esa-sceva/satcom-chunk-collection huggingface dataset:
# Life Cycle Assessment for the Production Phase of Nano-Silica-Modified Asphalt Mixtures
###### Abstract  
To combat the rutting effect and other distresses in asphalt concrete pavement, certain modifiers and additives have been developed to modify the asphalt mixture to improve its performance. Although few additives exist, nanomaterials have recently attracted significant attention from the pavement industry. Several experimental studies have shown that the use of nanomaterials to modify asphalt binder results in an improved oxidative aging property, increased resistance to the rutting effect, and improves the rheological properties of the asphalt mixture. However, despite the numerous benefits of using nanomaterials in asphalt binders and materials, there are various uncertainties regarding the environmental impacts of nano-modified asphalt mixtures (NMAM). Therefore, this study assessed a Nano-Silica-Modified Asphalt Mixtures in terms of materials production emissions through the Life Cycle Assessment methodology (LCA), and the results were compared to a conventional asphalt mixture to understand the impact contribution of nano-silica in asphalt mixtures. To be able to compare the relative significance of each impact category, the normalized score for each impact category was calculated using the impact scores and the normalization factors. The results showed that NMAM had a global warming potential of 7.44563 \(\times\) 10\({}^{3}\) kg CO\({}_{2}\)-Eq per functional unit (FU) compared to 7.41900 \(\times\) 10\({}^{3}\) kg CO\({}_{2}\)-Eq per functional unit of the conventional asphalt mixture. The application of LCA to NMAM has the potential to guide decision-makers on the selection of pavement modification additives to realize the benefits of using nanomaterials in pavements while avoiding potential environmental risks.  
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## 4 Analysis of the Influence of High Peening Coverage on Almen Intensity and RCS  
### Influence of High Peening Coverage on Almen Intensity  
In this section, the influence of the high peening coverage on the Almen intensity with three different dent sizes is studied.  
The peening coverage increases from 200% to 800%. The dent size of three cases is 0.125, 0.14, and 0.16 mm. With three dent sizes, the variation of the Almen intensity with the peening coverage is shown in Figure 9, respectively. Figure 9a shown the variation of the Almen intensity at 0.125 mm of dent size. Figure 9b,c shows the dent size at 0.14 and 0.16 mm, respectively.  
Figure 9: _Cont._  
From Figure 9, firstly, with dent size of 0.125, 0.14, and 0.16 mm, the FE simulation results agree well with that of the Almen test. Secondly, with dent size of 0.125, 0.14, and 0.16 mm, the Almen intensity all increases firstly and then decreases as the peening coverage increases, there is a maximum value of Almen intensity. In other words, it's not that the higher the peening coverage, the higher the Almen intensity, which is an important reference for the shot peening process in industry. The reason for the decrease of the Almen intensity with the peening coverage increasing might be the residual stress relaxation of the Almen strip surface.
## 2 Preliminary Information  
### Problem Statement  
This article solves the space coverage problem using multiple robots in underwater environments. For complete monitoring of the given 3D workspace, it is required to build sensor networks such that no sensing hole exists in the given workspace.  
Initially, all robots are carried by surface ships. Here, each robot has both sensing and communication abilities. As a ship arrives at the sea surface of the workspace, it begins dropping robots while not moving at the site.  
The dropped robots move autonomously until the 3D workspace is fully covered by sensors of the robots. The obstacles in the workspace are unknown to each robot, and every robot moves based on local interaction with its neighboring robot. Our coverage methods result in underwater sensor networks without sensing holes, while localizing all robots. Section 3 discusses the 3D coverage methods using multiple robots.
## 5 Analysis
### Decreasing the Amount of Counterweights Used for Balancing
#### 5.2.1 Pareto Front Comparison to Eliminate One Counterweight  
Figure 8 shows the Pareto front of the different optimization results when using all possible combinations (three, two, or only one counterweight). The black stars correspond to the original Pareto front when the three counterweights are used. The blue crosses correspond to the Pareto front when Counterweight-2 has been eliminated and only Counterweights 1 and 3 are being considered. Comparing these results with those obtained when using all the counterweights, it is possible to see that the Pareto fronts are very similar.  
The green crosses correspond to the Pareto front when using only Counterweights-2 and 3. The yellow crosses correspond to the Pareto front when Counterweight-3 has been eliminated and Counterweights-1 and 2 are in use.  
Using the information provided by the Pareto fronts (Figure 8), it is possible to conclude that, if one decides to eliminate a counterweight to simplify the balancing of the mechanism, it should be Counterweight-2, since using only Counterweights-1 and 3 produces a similar result to that obtained when all three counterweights are used.  
