esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 2 Materials and Methods
### Methods
#### 2.3.2 Manufacture of Asphalt Mixtures and Production of Specimens  
The experimental plan started with the manufacture of the asphalt mixtures according to EN 12697-35 [42] in a heated planetary mixer. Apart from the reference mixture, each type of plastic waste was incorporated to produce three alternative blends by replacing 4%, 6%, and 8% of the bitumen mass by plastic waste. In total, the study involved the assessment of 13 different compositions. The mixtures were produced by the so-called dry process, which consists in adding plastic waste into the mixer bowl during the manufacturing process. The mixing temperature varied between 165 and 170 \({}^{\circ}\)C.  
Figure 2: Types of plastic waste used as polymers in the studied asphalt mixtures in the present study.  
In the first stage of the study, the analysis of the mixtures' volumetric and Marshall properties was carried out on cylindrical specimens with 101.6 \(\pm\) 0.1 mm diameter and 63.5 \(\pm\) 2.5 mm height, compacted according to EN 12697-30 [43], by applying 75 blows to each face of specimens. For water sensitivity evaluation, the same type of specimens was used. Evaluating the mixtures' workability also required the production of cylindrical specimens in a gyratory compact with a diameter of 150 mm and a height of 115 mm (EN 12697-31 [44]).  
The study on permanent deformation resistance required moulding further cylindrical specimens, with a diameter of 150 mm and height of 60 mm. A vibrating hammer compacted the samples by applying from 2000 to 4000 pulses per min, according to the principles provided by EN 12697-32 [45]. The equipment's circular tamping foot was used for 1.5 min on each specimen's face. Laboratory prismatic specimens (400 \(\times\) 52 \(\times\) 52 mm\({}^{3}\)) were cut from slabs compacted in a mould with a two-axle tandem-vibrating roller.  
Figure 4 illustrates the production of mixtures and specimens used to evaluate volumetric properties and mechanical performance throughout the study.
# A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces
## 4 Experiment and Analysis
### Weighting Strategy Steps
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **Ceiling** & **Floor** & **Wall** & **Beam** & **Column** & **Window** & **Door** & **Table** & **Chair** & **Sofa** & **Case** & **Board** & **Clutter** \\ \hline IM & 0.911 & 0.910 & 0.912 & 0.915 & 0.999 & 0.990 & 0.971 & 0.996 & 0.998 & 0.759 & 0.989 & 0.976 & 0.875 \\ IoU & 0.957 & 0.956 & 0.964 & 0.965 & 0.998 & 0.997 & 0.989 & 0.997 & 0.999 & 0.862 & 0.998 & 0.997 & 0.954 \\ CPA & 0.094 & 0.087 & 0.086 & 0.079 & 0.268 & 0.252 & 0.176 & 0.316 & 0.309 & 0.318 & 0.174 & 0.218 & 0.045 \\ \hline \end{tabular}
\end{table} 
Table 6: Normalized decision matrix. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & **Ceiling** & **Floor** & **Wall** & **Beam** & **Column** & **Window** & **Door** & **Table** & **Chair** & **Sofa** & **Case** & **Board** & **Clutter** \\ \hline \(c_{j}\) & 0.029 & 0.024 & 0.030 & 0.001 & 0.0008 & 0.0045 & 0.013 & 0.035 & 0.039 & 0.031 & 0.007 & 0.003 & 0.0001 \\ \hline \end{tabular}
\end{table} 
Table 7: Standard variances of criteria. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & **Ceiling** & **Floor** & **Wall** & **Beam** & **Column** & **Window** & **Door** & **Table** & **Chair** & **Sofa** & **Case** & **Board** & **Clutter** \\ \hline \(c_{j}\) & 0.029 & 0.024 & 0.030 & 0.001 & 0.0008 & 0.0045 & 0.013 & 0.035 & 0.039 & 0.031 & 0.007 & 0.003 & 0.0001 \\ \hline \end{tabular}
\end{table} 
Table 8: Distance correlations between different pairs of criteria. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline  & **Ceiling** & **Floor** & **Wall** & **Beam** & **Column** & **Window** & **Door** & **Table** & **Chair** & **Sofa** & **Case** & **Board** & **Clutter** \\ \hline Ceiling & 1.000 & 0.199 & 0.529 & 0.131 & 0.965 & 0.466 & 0.822 & 0.180 & 0.249 & 0.473 & 0.673 & 0.092 & 0.213 \\ Floor & 0.199 & 1.000 & 0.263 & 0.722 & 0.323 & 0.480 & 0.124 & 0.110 & 0  
