esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 3 State of the Art of Practice and Research
### Current Practice and Research
#### 3.1.4 Remote Sensing of Future Transportation Infrastructures  
UAVs have already been implemented for remote sensing applications, which refers to identifying and monitoring an area's physical features by measuring the reflected and emitted radiation from a distance. This process requires data acquisition with the aid of specialized cameras and sensors. UAVs can provide a safe and cost-efficient way of data acquisition. They can also operate at low altitudes, resulting in ultra-high-resolution spatial data. Remote sensing is a generic and broad term. Its various applications, primarily in the transportation sector, encompass vehicle navigation, monitoring rural roads' conditions, measuring vehicle emissions, urban planning, and other traffic management [78].  
Information regarding road pavements' health and damage level could be obtained non-invasively using remote sensing techniques. [PERSON] et al. presented an approach for surveying road conditions by integrating remote sensed aerial and satellite visual and thermal image data from UAVs, spectroscopy, and ground-penetrating radar [57]. [PERSON] and [PERSON] presented photogrammetric methods to analyze road surface analysis using a UAV to obtain road imagery [59]. This imagery was then used for three-dimensional (3-D) reconstruction of the road surface.  
In the experimental study by [PERSON] et al., the performance of a fixed-wing UAV was compared to that of a multi-rotor UAV for condition assessment of unpaved roads. It was concluded that multi-rotor UAVs could provide better resolution than fixed-wing UAVs, and fixed-wing UAVs should be preferred when data collection takes a long time [60]. [PERSON] et al. presented a methodology to analyze the sight distance on highways for increasing highway safety. A small UAV was employed to collect the data in this experimental study, and subsequently, a 3-D road environment model was built [62]. This model was concluded to be adequate for sight distance assessment on highways, thus increasing highway safety and possibly preventing road accidents. [PERSON] et al. proposed and evaluated technology for infrastructure condition monitoring, using UAVs coupled with a high-quality camera and a global navigation satellite system [61]. The data obtained can be used to identify distress features in transportation physical infrastructure like permanent deformation and cracking patterns.
## 3 Results and Discussions
### Split Tensile Strength  
In these experiments, cylindrical specimens of 30 cm height and 15 cm diameter were considered. For different replacement levels of NS and RHA, all the mixes' split tensile strength was determined at the ages of 7, 14, and 28 days. Table 8 and Figure 5 show the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Sample**} & \multirow{2}{*}{**NS and RHA in Concrete (\%)**} & \multicolumn{3}{c}{**Compressive Strength (N/mm\({}^{2}\))**} \\ \cline{2-4}  & & **7 Days** & **14 Days** & **28 Days** \\ \hline
1 & 0\% & 3.16 & 3.412 & 3.71 \\
2 & 0.5\% NS \& 5\% RHA & 3.21 & 3.60 & 3.91 \\
3 & 1.0\% NS \& 10\% RHA & 3.34 & 3.842 & 4.2 \\
4 & 1.5\% NS \& 15\% RHA & 3.28 & 3.789 & 4.15 \\
5 & 2.0\% NS \& 20\% RHA & 3.23 & 3.58 & 3.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Flexural strength of concrete.  
Figure 5: Comparison of flexural strength of concrete with different proportions of NS and RHA.  
split tensile strength test results. Figure 6 shows the split tensile strength of concrete with different content of NS and RHA. The addition of NS and RHA resulted in an increase of the split tensile strength at 28 days. This trend was observed up to 10% for all NS and RHA content mechanical properties, which can be explained by the rapid consumption of calcium hydroxide produced during the hydration process. After 10% NS and RHA content, the reduced concrete strength values can be attributed to the proportions of NS and RHA, which are higher than the amount needed during the hydration process to be combined with the released lime. [PERSON] et al. (2018) tested the splitting tensile strength of nano-SiO concrete. When 3% nano-SiO replaced cement, the tensile strength of NS modified concrete was improved by 16.10% over that of ordinary concrete. [PERSON] et al. (2018) tested the splitting tensile strength of nano-SiO concrete. When 8% nano-SiO replaced cement, the tensile strength of NS modified concrete was improved by 16.10% over that of ordinary concrete. Nano-SiO enhances the interfacial strength between the concrete matrix and the aggregate, which causes this behavior ([PERSON] et al., 2018). The addition of nano-SiO to concrete serves as both a nano-reinforcement and a filling agent, filling the holes in the concrete matrix ([PERSON] et al., 2018).
