esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Density Geopolymer Composites for the Construction Industry
###### Abstract  
The article presents preliminary results in studying reinforced and light-weight geopolymers, which can be employed in buildings, especially for walling. Such materials are very promising for the construction industry having great potential due to their favorable properties such as high mechanical strengths, low thermal conductivity, and low density. Moreover, they also exhibit several advantages from an economic and ecological point of view. The present study examinated the use of specific fillers for the metakaolin-based light-weight geopolymers, emphasizing the above-mentioned physical properties. This research also investigated the electromagnetic shielding ability of the carbon grid built into the light-weight geopolymer structure. According to the study, the most suitable materials to be used as fillers are polystyrenes, along with hollow ceramic microsphere and Liapor. The polystyrene geopolymer (GPP) achieves five times lower thermal conductivity compared to cement concretes, which means five times lower heat loss by conduction. Furthermore, GPP is 28% lighter than the standard geopolymer composite. Although the achieved flexural strength of GPP is high enough, the compressive strength of GPP is only 12 MPa. This can be seen as a compromise of using polystyrene as a filler. At the same time, the results indicate that Liapor and hollow ceramic microsphere are also suitable fillers. They led to better mechanical strengths of geopolymer composites but also heavier and higher thermal conductivity compared to GPP. The results further show that the carbon grid not only enhances the mechanical performances of the geopolymer composites but also reduces the electromagnetic field. Carbon grids with grid sizes of 10 mm \(\times\) 15 mm and 21 mm \(\times\) 21 mm can reduce around 60% of the Wi-Fi emissions when 2 m away from the signal transmitter. Moreover, the Wi-Fi emission was blocked when the signal transmitter was at a distance of 6 m.  
light-weight geopolymers; polystyrene; ceramic microsphere; Liapor; carbon fiber grid; walling materials +
Footnote †: journal: _Chemistry of Technol._
# A Novel Visual SLAM Based on Multiple Deep Neural Networks
## 4 Experiments
### Evaluation Experiment  
To evaluate the accuracy of the proposed system, the results are compared with the data on ORB-SLAM2 [10], LSD-SLAM [11] and DSV-SLAM [12]. The main experimental procedure is to connect each image sequence to our system and other comparison systems, respectively. Each system was run independently five times, and then the results of each system were recorded. Table 1 shows the results on the 8 sequences. Figure 5 shows some examples of the estimated trajectories.  
Our system outperforms ORB-SLAM2, LSD-SLAM and DSV-SLAM in most sequences. In addition to Sequence 04, the estimation accuracy of our system is improved by at least 0.1 m (Sequences 03, 06, 07, 09) and at most 0.3 m (Sequence 00) compared to ORB-SLAM2.  
Compared to LSD-SLAM, the estimation accuracy of our system is improved by more than 0.1 m in most cases and even more than 1.0 m in some cases, except for Sequences 00 and 04, which are equal. This is mainly due to the presence of many scenes in those sequences (Sequences 03, 05, 06, 07, 09, 10) that cause dramatic changes in light intensity. For example, weaving through a forest, entering a residential area from an open road, etc.  
Compared to DSV-SLAM, the estimation accuracy of our system is only more than 0.1 m for Sequence 06, and at least 1.0 m for other sequences. This is because our system, using SuperPoint, extracts features that are less susceptible to changes in light intensity and are robust. The features are then matched continuously and accurately using SuperGlue. Moreover, the use of Seq-CALC, a deep loop closure detector that uses only the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & **Our** & **ORB-SLAM2** & **LSD-SLAM** & **DSV-SLAM** \\ \hline
00 & **1.0** & 1.3 & **1.0** & 2.6 \\
03 & **0.5** & 0.6 & 1.2 & 2.5 \\
04 & 0.2 & 0.2 & 0.2 & 1.1 \\
05 & **0.6** & 0.8 & 1.5 & 3.8 \\
06 & **0.7** & 0.8 & 1.3 & 0.8 \\
07 & **0.4** & 0.5 & 0.5 & 4.4 \\
09 & **3.1** & 3.2 & 5.6 & 5.3 \\
10 & **0.8** & 1.0 & 1.5 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of accuracy in the KITTI odometry dataset using RMSE (m).  
original image, avoids the propagation of feature extraction errors in the loop closure detection process.  