Table 9 indicates that among all the labels, table, chair, and wall hold the top three rankings in terms of weights, which aligns well with the practical requirements of point cloud registration in 3D indoor spaces. In the Stanford Large-Scale 3D Indoor Spaces Dataset, chairs, walls, and tables are indispensable objects that occupy the central positions in the indoor space and the majority of the spatial volume. Improving the accuracy of point cloud registration for these objects will correspondingly enhance the registration accuracy for the entire 3D indoor space, as registration transforms the entire indoor space into a rigid structure. On the other hand, the weight assigned to clutter is minimized to a value close to zero, effectively minimizing the negative impact of noise and incorrectly matched points that will reduce registration accuracy.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 4 Numerical Results
### Channel with Memory (M-BEC)  
In this subsection, we compare performance of the binary WA codes and the RS-convolutional codes on the erasure channel with memory. We model this channel as a discrete approximation of the fading channel. The Rice fading channel model is determined by signal-to-noise ratio \(E/N_{0}\), correlation coefficient \(\rho\) and coefficient \(k^{2}\) characterizing the ratio of energies of regular and random components of the received signal. If \(k=0\), we obtain the Rayleigh fading channel. We approximate this channel by the Gilbert-Elliott model using technique in [19; 20]. See also the tutorial in [30] for overview of approaches to constructing finite-state discrete approximations of analog wireless channel models.  
In Figure 8, the same codes with SWML-decoding are compared over the Gilbert-Elliott model approximating the Rayleigh fading with \(\rho=0.8\), \(\rho=0.9\), and \(\rho=0.95\)[19; 25]. The decoding performance rapidly degrades with increasing correlation coefficient. Larger delay codes are superior with respect to the codes with \(L=2\). The obtained coding gain decreases from 2 dB to 1 dB with increasing \(\rho\) from 0.8 to 0.95.  
Figure 8: Comparison of the binary rate \(R=3/4\) WA code with the rate \(R=3/4\) RS-convolutional code for different correlation coefficients of the fading channel.  
Figure 7: Comparison of RS-block codes and the RS-convolutional code for different delays.
# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 3 The Proposed Methodology
### Hash Representation Learning  
Beyond all doubt, preserving semantic consistency between original instances and their hash representations is a key factor of high-quality hash code generation. In other words, the more similar semantically the instance pair (\(\mathbf{o}_{i}\) and \(\mathbf{o}_{j}\)) is, the smaller the Hamming distance between their hash codes should be, and vice versa. To this end, we construct the hash representation learning objective function by integrating the following three losses: (i) _an intra-modal semantic similarity loss_, (ii) _an inter-modal semantic similarity loss_, and (iii) _a multi-label weighted contrastive loss_.  
**Similarity Matrices.** To construct intra- and inter-modal semantic similarity loss, the cross-modal semantic similarities should be represented firstly. Specifically, three similarity matrices, i.e., \(\mathcal{S}^{vv},\mathcal{S}^{tt},\mathcal{S}^{vt}\in\mathbb{R}^{N\times N}\), are constructed by the inner product between normalized hash representations: \(\mathcal{S}^{vv}_{ij}=\bar{\mathbf{x}}^{\intercal}_{v_{i}}\mathbf{x}_{v_{j}}\), \(\mathcal{S}^{tt}_{ij}=\bar{\mathbf{x}}^{\intercal}_{i}\mathbf{x}_{t_{j}}\), and \(\mathcal{S}^{vt}_{ij}=\bar{\mathbf{x}}^{\intercal}_{v_{i}}\mathbf{x}_{t_{j}}\), where \(\mathcal{S}^{vv}_{ij}\), \(\mathcal{S}^{vt}_{ij}\) and \(\mathcal{S}^{vt}_{ij}\) are the semantic similarity between \(\mathbf{v}_{i}\) and \(\mathbf{v}_{j}\), and \(\mathbf{t}_{i}\) and \(\mathbf{t}_{j}\), as well as \(\mathbf{v}_{i}\) and \(\mathbf{t}_{j}\), respectively.  
