esa-sceva/satcom-chunk-collection huggingface dataset:
# Life Cycle Carbon Assessment of Mortars with Carbonated and Non-Carbonated Recycled Aggregates
## 3 Life Cycle Inventory  
### Raw Materials Production (A1)  
The mortars analysed in this research are composed of sand, recycled aggregates, cement and admixtures. The global warming potential (GWP) environmental impact associated with these raw materials (A1) was quantified through site-specific data, scientific research data, EDPs and databases (Table 2).  
The GWP associated with sand utilization was obtained from the European Life Cycle Database (with the designation of \"Sand 0/2 mm, wet and dry quarry, production mix, at plant, undried RES\"). The environmental impact associated with CEM II/B-L 32.5 N was obtained from an EPD of the European Cement Research Academy [3]. Similarly, the GWP impact of the admixtures used came from three EPDs of the European Federation of concrete admixtures associations. The information about the environmental global warming potential impact of cement, sand, admixtures and recycled aggregates presented in this paper was calculated by the authors using Simapro software (using version 3.05 of CML 2 baseline 2000 released by CML in April 2013, and CED version 1.10).  
Table 3 summarises the GWP of sand, cement and admixtures.  
The GWP impact associated with recycled aggregates in the raw materials component (A1) includes the CDW treatment. This treatment includes metal separation, plastics separation, paper separation (in some recycling plants), crushing processes and sieving processes [26]. The energy used to crush CDW depends on the type of aggregates used in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} Site-specific data (adapted \\ from [PERSON] et al. [7]) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Source and location of the databases used.  
mortars as well as their volume of incorporation. Concrete aggregates (CAs) and recycled concrete aggregates (RCAs) spend more energy to be treated (crushed and sieved) than mixed recycled aggregates (MRAs). Part of the energy used in Portugal consumes fossil fuels, and so in this sense the recycling process that aggregates pass through contributes to the GWP. This difference was considered in this LCA. The total GWP associated with recycled aggregates, carbonated and non-carbonated, is presented in Table 4.  
On the other side, the environmental impacts associated with sand, namely natural extraction, are eliminated when sand is replaced with recycled aggregates. On the other hand, besides the GWP impacts associated with their treatment, carbonated aggregates are able to absorb CO2 through carbonation. The CO2 uptake associated with carbonated aggregates counts as a benefit in their GWP impact. This uptake depends on the recycled aggregates used. In Table 5 the CO2 uptake of each recycled aggregate is summed up, based on a previous study by [PERSON] et al. [19].
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 2 Preliminaries
### Timing Attacks on CDT Sampler  
As stated repetitively, the Gaussian sampler is the core element in implementing lattice-based cryptography. If an attacker knows every sampled value from the Gaussian sampler, then the secret key is revealed by simple linear algebra. Hence, resistance against a potential side-channel attack is necessary. In this section, we mainly focus on timing attack performed in the CDT sampler and its countermeasures.  
The timing attack proposed by [PERSON] et al. is a side-channel attack performed by measuring the time of operation of a cryptographic device [22]. The attack exploits the data-dependent characteristic of an implementation that leads to the revelation of the secret key. For example, if an implementation terminates or executes an algorithm depending on the input data, this may be vulnerable to the timing attack. A timing attack on a CDT sampler is performed as follows. Recall that steps 4 to 6 in Algorithm 1 are repeated until it finds \(k\) such that \(\Psi[k]\) is smaller than the selected random \(S\). Let \(n\) be the length of a CDT table. By measuring its time, an attacker can classify the timing of an algorithm to \(n\) different groups, where the shortest sampling time will most likely have output zero. This means that naive implementation allows an attacker to determine the sampled value which can lead to the secret key. Hence, [PERSON] et al. proposed a constant-time CDT sampler which guarantees that no information is leaked through timing analysis [7]. Below is the pseudocode of the constant-time CDT sampler implemented in cryptosystems such as FrodoKEM [18].  