The accuracy of our system, ORB-SLAM2 and LSD-SLAM, is the same on Sequence 04 because Sequence 04 is a very simple sequence with a slow short-range linear motion on a flat road, as shown in Figure 6.  
Figure 5: Estimated trajectory (blue) and ground-truth (red) in 00, 03, 05, 07, 09 and 10 KITTI odometry datasets.
## 5 Conclusions  
The proposed framework significantly enhances dexterous robotic grasping by integrating advanced vision and deep learning techniques. The results indicate a substantial improvement over the previous LSTM-based system, which achieved only 60% efficiency across five object classes. In this study, with nine object classes [Apple, Ball, Banana, Bottle,Box, Lemon, Orange, Strawberry, Tomato), the LSTM and GP models achieved an efficiency of 91.3%, while the Transformer model outperformed with an efficiency of 95.6%.  
The findings highlight the potential of combining probabilistic modelling, self-attention mechanisms, and multi-modal sensing to improve grasp adaptability and precision. By addressing current limitations, future advancements will contribute to enhancing robotic dexterity in unstructured environments, facilitating more reliable automation in complex grasping tasks. The proposed framework has practical applications in industrial assembly, logistics, and agricultural harvesting. By improving grasp stability and adaptability, this approach reduces reliance on pre-programmed grasp strategies, increasing operational efficiency and flexibility. In agriculture, it can be applied to delicate produce handling, minimizing damage and optimizing sorting efficiency.  
Conceptualization, methodology, software, writing and editing, S.K.S.; writing--review, supervision N.W., M.P., C.Y., H.W. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Author [PERSON] was employed by the company Antobot Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 4 Proposed Methodology
### The GNN-DDQN Agent
The Q-network is trained using gradient descent on a loss function \(L(\theta)\), which measures the discrepancy between the predicted Q-values and the target Q-values. For the sampled transitions \(b_{j}\), the loss function is defined as follows:  
\[L(\theta)=\sqrt{\mathbb{E}\left[\sum_{b_{j}}((\mathcal{Y}_{b_{j}}-Q(\mathcal{ S}_{b_{j}},\mathcal{A}_{b_{j}}|\theta))^{2}\cdot mask)\right]} \tag{9}\]  
where \(\mathcal{Y}_{b_{j}}\) is defined as follows:  
\[\mathcal{Y}_{b_{j}}= \begin{cases}\mathcal{R}_{b_{j}},&\text{if episode terminates at }b_{j+1}\\ \mathcal{R}_{b_{j}}+\frac{1}{2}\gamma\sum_{r\in\hat{Q}(\mathbf{S}_{b_{j+1}} \text{arg}\max,\mathcal{A}^{\prime}_{b_{j+1}}\cdot,\mathcal{A}^{\prime}_{b_{j+ 1}}\mid=z-Q(\mathbf{S}_{b_{j+1}}\mathcal{A}^{\prime}_{b_{j+1}}\mid\theta))}^{ \intercal},&\text{otherwise}\end{cases}  \tag{10}\]  
and \(mask\) is defined as follows:  
\[mask= \begin{cases}1,&\text{if }n_{k}\text{ is a router with the caching capability}\\ 0,&\text{otherwise}\end{cases}  \tag{11}\]  
where \(\mathcal{Y}_{b_{j}}\) represents the ground truth Q-values. If the episode terminates at transition \(b_{j+1}\), \(\mathcal{Y}_{b_{j}}\) is equal to \(\mathcal{R}_{b_{j}}\). Otherwise, it is computed as the sum of \(\mathcal{R}_{b_{j}}\) and the discounted expected reward of the next state. To estimate the expected future reward, the Q-network selects the top \(z\) greedy actions based on state \(S_{b_{j+1}}\), and the corresponding Q-values are computed using the target Q-network \(\hat{Q}\). The discount factor \(\gamma\) determines the importance of long-term rewards and is typically between 0 and 1. To ensure that each action taken at the next time step contributes equally, the sum of the expected long-term rewards is divided by \(z\). To focus the loss contribution on routers with caching capabilities, a mask is applied in Equation (9). Nodes without caching capabilities are assigned a mask value of 0, while routers with caching capabilities have a mask value of 1. To maintain the training stability, the parameters of the Q-network \(Q\) are periodically copied to the target Q-network \(\hat{Q}\) every \(K\) steps. This helps to reduce the potential for the overestimation of the Q-values during training. The key training parameters for the GNN-DDQN model are summarized in Table 3.
