observed that the micrograph is composed of two distinct regions, while the shell region shows Mg, the core region is Mg-20 GMB syntactic foam with white and grey phases. Further, the SEM micrograph also demonstrates a near clear interface  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Shell Region (\(\upmu\)m)** & **Interface Region (\(\upmu\)m)** & **Core Region (\(\upmu\)m)** \\ \hline Mg/Mg-20 GMB & \(35.44\pm 3.19\) & \(34.11\pm 2.30\) & \(33.65\pm 2.45\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average grain size of as-cast Mg/Mg-20 GMB hybrid composite.  
Figure 3: X-ray diffractograms of as-cast: (**a**) pure Mg and (**b**) Mg/Mg-20 GMB hybrid composite.  
with metallurgical bonding between the shell and the core regions barring slight debonding at some interface locations, as observed in Figure 4b.  
The lower homogenization temperature (250 \({}^{\circ}\)C) used in this study ensures greater stability of microspheres in the Mg-20 GMB core and counteracts the healing of inter-particle porosity facilitated by the yielding of microspheres in higher melting temperature matrices. A further concern raised by the high reactivity of Mg is the possible formation of detrimental phases in the interface region between GMB particles and Mg matrix; this has been avoided by eliminating the sintering process for fabricating the core material, which resulted in minimal Mg\({}_{2}\)Si secondary phase formation for Mg-20 GMB core material. Constrained reaction areas are clearly noticeable along the walls of the glass spheres, which might prompt solid interfacial bonding between the matrix and GMB constituents. Minimally fractured GMB particles were observed due to carefully selected and optimized processing parameters. Higher magnification clearly shows the presence of Chinese script shaped Mg\({}_{2}\)Si (Figure 5) in the Mg-GMB region. As Mg is highly reactive, a chemical reaction between Mg and SiO\({}_{2}\) is possible thermodynamically.  
\[2\text{Mg}+\text{SiO}_{2}\rightarrow\text{Si}+2\text{MgO} \tag{2}\]  
\[2\text{Mg}+\text{Si}\rightarrow\text{Mg}_{2}\text{Si} \tag{3}\]
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 2 Theoretical Foundations and Related Works
### Related Works and Research Gap
Using Neo4j, authorsdemonstrated a system that supports sustainable decision-making during the design phase, emphasizing the importance of considerations of early-stage circularity. Another study introduced an automated LCA system that uses a knowledge graph to optimize the efficiency of life cycle modeling and calculations [44]. Its system integrates advanced mapping algorithms and a domain thesaurus to automate background dataset recommendations and improve data accuracy. Validated through case studies, this approach demonstrated significant improvements in response time and recommendation precision compared to existing search systems. This method underscores the potential of knowledge graphs to bridge data gaps and enhance the overall usability of LCA tools. In summary, the existing research focuses primarily on the management of LCI data, often overlooking the significant impact of early-stage decisions made during the design and engineering phases on the environmental outcomes of later stages. Although some studies have explored the integration of entire LCAs, these efforts remain domain-specific and have not been widely adopted. Additionally, they concentrate on stages where sufficient data are available to perform an LCA and utilize KGs to enhance the process in various aspects. In contrast, our proposed methodology broadens the scope to encompass the entire product life cycle, including the engineering, manufacturing, usage, and end-of-life phases. We specifically focus on the engineering phase, where data availability is limited, by recommending the use of advanced language models to estimate missing data. This approach is particularly crucial during the product design phase, where immediate access to data is essential for informed decision-making. By integrating synthetically generated data, our methodology addresses the challenge of obtaining precise environmental data during the early stages of the design, enabling more comprehensive and accurate environmental assessments throughout the product's life cycle.