**Intra-Modal Semantic Similarity Loss.** According to the above similarity matrices, we define the intra-modal semantic similarity loss for both modalities to preserve intra-modal semantic consistency:  
\[\mathcal{L}^{sim}_{intra\sim\
u} =\sum_{i,j=1}^{N}\Big{\|}\mathcal{S}_{ij}-\mathcal{S}^{vv}_{ij} \Big{\|}_{F}^{2}, \tag{21}\] \[\mathcal{L}^{sim}_{intra\sim t} =\sum_{i,j=1}^{N}\Big{\|}\mathcal{S}_{ij}-\mathcal{S}^{tt}_{ij} \Big{\|}_{F}^{2}. \tag{22}\]  
**Inter-Modal Semantic Similarity Loss.** To preserve inter-modal semantic consistency, we use the inter-modal semantic similarity loss to effectively capture the heterogeneous similarities across different modalities:  
\[\mathcal{L}_{inter}^{sim}=\sum_{i,j=1}^{N}\left\|S_{ij}-S_{ij}^{et}\right\|_{F}^ {2}. \tag{23}\]  
Combining Equations (21) and (22), we obtain the intra-modal semantic similarity loss as follows:  
\[\mathcal{L}_{intra}^{sim}=\mathcal{L}_{intra-v}^{sim}+\mathcal{L}_{intra-t}^ {sim}. \tag{24}\]  
Combining Equations (23) and (24), we obtain the total semantic similarity loss as follows:  
\[\mathcal{L}_{total}^{sim}=\mathcal{L}_{inter}^{sim}+\mathcal{L}_{intra}^{sim}. \tag{25}\]  
Finally, we construct the hash representation learning objective function \(\mathcal{L}_{rep}\) that consists of the above two losses, shown as follows:  
\[\mathcal{L}_{rep}=\mathcal{L}_{con}^{weight}+\alpha\mathcal{L}_{total}^{sim}, \tag{26}\]  
where \(\alpha\in[0,1]\) is a trade-off factor.
## 3 Result and Discussion  
### NaOH Concentration Effect  
The results from Table 2 (validated with ANOVA) showed that the best ratio was 0.5 for alkali liquid to solid, and 2.5 for sodium silicate to sodium hydroxide. The concentration 10 M for NaOH agrees with [9; 11; 17; 18; 19; 20].  
According to the results, the compressive strength is maximum when NaOH concentration is at 10 M. This could be due to the higher dissolution of initial solid materials at high concentrations of NaOH, which accelerates the geopolymerization reaction, leading to the higher compressive strength of geopolymers [21]. The solubility of aluminosilicate is directly proportional to the concentration of NaOH [12; 22]. Increased additions of NaOH accelerated the chemical dissolution but suppressed the formation of ettrinige and CH in the course of binder hydration [23]. Reduction in the CH content resulted in superior strength and durability [24]. However, compressive strength is inversely proportional to the concentration of NaOH at 10-14 M. This could be due to the presence of excess hydroxide ion concentration [25] and excess Na\({}^{+}\) ions in the framework [22; 26], which precipitates aluminosilicate gel during the early stages of the reaction, thus preventing geopolymerization, culminating in lower compressive strengths. The higher the alkalinity of the hydration water, the slower the rate of the hydration [27].
## 3 Results and Discussion
### Product Efficiency under Field Conditions
In the second stage, the experiment was conducted two weeks after the application, in which additional 296 fides (total of 446) of Euclid Trucks were recorded. The results of the dust suppression efficiency (%) are presented in Table 4. The organic products (Lignin and Resin) were found to be non-efficient applications for dust suppression by wind. The efficiency of the Bitumen and the PVA reduced dramatically compared to the first stage (Table 3) with only minor dust suppression at the higher wind velocity (14.3% and 3.9%, respectively). It revealed that all these product applications were destructed by the truck wheels after 150 fides, leading to dust emission at the same rate of the control (no treatment) road section. However, the application of magnesium-chloride solution (Brine) on the road surface kept on the same high-level of efficiency as in the first stage.