```
0: CDT table \(\Psi,\sigma,\tau\)
0: Sampled value \(S\)
1:\(S\leftarrow[0,\tau\sigma)\)
2:\(sign\leftarrow[0,1]\cap\mathbb{Z}\) uniformly at random
3:\(k\gets 0\)
4:while \((S>\Psi[k])\)
5:\(k++\)
6:end while
7:\(S\leftarrow((-sign)\wedge k)+sign\)
8:return \(S\)
```  
**Algorithm 1** CDT sampling  
Note that, instead of terminating the algorithm when the index is found, Algorithm 2 searches through the whole table. Algorithm 2 adds 0 to a variable \(S\) when the selected random value is less than the table value and adds 1 when the selected random value is greater than the table value. Therefore, the returned value can be equal to the index \(i\) such that \(rnd<\Psi[i+1]\) even though the whole table has been searched. The timing attack can be eliminated since the execution time of the algorithm is independent from the selected random value.  
```
0: CDF table \(\Psi\) of length \(\ell\), \(\sigma\), \(\tau\)
0: Sampled value \(S\)
1:\(S\gets 0\)
2:\(rnd\leftarrow[0,\tau\sigma)\cap\mathbb{Z}\) uniformly at random
3:\(sign\leftarrow[0,1]\cap\mathbb{Z}\) uniformly at random
4:For \(i=0\) up to \(\ell-1\) do
5:\(S\leftarrow(\Psi[i]-rnd)\gg 15\)
6:\(S\leftarrow((-sign)\wedge S)+sign\)
7:return \(S\)
```  
**Algorithm 2** Constant-time CDT sampling
# Research on Part Tolerances for RV-40E Reducer Based on Virtual Prototype Technology
###### Abstract  
The RV-40E reducer is the representative model of the a typical RV reducer, where transmission accuracy and manufacturing cost are two important optimization indicators, while transmission accuracy depends on both the selection of accuracy levels for components, such as bearings, and the design of tolerance values for parts such as cycloidal gears. The selection of accuracy levels, such as bearings, is relatively simple, and studying manufacturing costs requires the use of a large amount of data from manufacturing equipment and processing methods. Therefore, this article mainly studies the design optimization problem of tolerance values closely related to transmission accuracy. The RV-40E reducer is designed based on the experience of engineers. So far, it is unclear whether the tolerance value design of parts such as cycloidal gears meets the optimization requirements of transmission accuracy. This article first explains the research background and the necessity of tolerance value design for RV-40E components. Next, it details the reducer's structural composition and lists specific part tolerance values. Subsequently, a virtual prototype is established to evaluate transmission errors under different component and part combinations. Finally, through the design of experiment analysis, optimal tolerance values are derived for selected parts as recommended in the final table. The results demonstrate that the optimized tolerance values for RV-40E reducers are basically consistent with the empirical tolerance values designed by engineers, achieving similar results.  
part tolerance; transmission accuracy; virtual prototype; design of experiment
## 4 Experiment
### Datasets and Scenarios  
The experimental data is obtained from a comprehensive open-source dataset named pNEUMA, which contains approximately 500,000 real trajectories collected from the central business district of Athens, as displayed in Figure 4[8]. These trajectories were derived from nearly 10 h of aerial video captured at 25 Hz by two five-drone swarms (H1 and H2) during the morning peak hours of weekdays and calibrated in the World Geodetic System 1984 (WGS84). The pNEUMA can provide vehicle-state information at any given timestamp and corresponding vehicle-type information for each trajectory. The dataset can be downloaded from [[https://open-traffic.epfl.ch](https://open-traffic.epfl.ch)]([https://open-traffic.epfl.ch](https://open-traffic.epfl.ch)) (accessed on 23 November 2019).  
The official website of pNEMUA does not directly offer high-precision maps, so we download the OpenStreetMap (OSM) based on its WGS84 boundary coordinates, as shown in Figure 5a. Then, the scenario designer in the composable benchmarks for motion planning on roads (CommonRoad) is employed to convert the OSM into a CommonRoad map [9]. As demonstrated in Figure 5b, the zones of drones 1, 2, 3, and 4 are selected to establish our research scenarios because these zones are densely populated with signalized intersections. Next, the trajectories of pNEUMA are smoothed by the Unscented Kalman Filter and matched with the CommonRoad map to reconstruct the actual traffic scenario, while simultaneously computing the azimuths [35]. As shown in Figure 5c, two two-phase signalized intersections and two four-phase signalized intersections are selected as the experimental scenarios, respectively named Scenario 1 and Scenario 2.  