# SubvectorS_Geo: A Neural-Network-Based IPv6 Geolocation Algorithm
## 3 The SubvectorS_Geo Algorithm
### Algorithm Overview  
Through a large amount of measurement data, the hypothesis that hosts in the same region have a similar delay distribution and similar IP address prefixes, while the closest common router near the edge end of the network on the path can determine the path direction can be verified. Based on this fact, the following hypothesis is proposed: there exists a similar geographical region with similar delay vectors and similar IP address prefixes for hosts in a fine-grained trusted region. The design is based on the idea of layer by layer approximation, and a coarse-grained-fine-grained target IP trusted region is delineated after rule filtering of AS domain names, identification IPs, closest common routers, common prefixes of terminal addresses, and the closest routes with similar hostnames in the order of the measurement paths. The existence of the closest common router specifies the consistency of path directions. The common prefixes of terminal addresses characterize the target IP near path address prefixes that characterize the paths, while the IPv6 address segment in the Whois information verifies the accuracy of the common prefixes. Finally, the target IP geolocation information is determined within a fine-grained trusted region with the result of regional delay similarity evaluation, and the task of IPv6-based IP geolocation is finally completed. Based on this assumption, this paper proposes a new IPv6 geolocation algorithm framework, as shown in Figure 2.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Carrier
#### 3.7.2 Noma  
To minimize interference across user groups, a NOMA-based hybrid beamforming approach is used, which takes advantage of the k-means algorithm. A user's channel correlation value determines whether they are assigned to a group with high correlation or a group with low correlation. As far as energy efficiency is concerned, this method outperforms the status quo of a completely digital architecture [76].  
The alternating optimization method based on reconfigurable intelligent surface NOMA achieves higher results in terms of attainable rate in [77]. A combination of digital and analog beamforming techniques, including optimization of power consumption and phase shifts, allows this to happen. According to [78], NOMA outperforms OMA in terms of sum rate because it minimizes intercluster interference. By changing the users and applying singular-value decomposition (SVD) on each user's channel response, the NOMA-based system outperforms both LOS and NLOS in terms of sum rate [79].
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 1 Introduction
This paper is organized as follows. After this introductive section (Section 1), Section 2 defines the tight coupling filter between IMU, LEO SV Doppler shift and magnetometer data. Section 3 presents the simulation and signal processing tools developed to conduct this study. Section 4 studies the performance of the positioning and the attitude estimation in which the effect of each error source is determined, and the performance in a typical intermediate case and worst case are derived and compared to an INS solution. Section 5 concludes the paper.
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
### Google Smartphone Decimeter Challenge (GSDC)  
GSDC was a Kaggle competition held by Google in 2021 (with a second expected offering in 2022). The objective was to generate the most accurate position solutions for a large number of raw observation data sets collected using Android devices inside a moving vehicle in the San Francisco Bay area [8]. In 2021, two sets of data, training and test, were provided by Google on Kaggle [9]. There were 29 routes and 73 smartphone data in the train set, and 19 routes and 45 data in the test set. There were multiple data for each route because multiple Android devices, spaced approximately 20 cm apart, were logging the data at the same time. During the drive, truth data was also collected using a NovAtel SPAN ISA-100C unit. This is because the SPAN unit is able to provide a horizontal accuracy of up to 0.01 m RMS [10]. As the smartphones were not in the same location as the antenna, their relative displacements with respect to the NovAtel antenna were taken into account for the smartphone truth determination. The training set was provided with the corresponding truth positions, but the truth for the test set was used for the Kaggle leaderboard determination. The data collected was provided in both RINEX and raw GnssLogger format. Although the raw format is what is logged by the smartphone, Google also provided a translation of the raw measurements into the RINEX format for those who preferred the more commonly used format. The measurements were filtered by Google during the conversion, and more details on this are provided in the Data Conversion section.  
The drive routes can be classified into highway, street, and downtown, depending on the amount of expected multipath in the signals [11]. The highway is mostly open-sky, the street is open-sky with some attenuation from trees and nearby buildings, and downtown is heavily affected by attenuation and multipath from high-rise buildings. For the phone setup, the devices were located on the dashboard of the vehicles with no ground plane, which means that they were exposed to significant amounts of additional signal attenuation, and multipath at least at the level as to what would be expected if the antennas were placed on the roof of the vehicle.  
The scores for the competition were determined by averaging the 50 th percentile and 95 th percentile errors of the computed latitude and longitude positions at each time epoch. The errors were computed using truth provided by the NovAtel SPAN unit. As the truth was not released for the test set, the scores were available upon submitting a list of the computed positions to the Kaggle website. In order to allow the participants to test the Kaggle website interface, a baseline results file was provided by Google as well. The algorithms used by Google to obtain the positions in the baseline file were proprietary, but some details about how the results were processed were provided to all participants [12].