## 4 Designing Sensors for Soft Robots
### Emerging Deformable Optical-Waveguide Based Sensing Technologies
Once the pressure was released, the soft finger returned to its original pose and the sensor reading returned to its base value. In comparison to an optical lightguide sensor made of two different silicone materials [42], this sensor with the integrated abraded fibre was found to be less sensitive to external pressure, making it practicable for identifying the pose when the soft structure is subjected to mechanical stimuli from the interaction with objects. These abraded optical fibre sensors have limited stretchability as they have a plastic core. Consequently, their application may be limited to soft surgical tools that exhibit bending around a single axis analogous to concentric tube robots. For multi-axis bending or for manipulators that exhibit significant stretching, stretchable optical waveguides made entirely of stretchable silicones can be used. However, they need to be carefully designed to shield them from external forces during interactions; a challenge given the strict size constraints imposed by the requirements of minimally invasive surgery applications. As the dexterity requirements in soft surgical manipulators increase, the number of optical fibre sensors that need to be integrated into the manipulator and the corresponding space requirements increase too. Reducing the number of independent fibre optic cables required for sensing would help in designing effective MIS manipulators. In a recent work, [PERSON] et al. have developed a waveguide with different chromatic patterns along its length, exhibiting wavelength specific adsorption of light, as a basis for multi-segment soft sensors that can detect mechanical stimuli at different locations [43]. This technique has excellent potential for addressing the canonical challenge of high degree-of-freedom sensing with minimal sensing structures. This would be particularly helpful for MIS applications where it is not feasible to have 9 optical fibres to measure 3 DOF deformation in 3 segments. Moreover, the demarcation of deformation modes of the soft structure to reduce the dimensionality of sensor requirements within a task, will be instrumental meeting the challenges posed by proprioceptive sensing.
## 3 Results
### The Impact of Depression on the Baseline and Postoperative CRP Levels  
Since [PERSON]'s correlation analysis revealed a significant association of infection with CRP levels measured on days 4 (r = 0.141, \(p\) = 0.043) 5 (r = 0.194, \(p\) = 0.005) and 6 (r = 0.190, \(p\) = 0.006) this variable, together with baseline CRP values, was added as an additional covariate in models examining the impact of depression scores on early and persistent postoperative CRP change. Linear regression analysis performed to examine the cross-sectional relationship of baseline BDI-II depression score with baseline CRP levels (t = 0.35, \(p\) = 0.729) and postoperative \"early\": (t = 1.0, \(p\) = 0.318) and \"persistent\" (t = 0.13, \(p\) = 0.899) CRP response resulted in no significant association. There were no significant predictors in the linear regression model of cross-sectional relationship between the BDI-II depression score and basal CRP levels. Preoperative hemoglobin levels (t = 2.85, \(p\) = 0.005) were the only significant predictors of early CRP change (Table 6), while current smoking (t = \(-\)3.09, \(p\) = 0.002) and (post)operative infection (t = 3.31, \(p\) = 0.001) were the only predictors of persistent CRP change after Bonferroni correction was applied (Table 7).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **B** & **SE** & \(\beta\) & **t** & \(p\)**-Value** \\ \hline Smoking & \(-\)20.373 & 7.932 & \(-\)0.180 & \(-\)2.568 & 0.011 \\ EuroSCORE II & \(-\)1.623 & 1.793 & \(-\)0.069 & \(-\)0.905 & 0.366 \\ Preoperative hemoglobin levels & 0.540 & 0.189 & 0.205 & 2.851 & 0.005 * \\ Extubation/ventilation time & 0.403 & 0.678 & 0.041 & 0.595 & 0.552 \\ Postoperative AF & 18.007 & 7.138 & 0.179 & 2.523 & 0.012 \\ \(\beta\)-blockers & \(-\)1.994 & 7.889 & \(-\)0.017 & \(-\)0.253 & 0.801 \\ Preoperative CRP & 1.276 & 0.591 & 0.154 & 2.160 & 0.032 \\ Infection & 7.868 & 11.347 & 0.047 & 0.693 & 0.489 \\ BDI-II score & 0.531 & 0.530 & 0.072 & 1.001 & 0.318 \\ \hline \hline \end{tabular} AF, atrial fibrillation; B, unstandardized regression coefficient; \(\beta\), standardized regression coefficient; CRP, C, reactive protein; SE, standard error. * Significant \(p\) values (cPc). Bonferroni correction for linear regression analysis; \(\text{Pc}=0.0055\) (0.05/9 – number of model comparisons).  
\end{table}
Table 6: The predictive value of the BDI-II depression score on early CRP response.
# Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
## 3 Experiment and Result
### Feasibility Experiments  
In the feasibility experiment, we use the real BeiDou satellite orbital root and terminal data as the basic experimental data. In order to show the experimental results more intuitively, we choose the following satellites in the three orbital planes of the Big Dipper constellation for the experiment, with a simulation start time of 20:00:00 on 23 May 2021 and end time of 02:00:00 on 24 May 2021. The simulation duration is 6 h, and the time interval is 10 s for each simulation (Table 1).  
Figure 6: Schematic diagram of grid calculation of satellite motion process.  