Utilizing the built-in map converter, the CommonRoad map is converted into the Lanelet2 format. The Lanelet2 map discretizes the road space into a series of lane segments, as depicted in Figure 5d. The lane segment size is set to \(15\pm 2.5\) m in length and the same width as the corresponding lane, providing sufficient space for a bus or heavy vehicle. It is worth noting that the connectivity matrix of lane segments is determined by both their topological relationships and signal phases at signalized intersections at each time step.  
Figure 4: The data collection area in pNEUMA and the zone for each drone in the swarms.
[PERSON] and [PERSON] [31] analyzed soil moisture patterns also by EOF in two field sites during the Southern Great Plains (SGP) 1997 hydrology experiment and showed that the patterns are mostly controlled by topography-related factors. [PERSON] and [PERSON] [32] used wavelet analysis to determine the contribution of biophysical factors to the redistribution of soil moisture across varying ranges of scales with airborne soil moisture data collected during the SMEX02, SMEX04, and SGP1997 campaigns, and found that the dominant contribution differs with the study areas. [PERSON] et al. [33] investigated the controls of precipitation and temperature on the Essential Climate Variable Soil Moisture (ECV_SM) in the Trium River basin of China and found that precipitation is a more important factor than temperature in the controlling of soil moisture at a large scale. As the soil moisture spatiotemporal variability is determined by the complex interaction of various geophysical controls, the subject still needs debating in further research. The main objective of this study was to analyze the spatiotemporal patterns and the physical controls of soil moisture derived from the Sentinel-1A data in the study site. A LUT method was adopted to estimate soil moisture from the time-series Sentinel-1A images. When considering that the number of ground-based measurements in the study site was not sufficient, we used the ongoing microwave products to assist with the evaluation of the Sentinel-1A soil moisture at a coarse scale. The EOF method was then conducted to derive the spatiotemporal patterns of the Sentinel-1A soil moisture. The relationships between the spatiotemporal patterns and factors including topography, soil texture, vegetation, and temperature were finally analyzed at last to investigate the contributions to the soil moisture variability in our study site.
## 2 Results and Discussion
### Volatile Composition of Celery Samples
Samples 12, 25, 25x12 and 12x22 were separated across F1, whereas samples 12, 22 and 22x12 along F2, respectively. The observation plot confirmed the findings presented in Table 2, where samples 12 and 22x12 expressed a strong association with many volatile compounds due to the high abundance identified. Conversely, samples 25 and 25x12, observed on the opposite side of the observation plot, displayed little or weak association with all volatile compounds (Figure 1). Due to the low abundance of volatile compounds, we can assume that these genotypes would be perceived as less aromatic when compared to the other genotypes. The hybrid 12x22 was positioned in the middle of the observation plot, displaying a stronger association with volatile compounds than genotype 25 and its hybrid 25x12; however, the relative abundance expressed within this hybrid remains consistently lower than 22x12 in all compound groups, except for phthalides. Thus, we could assume that this hybrid (12x22) was less aromatic than 22x12 but still had the typical, distinctive celery aroma. Comparing the aroma profile between the three parental genotypes and the hybrid lines, genotype 12 and hybrid 22x12 expressed the highest relative abundance of volatile compounds and it can be hypothesised that these will be more aromatic genotypes in comparison to the other samples. The current results (Table 1) confirmed previous work [12; 13] where genotype 12 was shown to be very aromatic with strong flavour associations but low scoring in mouthfeel attributes such as crunchy and moist yet scored high for stringiness. Genotype 25 was reported to be less aromatic with a distinct cucumber flavour but was profiled as very crunchy, moist and with a firm first bite. The volatile content of genotype 22 was not significantly higher to genotype 12 or lower than 25 [12; 13]. Overall, genotype 25 and hybrid 25x12 displayed clear maternal inheritance within the volatile content in terms of the compounds identified and their relative abundance. The high abundance of volatile compounds identified in genotype 12 appeared to have been inherited by hybrids 22x12 and 12x22 (Table 1). This relationship is also clear in the observation plot (Figure 1), where genotypes 12 and 22 with 22x12 and 12x22 expressing strong associations with all volatile compounds identified. We hypothesised that the parental genotypes would perform as previously [12; 13] and maternal and paternal inheritance patterns become clearer upon sensory assessment, identifying phenotypic similarities between the parents and hybrids. Therefore, sensory evaluation was performed using a trained panel to further investigate these assumptions.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 5 Simulations