# A Hybrid Tolerance Design Method for the Active Phased-Array Antenna
## 2 Surrogate Model of the Electrical Performance and Hybrid Geometric Errors
### Second-Order Taylor Expansion  
The gain of the antenna cannot be used to optimize the tolerance directly, because the realistic gain of the antenna cannot be modeled explicitly in a statistical way, and details can be found in Appendix C. The power pattern was used to optimize the tolerances in this paper instead, as shown in Figure 5. The correlation function between the antenna power pattern and the position errors of the elements has been established based on the first-order Taylor expansion in our previous work [16].  
\[P(\theta,\varphi)\approx P_{o}+\sum_{i=1}^{m}\sum_{j=1}^{n}\left\{\frac{ \partial P}{\partial x_{ij}}\Delta x_{ij}+\frac{\partial P}{\partial y_{ij}} \Delta y_{ij}+\frac{\partial P}{\partial z_{ij}}\Delta z_{ij}\right\} \tag{7}\]  
where \(P(\theta,\varphi)\) represents the realistic power pattern of the antenna, and \(P_{o}\) represents the ideal power pattern. For the convenience of description, \(P(\theta,\varphi)\) is abbreviated as \(P\) in the following.  
The far field patterns of all the horn antennas are assumed to be the same, and the far-field pattern of the horn is assumed as ideal. Based on the above assumption, the far-field patterns of the horns are just coefficients used to multiply the array factor, the pattern of the array antenna is affected only by the array factor. The main lobe becomes sharp if considering the far-field pattern of the horn. In order to show the changes of the array factor more clearly, the far-field pattern of the horn is not considered.  
Figure 5: The power pattern in the E plane (\(\varphi=0\)) of the \(8\times 8\) subarray.  
The maximum power density in the main lobe reflects the detection capability of the antenna and was used to optimize the hybrid tolerances. The side lobes in the power pattern are not important in our analysis.  
To improve the approximation precision of the performance, the second-order Taylor expansion was derived, as shown in Equation (8). Details can be found in Appendix A.  
\[P(\theta,\varphi)\approx P_{o}+\sum_{i=1}^{m}\sum_{j=1}^{n}\left\{\frac{ \partial P}{\partial z_{ij}}\Delta z_{ij}\right\}+\frac{1}{2}\sum_{i=1}^{m} \sum_{j=1}^{n}\left\{\frac{\partial^{2}P}{\partial z_{ij}^{2}}\Delta z_{ij}^{2 }\right\}=P_{o}+\Delta P \tag{8}\]  
For the convenience of expression, Equation (8) was rewritten as follow.  
\[\Delta P\approx\Delta P_{1}+\Delta P_{2},\quad\Delta P_{1}=\sum_{i=1}^{m}\sum_ {j=1}^{n}\left\{\frac{\partial P}{\partial z_{ij}}\Delta z_{ij}\right\},\quad \Delta P_{2}=\frac{1}{2}\sum_{i=1}^{m}\sum_{j=1}^{n}\left\{\frac{\partial^{2} P}{\partial z_{ij}^{2}}\Delta z_{ij}^{2}\right\} \tag{9}\]  
To illustrate the effectiveness, the second-order Taylor expansion was applied to the \(8\times 8\) subarray with random geometric errors, as shown in Figure 6. In the main lobe, the second-order Taylor expansion is much more precise than the first-order Taylor expansion, and in the side lobes, the second-order Taylor expansion is almost same with the first-order Taylor expansion.
## 5 Key Enablers
### Virtualization  
Virtualization is a technology that delivers network resources, services, and functions in an abstract layer, decoupling from the actual hardware. Applying virtualization techniques, edge cloud architecture transforms from a dedicated physical component to a combined physical and software-based solution. Virtualization allows creation, management, optimization, and sharing of resources (compute, storage, and network) among different applications. Virtual compute function (VCF), virtual network function (VNF), and virtual storage function (VSF) are realized as software instances on an abstract layer by leveraging commodity servers and storage. This way, VCF, VNF, and VSF could be implemented in a software environment running on one or more physical devices located in data centers, distributed network nodes, and at end user premises. This decoupling allows VCF, VNF, and VSF to relocate and instantiate at different network locations without the need to purchase and install new hardware.  