In our experiments, we use the satellite orbit data to generate satellite motion grid data based on the satellite within the test window, and use STK to simulate and verify the satellite motion grid data. We use the satellite orbit data to generate satellite motion grid data based on the satellite within the test window, and use STK to simulate and verify the satellite motion grid data based on satellite motion data.  
\begin{table}
\begin{tabular}{c c} \hline
**Satellite** & **Terminal** & **Test Time** \\ \hline BEIDOU 10 (C10) & Properties of the simulated terminal: \\ BEIDOU 17 (C31) & (Lat: 39.9989 deg \\ BEIDOU 7 (C07) & Lon:116.317 deg \\ BEIDOU 20 (C18) & Height: 0 m) \\ BEIDOU-3M9(C23) & Cone Half Angle: 45 deg \\ BEIDOU-3 M11 (C25) & \\ BEIDOU-3 M10 (C24) & \\ BEIDOU-3 M12 (C26) & \\ BEIDOU 9 (C09) & \\ BEIDOU-3 IGSO-2 (C39) & \\ BEIDOU IGSO-7 (C16) & \\ BEIDOU 5 (C06) & \\ \hline \end{tabular}
\end{table}
Table 1: Satellite data for experiments.  
Figure 7: Experimental flow chart.  
the BeiDou satellite constellation model to simulate the generation of the link between the satellite and the terminal.  
The experimental steps are as follows.  
Step1: Use the GeoSOT grid model to grid the satellite trajectory data to produce grid coordinates;  
Step2: Use the model in Section 2.2 to calculate the grid positions of satellites and terminals and rank the satellites according to the results;  
Step3: Use the STK software satellite and terminal link for simulation;  
Step4: Compare the results of grid calculation with the results of simulation.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 3 Results and Discussion
### Permeate Flux  
The driving force of permeate flux in MD is the difference between feed and distillate temperature. Ideally, feed and distillate temperature is maintained at over 50 and 20-25 \({}^{\circ}\)C, respectively to obtain a permeate flux of approximately 10 L/m\({}^{2}\) h [27; 48]. In this study, however, to avoid thermal inhibition of lacase [46], temperature of the enzymatic reactor and permeate tank was kept at 30 and 10 \({}^{\circ}\)C, respectively. A stable permeate flux of around 4 L/m\({}^{2}\) h was observed during all experiments (Supplementary Data Figure S4), suggesting that membrane fouling did not occur during the operation period. This level of flux is consistent with the feed temperature employed. Notably, the average permeate flux (Figure 6) for lacase only, lacase-HBT, lacase-VA and lacase-SA variations was 3.69 \(\pm\) 0.44 L/m\({}^{2}\) h (\(n=150\)), 3.89 \(\pm\) 0.63 L/m\({}^{2}\) h (\(n=283\)), 3.92 \(\pm\) 0.62 L/m\({}^{2}\) h (\(n=291\)) and 3.86 \(\pm\) 0.66 L/m\({}^{2}\) h (\(n=288\)) LMH, respectively, confirming negligible impact of different type of mediator addition on membrane flux. In this study, the mass transfer coefficient (K\({}_{\rm m}\)) of the DCMD, which was calculated based on the method described by [PERSON] et al. [49], ranged from 1.22 to 1.28 (\(\times 10^{-3}\)) L/m\({}^{2}\) h Pa. This value is in good agreement with that in previous studies [48; 50].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reaction Mixture** & **Toxicity of the Reactor Mixture (rTU)** & **Toxicity of the Permeate (rTU)** \\ \hline TrOCs + Lacase & \(<\)1–1.8 & \(<\)1 \\ TrOCs + Lacase + HBT (0.5 mM) & \(<\)1–1.7 & \(<\)1 \\ TrOCs + Lacase + VA (0.5 mM) & 3.3–3.9 & \(<\)1 \\ TrOCs + Lacase + SA (0.5 mM) & 109–116 & \(<\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Toxicity of the reactor mixture and permeate following treatment of TrOCs with different mediators in MD-EMBR, expressed as relative toxic unit (rTU). Mediators were added separately at a concentration of 0.5 mM. The limit of detection of the toxicity assay was 10% inhibition of luminescence (i.e., 1 rTU). Toxicity in all permeate samples was below the limit of detection (\(n=2\)).  
Thus, this study shows both stable membrane hydraulic performance and improved enzymatic degradation of TROCs following their complete retention by the MD membrane.