Figure 7: Parameters used to analyze the dimension of each counterweight when using three of them.  
Appendix A shows the numerical results when using only two counterweights giving importance to the optimization of the ShF, ShM, and both. A sensitivity analysis is also presented to decide which counterweight should be used if one decides to use only one counterweight. The partial derivatives, volumes and relation area-thickness is also presented giving information about the selected optimization limits, allowing to further improve the results.  
Appendix B shows the numerical results when using only one counterweight and presents the sensitivity analysis and the partial derivatives, volumes and relation area-thickness using the methods previously proposed.  
Table 2 shows the optimization results when using three, two, and one counterweight.
# Effect of Boundary Conditions on the Mechanical Behavior of the Geogrid-Soil Interface
## 2 Materials and Methods  
### Pullout Equipment  
The large-scale pullout equipment utilized in this study was developed by Shijiazhuang Tiedao University. The pullout apparatus was mainly composed of four parts, pullout box, vertical controlling system, horizontal controlling system, data acquisition and logger system. The pullout box was built of steel and was 600 mm in length, 400 mm in width, and 500 mm in height on the inside dimensions. The vast dimensions of the pullout box can reduce the effect of the dimension of specimen and boundary effect for the duration of the test. In addition, to enhance the rigidity of the test box, the longitudinal members made of steel were added uniformly on the outside of the pullout box with the thickness of 10 mm to ensure the plane strain condition in the process of test. The vertical loading  
Figure 1: (a) Reinforced structures with top rigid and positive rigid; (b) reinforced structures with top flexible and positive flexible.  
was applied by an inverted hydraulic jack and transferred to geosynthetic composite soil by loading plate. The vertical stress applied by the apparatus can reach 800 kPa and be monitored by a pressure sensor to keep it constant during the loading process, and the precision is 0.01% F.S. The pullout force in the horizontal direction was applied by the horizontal controlling system. The horizontal controlling system of pullout apparatus adopted the strain control type. The pullout rate varied from 0 to 30.0 mm/min, and the maximum pullout displacement can reach 150 mm, and the precision is \(\pm\)0.02 mm. The maximum horizontal pullout force can reach 100 kN, and the precision is 0.01% F.S. At last, the test data can be displayed on the data acquisition and logger system in real-time, to enhance the control of the test progress. The schematic and view of the laboratory pullout apparatus and the main technical parameters corresponding to the components were shown in Figure 2.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 4 Experiment Results
### Experimental Results on Resnet50 with CIFAR-100
#### 4.3.2 Experimental Results on Case2  
We utilized the 'Case 2' network which was trained with 60 classes in CIFAR-100 dataset and updated the branch new network with the remaining 40 classes. As shown in Figure 7, the optimal sharing number of layers was 44 in six iterations, when target  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Classes**} & \multirow{2}{*}{**Network**} & \multicolumn{2}{c}{**With Sharing Layers**} & \multicolumn{1}{c}{
\begin{tabular}{c} **w/o** \\ **Sharing Layers** \\ \end{tabular} } \\ \cline{3-4}  & & & **Accuracy** & **Accuracy** & **-** \\  & & & **Degradation 2\%** & **Degradation 3\%** & **-** \\ \hline T0 & 70 (Base) & & - & - & 81.73\% \\ \hline T1 & 30 & & 83.33\% & 81.17\% & 84.40\% \\ \hline T0–T1 & 100 & ResNet 50: 53 convolution, 53 BN, 40 ReLU 11 Averpool, 1 FC layer & 67.84\% (the optimal configuration: 39) & 67.03\% (the optimal configuration: 47) & 69.74\% (Baseline) \\ \hline T0\({}^{\prime}\) & 60 (Base) & 49 ReLU, 1 Averpool, 1 FC layer & - & - & 82.27\% \\ \hline T1\({}^{\prime}\) & 40 & 1 Averpool, 1 FC layer & 82.10\% & 78.38\% & 83.93\% \\ \hline T0\({}^{\prime}\) –T1\({}^{\prime}\) & 100 & & 67.78\% (the optimal configuration: 44) & 66.82\% (the optimal configuration: 49) & 69.81\% (Baseline) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results on the proposed algorithm in CIFAR-100 (Case 1,2).  
Figure 5: Visualization results of the optimal sharing layer configuration through BayesOpt as threshold accuracy degradation is 2% less than the baseline accuracy. (70 classes/30 classes) (**a**) the result of the selected optimal number of sharing layers. (**b**) The L2 distance of the consecutive observed points. (**c**) The value of the calculated best-selected sample for every iteration.  