### Analysis of IA Simulation
The experiment results listed in Table 7 and Figure 17 show that there exists approximate linearity between number of sessions and routing links with number of vehicles. As shown in Figure 18, with the increase in the number of vehicles, proportion of routing task that base station participated also increases, which is is due to MIMO and large broadcast coverage of base station. Besides, _PTU_ keeps increasing along with increase of vehicle number. The proportion of idle time slots as shown in Table 8, could be 45% at 30 vehicles. Even in 70 vehicles simulation, this number could keep 15%. For comparisons, the time slots of MIMO without IA are almost used up when there are 70 vehicles, which would lead communication congestion. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Vehicles**} & \multicolumn{2}{c}{**Number of Transmit**} & \multirow{2}{*}{**Number of Sessions**} & \multicolumn{2}{c}{**Number of Base**} \\  & **Links (Z)** & & **Station Session** \\ \hline
30 & 61 & 82 & 53 \\
40 & 93 & 110 & 71 \\
50 & 119 & 148 & 99 \\
60 & 135 & 196 & 137 \\
70 & 220 & 244 & 175 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The number of Sessions and Transmit Links. Figure 17: Number of Route Links and Sessions. Figure 18: _PTU_ and Some Percentages of Sessions as the number of vehicles increases. As shown in Table 9, in simulation with 30 vehicles, the proportion direct transmission is relatively high, mainly due to fewer vehicles within safety distance. And communication delay could be reduced by 45%. However, with the increase of vehicle number, the proportion of assisted transmission is getting higher, which can reach 50%. Routing communication is reduced by an average of about 20% compared with no assisted transmission.
## 7 Open-Pit Mine Application
### Inhanced Architecture toward Smart Grid Open-Pit Mine  
The proposed application of the SMES is on an experimental open-pit mine application, using power meters of different machines, mainly, draglines, jaw crushers, conveyors, bucketwheel reclaimers and stackers, to collect energy consumption data and then then understanding their behaviors at different times of year and different production constraint scenarios. These power meters are interfaced with programmable logic controllers (PLC), and distributed control systems (DCS) [33], which apply the decisions and control directly the motors in order to optimize energy consumption. The same type of power meters shown in Figure 11 are connected to renewable energy sources in case the open-pit mine has photovoltaic, wind power, electric vehicles [34], or any other type of energy storage, for a global monitoring system integrating sources and loads. As shown in Figure 21 a smart micro grid dedicated for mining industry open pit is developed integrating different communication protocols and energy flow.
# Developmental scRNASeq Trajectories in Gene- and Cell-State Space--The Flatworm Example
## 2 Materials and Methods  
### scRNA Seq Data of Planarian Development  
Single-cell RNA Seq data of a whole planarian Schmidtea mediterranea were taken from the publication of [PERSON] et al. [29] as read-counts (in units of RPKM) of more than 28,000 transcripts per cell. Originally these data were measured by highly parallel droplet-based single-cell transcriptomics, Drop-seq [33], to study different cell types and progenitor stages present in adult planarians. In total 21,612 cells were captured in 11 independent experiments. The data set corresponds to five wild-type samples (10,866 cells), two RNA interference (RNAi) samples (3314 cells), a high-DNA content G\({}_{2}\)/M population corresponding to cycling planarian stem cells (typically defined as x-ray-sensitive  
Figure 1: Analysis of developmental paths (schematic overview). **(A)** Single-cell transcriptome data are provided as a matrix of cells (columns) versus transcripts (rows). Similarity sorting along genes and cells is applied to obtain gene- and cell-state trajectories, respectively. **(B)** Multibranched cell-state trajectories order cells along a similarity measure called pseudotime (pt). They are visualized either as hierarchical tree or as three-dimensional manifold using, e.g., “URD”-plots [29]. They reflect transcriptional changes upon development and differentiation of cells. **(C)** The gene-state trajectories were generated in self-organizing map (SOM)-space making use of a network formed between clusters of co-regulated genes marked with letters in the figure. Trajectories follow paths of maximum expression linking source (stem cells) and sink (differentiated tissues) nodes along the edges of the net. **(D)** RNA velocity analysis delivers vector fields in cell and gene phase space. Each vector points in direction of increasing transcript abundance either between cell states or between gene states. In fact, RNA velocity forecasts the “future” transcriptional state of each gene. The RNA-velocity vector of the cells is formed by the RNA-velocities of all genes as components while the RNA-velocity vector in gene space is composed by the RNA-velocities of all genes in the respective local gene cluster.  