Leveraging virtualization, network slicing provides multiple logical networks to operate on a shared distributed network infrastructure with the intention of providing different services with heterogenous QoS. Network slicing can be implemented using VMs, containers, and NFV instances at edge DCs. SDN controllers can be hosted at the edge data center, which can also be managed by an SDN controller. Moreover, using a network slicing approach, virtualized entities in an edge data center can be orchestrated with those in peer edge or cloud data centers for load balancing, failure recovery, and multi-site collaboration [26].  
In edge networks, clients in general request a set of network functions and resources. To accommodate these requests, software-based network functions are introduced, called service functions (SFs). When these SFs are interconnected in a specific order, they are called a service function chain (SFC). The service providers deploy a SFC across a shared physical network. Therefore, a SFC could be a flexible and agile approach for adding and deleting demanded functions to significantly improve end-to-end service performance and reduce the expense of edge cloud networks [33; 34].  
To fully realize the potential of automated networking paradigms, there are several challenges that have been raised in many research communities and industry experts which need to be addressed. Some of these challenges are autonomous self-deploying of VNFs, self-termination of VNF, self-healing, placement of VNFs, and intelligent management and orchestration (MANO) [35; 36].
# Performance Evaluation of Laetitic Subgrade Soil Treated with Lime and Coir Fibre-Activated Carbon
## 3 Test Results and Discussion
### Surface Area Analysis  
The BET surface area technique was used to assess the changes in surface area and micropores of untreated soil, 12% lime, and 3% ACF-treated soil. The impact of lime and AC on the surface area, pore volume, and pore size are shown in Figure 13. The BET values of the unmodified soil and 3% ACF increased from 25.57 m\({}^{2}\)/g to 55.57 m\({}^{2}\)/g, while pore size and pore volume decreased from \(3.05\times 10^{-7}\)dm and \(0.389\times 10^{-3}\frac{\text{dm}^{3}}{\text{g}}\) to \(7.42\times 10^{-8}\)dm and \(0.330\times 10^{-3}\frac{\text{dm}^{3}}{\text{g}}\), respectively. Although the BET value for 12% lime increased to 28.07 m\({}^{2}\)/g, it is insignificant compared to 3% ACF. The pore size and volume increased considerably in comparison with 3% ACF. The BET results confirmed that 3% ACF is more effective than 12% lime in terms of surface area and BET value. To better understand the effect of AC in decreasing pore volume and pore size, the BET result of 3%L-treated soil was added. Therefore, activated carbon improves the lateritic soil structure into a completely interlocking system with fewer tiny pores and prepares the situation for more reactions by raising the surface area [73].  
Figure 12: FESEM images of (**a**) lateritic soil, (**b**) AC specimen, and (**c**) lime specimen.
## 4 Discussion
### Error Results for RSA-SRSS (Error 2) and RSA-CQC (Error 3)  
The following remarks apply to RSA-SRSS and RSA-CQC and are based on numerical data in Tables 2 and 3 (plots are provided as Supplementary Materials, Figures S1-S9):* The general trends are very similar between both combination rules and THMA, except for a larger dispersion in most of the variables, and substantial differences in \(a\).
* Considering the average across stories, the median \(x^{50}\) is close to 0 and the dispersion (measured by \(x^{10}\) and \(x^{90}\)) is moderate, except for \(\
abla\), \(a\), and \(\
abla_{\rm S}\). The over-conservative error (max) is considerably smaller for all variables (except \(u\) and \(v\)) when the RSA-CQC method (error 3) is used.
* Considering the first story error, the median \(x^{50}\) is negative with both methods and for all variables, indicating that both RSA-SRSS and RSA-CQC tend to under-predict results at this story. However, RSA-SRSS (error 2) renders slightly more un-conservative results, therefore more extreme min values. However, larger over-conservative results for \(x^{90}\) and max are obtained with RSA-CQC (error 3).
* Considering the roof error, both methods tend to conservative (>0) median \(x^{50}\) results. More extreme un-conservative results min and \(x^{10}\) are obtained with RSA-CQC (error 3), but more extreme conservative results \(x^{90}\) and max are obtained with RSA-SRSS (error 2).
* The influence of inherent damping, type of system (shear-type or frame-type) and ground type follows the same trends as in THMA.
* As a conclusion, RSA-SRSS and RSA-CQC methods are expected to render reasonable average results across stories but generally produce un-conservative results for first story and over-conservative results for the roof. Dispersion (measured as x\({}^{90}\)-x\({}^{10}\)) is larger for RSA-SRSS. Overall, both methods are very similar for \(u\) and \(v\). RSA-CQC offers better results for all other variables.