Figure 6: Visualization results of the optimal sharing layer configuration through BayesOpt as threshold accuracy degradation is 3% less than the baseline accuracy. (70 classes/30 classes) (**a**) The result of the selected optimal number of sharing layers. (**b**) The L2 distance of the consecutive observed points. (**c**) The value of the calculated best-selected sample for every iteration.  
accuracy degradation was 2% which was lower than the baseline accuracy. As listed in Table 2, The combined classification accuracy of 44 layers sharing in the base network was 67.78% as the baseline accuracy value was 69.81%. Besides, we changed the threshold accuracy value to 3% less than the baseline accuracy, and we could get the result in Figure 8. As listed in Table 1, the optimal sharing layer configuration was 49 in eight iterations, and the combined classification accuracy was 66.82% when the baseline accuracy value was 69.81%.
## 4 Experimental Verification
### Measurement of Coaxial Deviation between Two Centers  
As shown in Figure 5, an electronic probe extended from the magnetic stand was adopted for use. This probe is the type of linearly varied differential transformer (LVDT, Chungyuan Instrument Company, Sanmenxia, China, model DGB-5B, with resolution 0.1 \(\upmu\)m, maximum indication error 0.5 \(\upmu\)m for the selected range of \(\pm 30\)\(\upmu\)m). The magnetic stand was firmly fixed to the base of lower center, i.e., the top of rotational table, and the probe head is pointed to the mandrel at the position close to the tip of the upper center. The extension arm was sturdy enough to hold the probe when the magnetic switch was on. When the bottom stage rotated, the probe set simultaneously rotated around the mandrel. Therefore, the reading of the probe represented the relative run-out motion of the tip of the upper center to the base of the lower center, or vice versa. This is also the value of the radial coaxiality error of the mandrel to the spindle average line (denoted by \"\(e\)\" in Figure 4). Measured results are shown in Figure 6. The relative radial motion of the upper center with respect to the base of the lower center was about 10 \(\upmu\)m.  
Figure 5: Measurement of coaxial error of the mandrel to the spindle.  
As pointed out in Figure 1 that the spindle itself also has tilt and radial errors that would result in the radial motion of the tip of lower center, denoted by \(\delta\) in Figure 4, during the spindle rotation. Such a radial motion has to be measured in order to verify Equation (16). Figure 7 shows the setup for measuring the radial error motion of the lower center. Two LVDTs were employed to contact two positions (A and B) of the lower center with a separation of 50 mm (\(h_{AB}\)). Measured results are shown in Figure 8. There is nearly no difference between the two readings, which reflects the very small tilted error of the spindle. Therefore, the radial error motion (\(\delta\)) of the tip of the lower center was estimated to be about 1.6 \(\upmu\)m. In addition, the actual tilted direction on the cross-sectional plane of the spindle, called the principal axis of tilt, was not known in practice. However, it has been defined in Figures 3 and 4 that the run-out value is minimum when \(\theta=0^{\circ}\) and maximum when \(\theta=180^{\circ}\). Figure 8 shows that the run-out value (\(r_{\theta}\)) increased to about \(90^{\circ}\) and then decreased. The actual position of \(\theta=0^{\circ}\), as indicated in Figure 4a, was at \(\theta=90^{\circ}\) in this experiment. This phase shift correction will be considered in a later experiment.  
Figure 8: Measured run-out errors of the lower center.  
Figure 6: Measured coaxial error of the mandrel to the spindle.  