## 4 Clustering and Information Content
### Connecting Cluster Variation to Entropy  
Entropy, in the context of information theory, quantifies the amount of uncertainty involved in predicting the value of a random variable. For our clusters, this random variable \(Y\) is the event that a randomly drawn data point belongs to cluster \(y\), and the uncertainty in this event is determined by the probability distribution \(p(y)\) of the clusters. The entropy \(H(Y)\) of this distribution is given by  
\[H(Y)=-\sum_{y=1}^{K}p(y)\log p(y)\]  
where \(K\) is the number of clusters. From our earlier subsection, we related cluster variation \(W_{y}\) to the probability \(p(y)\):  
\[p(y)=\frac{W_{y}^{d/2}}{\sum_{k=1}^{K}W_{k}^{d/2}}\]  
Inserting this expression into our entropy formula, we obtain  
\[H(Y)=-\sum_{y=1}^{K}\frac{W_{y}^{d/2}}{\sum_{k=1}^{K}W_{k}^{d/2}}\log\left( \frac{W_{y}^{d/2}}{\sum_{k=1}^{K}W_{k}^{d/2}}\right)\]Given this expression, a few key insights emerge:  
1. As variation increases: When a cluster has a high \(W_{y}\), meaning its data points are widely dispersed, its contribution to the entropy becomes more significant. This is because the corresponding \(p(y)\) value increases, and the logarithmic term in the entropy formula amplifies this effect.  
2. Influence of dimensionality \(d\): The dimensionality \(d\) of the data can also impact the entropy. As the dimensionality increases, the proportional contribution of each cluster to the entropy can change dramatically, leading to higher uncertainties and thus higher entropy values.  
3. Effect of number of clusters \(K\): The total number of clusters also influences the entropy. With fewer clusters, individual \(p(y)\) values might be larger, leading to potentially larger entropy values. On the contrary, with more clusters, each cluster's probability could be smaller, potentially reducing the overall entropy.  
In essence, the cluster variation provides a proxy measure for the entropy of the clustering. Clusters with high variation (indicative of capturing a wider range of data points with varying characteristics) tend to contribute more to the entropy, symbolizing higher unpredictability or uncertainty in data point assignment to these clusters.  
It is also worth briefly noting the theoretical congruence of our approach with frameworks such as information bottleneck (IB) theory [24] and maximum coding rate reduction [25]. In the context of the IB theory, our clustering strategy can be considered as an information compression step, encapsulating the vast space of adversarial examples into more informationally dense cluster representatives. This aids the model in navigating a potentially less complex and more informative adversarial landscape. From the perspective of maximum coding rate reduction, our method could encourage the model to preserve meaningful mutual information between challenging adversarial examples and the output labels, thereby refining the model's ability to focus on relevant'signals' over 'noise'. However, constructing a rigorous theoretical framework around these ideas would require extensive analysis and is considered as a direction for future research.
## 2 Results
### Nitrogen Metabolism Pathway in Resistant and Susceptible Chinese Cabbage Lines  
Considering that nitrogen metabolism might affect the development of petiole spots, we screened the differentially expressed genes and identified 11 genes that were strongly correlated with the nitrogen metabolism (Pearson correlation coefficients > 0.8, \(p\)-values < 0.05) at the transcriptomic or proteomic level. These genes encompass one nitrate transporter (NTR) gene (Brassica_rapa_newGene_963), three nitrate reductase (NR) genes (BraA07g026900.3c, BraA02g024390.3c, BraA07g040960.3c), two nitrite reductase genes (BraA07g005240.3c, BraA09g010360.3c), three glutamine synthetase (GS) genes (BraA10g023190.3C, BraA04g010460.3C, BraA02g006550.3), one glutamate dehydrogenase (GDH) gene (BraA01g043400.3C), and one NADH-dependent glutamate synthase (GOGAT) gene (BraA03g014440.3C).  
According to gene function analysis, the data of gene expression showed that the expression of Brassica_rapa_newGene_963 (NTR), BraA10g023190.3C, BraA04g010460.3C, BraA02g006550.3C (GS), and BraA01g043400.3C (GDH) was higher in CKA compared to those in CKB. Furthermore, expression levels of those genes were lower TB compared to CKB. Conversely, the expression levels of BraA07g026900.3c, BraA02g024390.3c, and BraA07g040960.3c (NR) were lower in Chinese cabbage that was resistant to petiole spot when compared to the sensitive cabbage. Additionally, their expression was lower in sensitive Chinese cabbage treated with high nitrogen as compared to the control. Notably, both GS and GDH are important enzymes in nitrogen metabolism, as they have a crucial function in converting inorganic nitrogen to organic nitrogen, thus reducing the toxicity of NH4+. In general, the resistant Chinese cabbage line has high assimilation ability of NH4+, highlighting the importance of GS and GDH in the development of petiole spot in Chinese cabbage.  