\"X1 cells\"; 981 cells), and three wild-type regeneration samples (6451 cells) [34; 35]. As in the original publication [29], we analyzed all single-cell data sets together. Cell types were assigned to the previously annotated 51 cell clusters. Cell type identities of neoblast, neural, epidermal, secretory, muscle, gut, and protonephridia cells were further elucidated by examining known marker genes based on previous knowledge [29]. We applied the same color scheme as in the figures shown in [29] throughout this publication for direct comparison.
# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
## 2 Materials and Methods
### Methods
#### 2.2.3 Rheological Analyses  
The extracted binders were collected and rheologically characterized using a dynamic shear rheometer (DSR), as per ASTM D7175-15 [35]. To ensure that the results were repeatable, each binder was tested with two replicate samples, and the average results were calculated.  
Rutting Parameter  
Binder samples--extracted from mixtures aged 0.00 to 0.04 years--measuring 1 mm in thickness and 25 mm in diameter were tested for the \(G^{*}\)/sin\(\delta\) at 64 \({}^{\circ}\)C; 10 rad/s frequency; and 10% shear strain.  
Fatigue Cracking Parameter  
The fatigue cracking resistance values of binders extracted from field mixtures aged over 0.04 years were evaluated. The binder fatigue resistance was investigated for specimens with a diameter of 8 mm and a thickness of 2 mm at 25 \({}^{\circ}\)C, 10 rad/s frequency, and 1% shear strain using the \(G^{*}\).sin\(\delta\).  
The resistance to block cracking was assessed on samples tested for fatigue cracking. At 15 \({}^{\circ}\)C, with a frequency of 0.005 rad/s and 1% shear strain, the block cracking or Glover-Rowe (G-R) parameter was assessed and computed using Equation (5) [36; 37; 38].  
Using Equation (5), the G-R parameter in kPa was determined as follows:  
\[\text{G}-\text{R}=G*~{}(\frac{(\cos\delta)^{2}}{\sin\delta}), \tag{5}\]  
where\(G^{*}\) is the complex shear modulus (kPa), and \(\delta\) is the phase angle (\({}^{\circ}\)).  
Thermal Cracking Parameter  
Thermal cracking resistance was assessed on samples tested for fatigue and block cracking. The test was conducted on a DSR using 4 mm plates for samples with a thickness of 1.75 mm. A frequency sweep test was conducted on the binders at angular frequencies of 50.00, 39.81, 25.12, 15.85, 10.00, 6.31, 3.98, 2.51, 1.58, 1.00, 0.63, 0.39, 0.25, 0.15, and 0.10 rad/s, with a strain value of 0.001% at temperatures ranging from \(-\)24 to 12 \({}^{\circ}\)C in 6 \({}^{\circ}\)C increments, while maintaining a normal force of 1 \(\pm\) 0.1 N.  
Thermal cracking parameter (\(\Delta T_{c}\)) was assessed using Equation (6), as follows:  
\[\Delta T_{c}=T_{c,S}-T_{c,m}, \tag{6}\]  
where  
\(T_{c,S}\) is the continuous temperature based on the binder's stiffness and was calculated using Equation (7) as follows:  
\[T_{c,S}=T_{1}+\frac{(T_{1}-T_{2})(\log 300-\log S_{1})}{\log S_{1}-\log S_{2}}-10 \tag{7}\]  
\(T_{c,m}\) is the continuous temperature based on the binder's \(m\)-value and was evaluated using Equation (8) as follows:  
\[T_{c,m}=T_{1}+\frac{(T_{1}-T_{2})(0.3-m_{1})}{m_{1}-m_{2}}-10 \tag{8}\]  
\(T_{1}\) is the temperature at which \(S(t)\) and \(m\)-value passed (\(S(t)\leq\) 300 MPa and \(m\)-value \(\geq\) 0.3); \(T_{2}\) is the temperature at which \(S(t)\) and the \(m\)-value failed (\(S(t)>\) 300 MPa and \(m\)-value \(<\) 0.3); \(S_{1}\) represents the \(S(t)\) value at \(T_{1}\);  
\(S_{2}\) symbolizes the \(S(t)\) value at \(T_{2}\);  
\(m_{1}\) corresponds to the \(m\)-value at \(T_{1}\);  
\(m_{2}\) stands for the \(m\)-value at \(T_{2}\).