# The Study of Tooth Erosion Tested by the Color Digital Holography (CDH) Detection System
## 4 Discussion
### The Effect of Temperature on the Tooth Erosion  
The effect of temperature on the enamel's erosion is shown in Figure 7. The six curves almost have no change with the increase in load, and the results of the Shapiro-Wilk (SW) normal distribution test are as follows [43]: \(p\) = 0.96199 for Group A, \(p\) = 0.37216 for Group B, \(p\) = 0.68516 for Group C, \(p\) = 0.055081 for Group D, \(p\) = 0.055081 for Group E, and \(p\) = 0.43614 for Group F [44]. These results indicate they meet the normal distribution at the 0.05 level, and that the fluctuation of the six curves is very small with the increase in the load. There is a small difference among Groups A, B, and C, indicating the temperature has a minimal impact on the non-acid-etched enamels. Furthermore, there is a non-negligible difference among Groups D, E, and F, indicating the temperature has a greater impact on the etching of enamels by acid.  
Figure 7b shows that the average values of [PERSON]'s modulus were 99.8, 100.7, 100.1, 86.3, 83.0, and 78.5 GPa for Groups A-F. Compared with the non-acid-etched enamels, the temperature has a greater influence on the etching of enamels by acid.  
The variance homogeneity of [PERSON]'s modulus for the six groups was tested. The value \(p\) = 0.919 \(>\) 0.05 of Group A-C shows that the variances are equal and significant, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**(I) Group**} & \multicolumn{1}{c}{**(J) Group**} & \multicolumn{1}{c}{**Mean Difference (I-J)**} & \multicolumn{1}{c}{**Std. Error**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{3-6} \cline{6-6} \multicolumn{1}{c}{} & & & & **Lower Bound** & **Upper Bound** \\ \hline Group B & Group E & 17.33326 * & 2.10945 & 0.000 & 12.1030 & 22.5635 \\ Group G & 75.33265 * & 2.10945 & 0.000 & 70.1024 & 80.5629 \\ Group E & Group B & \(-\)17.33326 * & 2.10945 &  
the value \(p=0.718>0.05\) of Group D-F shows that the variances are equal and significant. The ANOVA multiple comparisons test was also used to analysis the difference among the six Groups [44], as shown in Table 3.  
The value of Sig. more than 0.05 indicates that the temperature has no significant impact on non-acid-etched enamel, whereas the value of Sig. less than 0.05 indicates that the temperature has no significant impact on etched enamel. According to the experimental results of different temperatures and the ANOVA results in Table 3, it can be concluded that the acid-etching temperature has an effect on the acid etching of tooth enamel, and that this effect is significant when the temperature changes by more than 20 \({}^{\circ}\)C. The relevant literature indicates that, as the temperature rises, the irregular motion of the molecules accelerates, and acid-etching reactions occur faster [41; 45]. This is consistent with our results.
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 2 Related Work  
### Image Dehazing Techniques  
Image dehazing has been a significant research area due to its importance in improving the performance of computer vision systems in adverse weather conditions [1; 2]. Early methods for haze removal were based on physical exposure adjustments, combining multiple images taken with different exposure times to reduce haze effects [7]. While these approaches were somewhat effective for static scenes, they faced significant challenges in dynamic scenes and real-time processing scenarios due to the need for specialized equipment and complex post-processing steps [8; 9]. Image processing techniques such as histogram equalization and unsharp masking were also explored to enhance image contrast but often struggled with non-uniform haze and intricate depth information in complex scenes [12; 13].  
A significant advancement was made with the introduction of the Dark Channel Prior (DCP) method by [PERSON] et al. [15]. DCP leverages the observation that in most haze-free outdoor images, at least one color channel has very low intensity in some local regions. By utilizing this prior to estimate haze transmission, DCP effectively removes haze from single images. Despite its success, DCP is computationally intensive and may produce artifacts in regions with bright skies or white objects [7; 16].  
To overcome the limitations of prior-based methods, researchers have explored techniques that incorporate depth information [17; 18]. These methods utilize the relationship between haze density and scene depth, employing technologies like stereo vision, structured light, and depth sensors to estimate depth maps [19]. While depth-based methods can improve dehazing performance, obtaining accurate depth information in real-world applications is challenging, especially for distant objects or scenes with complex geometric structures [20; 21].