Figure 7: Measurement of run-out of a lower center.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Cohesive Subgraph Search
#### 4.3.3 Core Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Problem** & **Algorithm** & **Graph Type** & **Update Mode** & **Fully Dynamic** & **Incremental Dynamic** & **Decmental Dynamic** & **Streaming Dynamic** \\ \hline Core maintenance & Aksu [68] & General & Batch (distributed) & ✓ & ✓ & ✓ & � \\ \hline Core maintenance & Hua [69] & General & Batch (parallel) & ✓ & \(O(\Delta_{i}\cdot\max_{\varsigma,E_{i}}(m_{\varsigma,E_{i}}+L_{\varsigma, \varsigma,\varsigma})\cdot\) & \(O(\Delta_{i}\cdot\max_{\varsigma,E_{i}}(m_{\varsigma,\varsigma}+L_{\varsigma, \varsigma,\varsigma})\cdot\) & � \\  & & & \(n_{\varsigma,\varsigma,\varsigma,\varsigma})\) & \(n_{\varsigma,\varsigma,\varsigma,\varsigma})\) & \(n_{\varsigma,\varsigma,\varsigma,\varsigma})\) & � \\ \hline Core maintenance & [PERSON] [73] & General & Batch (distributed) & ✓ & ✓ & ✓ & � \\ \hline Core maintenance & [PERSON] [71] & General & Batch (parallel) & ✓ & \(O(\Delta I\cdot\max_{\varsigma,\varsigma,\varsigma}(m_{\varsigma}+L_{ \varsigma,\varsigma}))\) & \(O(\Delta D\cdot\max_{\varsigma,\varsigma,\varsigma}(m_{\varsigma}+F_{ \varsigma,\varsigma})\cdot n_{\varsigma,\varsigma})\) & � \\ \hline Core maintenance & Jin [72] & General & Batch (parallel) & ✓ & \(O(|E_{i}|\cdot|V|+\Delta_{i}\cdot\) & \(O(|E_{i}|\cdot|V_{0}|+\Delta_{0}\cdot\) & � \\  & & & \(\max_{\varsigma,E_{i}}(m_{\varsigma}+L_{\varsigma,\varsigma})\cdot n_{\varsigma })\) & \(\max_{\varsigma,E_{i}}(m_{\varsigma}+F_{\varsigma}\cdot n_{\varsigma})\) & � \\ \hline Core maintenance & Li [74] & General & Single & \(O(|V|\sum_{\varsigma\in V_{i}}D_{\varsigma})\) & ✓ & ✓ & � \\ \hline Core maintenance & TRAVESAL [70] & General & Single & \(O(|E|)\) & ✓ & ✓ & � \\ \hline Core maintenance & Lin [19] & General & Batch & ✓ & \(O(\sum_{\varsigma\in V_{i}}|N(V,G\star)|+|T^{\prime}(\sigma^{\prime})|)\) & \(\mathcal{N}\) \\ \hline Core maintenance & Yu [20] & General & Batch & ✓ & \(O(\Delta_{i}\cdot|E|)\) & \(O(\Delta_{0}\cdot|E|)\) & � \\ \hline Core maintenance & [PERSON] [20] & General & Batch (parallel) & ✓ & \(O(\Delta_{i}\cdot\max_{\varsigma,\varsigma,\varsigma}(m_{\varsigma}+L_{ \varsigma,\varsigma})\cdot n_{\varsigma})\) & \(O(\Delta_{0}\cdot\max_{\varsigma,\varsigma,\varsigma}(m_{\varsigma}+F_{ \varsigma}\cdot n_{\varsigma}))\) & � \\ \hline Core maintenance & [PERSON] [75] & General & Batch (parallel) & ✓ & \(O(\phi_{\zeta}\cdot|G^{\prime}|)\) & \(O(\phi_{\zeta}\cdot|G_{\varsigma}|)\) & � \\ \hline Core maintenance & [PERSON] [76] & General & Batch (parallel) & ✓ & \(O(E_{i}|E^{\ast}|\log|E^{\ast}|)\) & \(O(E_{0}|E^{\ast}|)\) & � \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of time complexity for dynamic core algorithms.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 5 Improving a Bin-Picking Workcell through Human-Robot Collaboration
### Object and Human Detection  
#### 5.2.1 Object Detection  
The objects picked from the bin during the experimental tests are M30 hexagon nuts. Hexagon nuts were chosen as objects to pick because they can be difficult objects to detect because of their reflectance properties.  
Object recognition is performed by exploiting _3D One Sight Model Locator Tool_, an iRVision tool that detects a pre-trained 3D model in the acquired 3D data and calculates the 3D position and posture. The 3D model is created from 3D data obtained by measuring a workpiece from a certain point of view or can be a 3D CAD model. In this case, a 3D CAD model of the hexagonal nut was used to teach the tool which shape to find in the acquired 3D map. Figure 3 represents a successful object recognition of hexagon nuts exploiting the _3D One Sight Model Locator Tool_.  
Once recognized, the detected objects are also numbered in the order in which they will be picked up from the bin. The order depends on the criteria chosen when setting up the vision process: in general, the first objects to be picked are the ones with the highest \(z\) quote. For example, the red nut in Figure 3 is labeled with number 1 and it will be picked before the green one underneath, labeled with number 6. Moreover, the nut under the orange and yellow one (labeled with numbers 2 and 3 respectively) is not recognized: this is not a problem because after each pick, the 3D map is acquired again, and therefore that nut will be recognized in the following scans, when the overlapping parts will be removed.  
Due to the reflectance properties of the nuts and sensor technology, object recognition failures may occur. In particular, they may occur with the change in lightening conditions.  