To further corroborate our hypothesis, we assessed the ammonium (NH4+-N) levels as well as enzyme activities of GS and GDH in Chinese cabbage varieties that exhibit resistance and susceptibility to petiole spot upon high nitrogen treatments. We found a notably increased level of NH4+ in the susceptible Chinese cabbage compared to the resistant variety (Figure 5B). Moreover, the distinction between the resistant and susceptible lines was magnified when there was exposure to high levels of nitrogen. The enzyme activities of GS and GDH in the susceptible sample (B) were notably low compared to those in the resistant sample. The enzyme activities of NH4+ glutamine synthetase and glutamate dehydrogenase in the susceptible material experienced a slight increase following high nitrogen treatment (Figure 5C,D). The results demonstrate that low activities of GS and GDH are responsible for the accumulation of NH4+ in the susceptible line. These were exacerbated by high levels of nitrogen fertilizer, thereby intensifying the occurrence of petiole spots.
## 3 Theoretical Analysis
### System Model  
The system is modeled as the transmitter transmitting the input signal \(X\) of block orthogonal codes through an additive white Gaussian noise (AWGN) channel and the receiver receiving the signal \(Y\). One purpose of the model is to find the symbol error probability of the block orthogonal codes. From the symbol error probability, we find the packet error probability of the block orthogonal codes (Section 3.2) and compare it with existing schemes. The received signal \(Y\) through the AWGN channel is given by:  
\[Y(i)=\max\{\beta X(i)\otimes S_{pattern}^{ideal}(\tau-i)\} \tag{1}\]  
where \(\beta\) is the attenuation from the AWGN channel with a Gaussian distribution, \(S_{pattern}^{ideal}\) is the ideal pattern of the block orthogonal codes' symbols for data bit zero and one, \(\tau\) is the correlation time period and \(\otimes\) is the correlation operation.  
The block orthogonal codes use chips per symbol (\(N_{cps}\)) and oversampling factor (\(N_{\alpha s}\)) to suppress noise. The \(N_{os}\) reduces the noise variance (noise power) by a factor of \(1/N_{os}\). The block orthogonal code can be represented as the \(M\)-ary block orthogonal codes \(O(N_{cps},k)\) with diversity \(L=N_{cps}/M\), where \(M=2^{k}\), and \(N_{cps}\) is the number of chips per symbol.  
The average signal-to-noise ratio per chip, SNR\({}_{c}\), is given by:  
\[\text{SNR}_{c}=\frac{k}{L}\text{SNR}_{b}\,N_{os}=\frac{k\,MN_{os}\,\text{SNR} _{b}}{N_{cps}} \tag{2}\]where \(\text{SNR}_{b}\) is the signal-to-noise ratio per bit. The symbol (code word) error probability, \(p_{es}^{orth}\) for \(M\)-ary block orthogonal signaling is given by:  
\[p_{es}^{orth}=1-\sum_{i=0}^{N_{cc}}{N_{cps}\choose i}p_{ec}^{ook^{i}}(1-p_{ec}^{ ook})^{(N_{cps}-i)} \tag{3}\]  
where \(N_{cc}\) is the number of chips that can be corrected by \(M\)-ary block orthogonal signaling:  
\[N_{cc}=\left\lfloor\frac{d_{min}-1}{2}\right\rfloor=\left\lfloor\frac{N_{cps}} {M}-\frac{1}{2}\right\rfloor \tag{4}\]  
The minimum Hamming distance \(d_{min}\), with diversity \(L\), is given by:  
\[d_{min}=2L=2\frac{N_{cps}}{M} \tag{5}\]  
the chip error probability of coherent OOK modulation, \(p_{ec}^{ook}\), through a matched filter is given by [22]:  
\[p_{ec}^{ook}=Q(\sqrt{\text{SNR}_{c}}) \tag{6}\]  
The value of \(\text{SNR}_{c}\) is given by:  
\[\text{SNR}_{c}=\frac{k\,MN_{os}\text{SNR}B_{s}}{N_{cps}R} \tag{7}\]