Figure 2: Collaborative bin-picking robotic workcell used during the tests.
## 4 Results
### Correlation Between GDP and Telecommunications Penetration  
Telecommunications services, especially fixed services like telephone, internet, and television, are essential for economic development. The degree to which these services are accessible to households often correlates with the economic output of a region. High penetration rates generally indicate better infrastructure, more developed economies, and better connectivity, all of which are catalysts for higher GDP growth. Thus, we would expect to see a positive correlation between higher GDP states and greater penetration rates for these services.  
Fixed Telephony: Telephone services were historically a crucial aspect of telecommunications infrastructure, but over time, their relative importance has been diminished by mobile networks. However, a high penetration rate of fixed telephony may still suggest well-established infrastructure, which often correlates with a higher GDP. States with higher  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Estimate** & **SE** & **tStat** & \(p\)**Value** \\ \hline (Intercept) & \(1\) & \(-0.3077\) & \(-0.5052\) & \(-0.7024\) \\ \(x_{1}\) & \(-0.3077\) & \(1\) & \(0.3849\) & \(0.455\) \\ \(x_{2}\) & \(-0.5052\) & \(0.3849\) & \(1\) & \(0.7778\) \\ \(x_{3}\) & \(-0.7024\) & \(0.455\) & \(0.7778\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Model coefficients.  
Figure 7: Predicted reduction of the digital divide by Mexican states.  
GDPs, such as Mexico City, Nuevo Leon, or Jalisco, should demonstrate higher or at least stable fixed telephony penetration rates over the years, while states with lower GDPs might show declining trends or consistently low penetration rates.  
Fixed Internet Access. The penetration of fixed internet access is perhaps the most critical modern telecommunications service for supporting economic activity. High-speed internet access enables businesses to operate more efficiently, supports e-commerce, and promotes educational advancements. Therefore, we expect to see a strong correlation between states with higher GDPs and higher internet penetration rates. Wealthier states with advanced urban areas and business centers, such as Mexico City, Monterrey, and Guadalajara, are likely to lead in internet penetration, while states with lower GDPs, particularly in rural areas, may lag behind.  
Restricted Television (Cable/Satellite). Restricted television, including services like cable and satellite, often reflects discretionary spending and entertainment consumption patterns. While not as critical to economic productivity as internet services, high penetration rates in wealthier states might indicate greater disposable income and demand for premium services. States with high GDPs may see greater penetration of restricted TV, while lower-GDP states may prioritize basic services like internet over entertainment subscriptions.
## 2 Methodology
### LLM Evaluation Design
For instance, terms like \"male\" and \"M\" were mapped consistently, and numerical data such as age ranges were validated against explicit and implied descriptions in the input text. This level of granularity provided a robust mechanism to ensure that the outputs generated by the LLMs were factually aligned with the source material. In addition to factual consistency, qualitative metrics such as coherence, consistency, fluency, and relevance were evaluated. These metrics, inspired by the Unified Multi-dimensional Evaluator framework [26; 31; 32], provided a nuanced understanding of the LLMs' language generation capabilities. Coherence assessed the logical flow and structure of the outputs, ensuring that the extracted information formed a comprehensible narrative. Consistency evaluated whether the outputs remained uniform across different sections of the generated text, avoiding contradictions or deviations. Fluency measured the linguistic quality of the text, focusing on grammar, syntax, and readability, while relevance ensured that the generated outputs were directly aligned with the input prompts and data. These qualitative metrics were applied systematically to compare the performance of multiple LLMs. The evaluation revealed key insights into the models' strengths and limitations, such as their ability to handle complex eligibility criteria with overlapping or ambiguous terms. By combining the quantitative assessment of factual consistency with these qualitative measures, our study offered a comprehensive framework for evaluating the performance of LLMs in extracting reliable and contextually appropriate outputs from free-text clinical data. To integrate these multiple metrics and identify the top-ranking LLMs, we employ Multi-Criteria Decision Analysis (MCDA) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [33]. TOPSIS allows us to consider the relative importance of each metric and rank the LLMs based on their proximity to the ideal solution [34]. By applying MCDA, we can make informed decisions about the most suitable LLMs for extracting patient characteristics from free-text eligibility criteria descriptions. Figure 3 presents a multi-dimensional evaluation framework for large language models, integrating automated metrics (inspired by BertScore, DeepEval, and UNDE) with human judgment. These measures encompass precision-based metrics (F1, precision, recall), factual consistency, semantic similarity, and coherence to ensure a comprehensive assessment of model outputs.