# Broadband Holography via Structured Black Silicon Nano-Antennas
###### Abstract  
Computer-generated holograms have wide applications in different fields of optics, ranging from imaging, data storage, to security.Herein, we report a new method for the fabrication of large-scale computer-generated holograms from an inexpensive material, such as Silicon. Our approach exploits dry etching to create a series of broadband nanoantennas, which can tune the reflectivity of Si from an average of 0.35 to 0.1 in the entire visible range. We demonstrated the realisation of different images at wavelengths of 450 nm, 532 nm, and 632 nm with an efficiency of 10%, 14%, and 12%, respectively, thus opening up the application of large-scale broadband computer-generated holographic surfaces.  
computer-generated holograms; black silicon; broadband holography +
Footnote †: journal: Applied Physics Letters B
## 3 Proposed System Model
### Integration of DQN-Base Approach  
In this paper, we present DAWPC-MAC protocol which improves communications efficiency in underwater networks with the RL. Under this framework, decision-making is agent-driven, and the courier node acts as an agent that learns to make the best decisions based on its experiences in the environment while maximizing long-term rewards. Rather than relying on fixed rules, the receiver node learns to determine its actions based on network state information and feedback in the form of rewards. Because high-dimensional state-action spaces invalidate traditional Q-learning algorithms that rely on lookup tables. To overcome this limitation, Deep Q-Networks (DQNs) combine Q-learning with deep neural nets (DNNs), which enables the agent to learn via function-approximation instead of storing the Q-values in a table. This allows for effective decision-making in complex contexts with continuous state spaces. The following subsections detail the state representation, action space, reward function, and Q-value updates that guide the protocol's decision-making.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 3 Aggregated Control Strategy for Multi-Type Loads in Industrial Parks
### Day-Ahead Optimal Dispatch of Aggregated Multi-Type Loads in Industrial Parks  
Day-ahead optimal dispatch is used to determine the start-stop combination and power regulation plan for each controllable load in 15 min intervals for the next day.
## 3 Our Proposed Method
\[\begin{cases}F_{11}=\mu_{1}1+\mu_{2}\mathcal{F}(\mathbf{\mathrm{D}}_{x})^{*}\circ \mathcal{F}(\mathbf{\mathrm{Q}}_{x})+\mu_{2}\mathcal{F}\big{(}\mathbf{\mathrm{Q}}_{y} \big{)}^{*}\circ\mathcal{F}(\mathbf{\mathrm{Q}}_{y})\\ F_{12}\ =\mu_{2}\mathcal{F}(\mathbf{\mathrm{Q}}_{y})^{*}\circ\  
```
0: Observe an EIT image \(f\) with parameters \(\lambda>0\), \(\mu_{1}\), and \(\mu_{2}>0\) given the iteration termination conditions;  Init: \(u^{(0)}=f\), \(p^{0}=\left(p_{1}^{0},p_{2}^{0}\right)=(0,0)\), \(v^{0}=\left(v_{1}^{0},v_{2}^{0}\right)=0\), \(w_{0}^{0}=0\);  Master Iterator: For \(k=0,1,2...\), calculate as follows
```
1: Calculate \(y^{k+1}=\text{Shrink}_{2}\left(\
abla u^{k}-p^{k}-v^{k},\alpha_{1}/\mu_{1}\right)\);
2:\(z^{k+1}=\text{Shink}_{\text{F}}\Big{(}\varepsilon\Big{(}p^{k}\Big{)}-w^{k}, \alpha_{0}/\mu_{2}\Big{)}\);
3: Solve the equations \(\left(\lambda A^{\mathbf{T}}A+\mu_{1}\mathbf{Q}_{x}^{*}\mathbf{Q}_{x}+\mu_{1} \mathbf{Q}_{y}^{*}\mathbf{Q}_{y}\right)u^{k+1}=\lambda A^{\mathbf{T}}f+\mu_{1 }\mathbf{Q}_{x}^{*}(y_{1}^{k+1}+p_{1}+v_{1}^{k})+\mu_{1}\mathbf{Q}_{y}^{*} \left(y_{2}^{k+1}+p_{2}+v_{2}^{k}\right)\), and obtain \(u^{k+1}\);
4: Calculate \(p_{1}^{k+1}=\mathcal{F}^{-1}\Big{(}\frac{F_{11\circ 2}E_{-12\circ 2}E_{1}}{F_{11 \circ 0}F_{11}-F_{12\circ 2}E_{12}}\Big{)}\), \(p_{2}^{k+1}=\mathcal{F}^{-1}\Big{(}\frac{F_{11\circ 2}E_{-2\circ 2}E_{1}}{F_{11 \circ 0}F_{11}-F_{12\circ 2}E_{12}}\Big{)}\);
5: Update \(v^{k+1}=v^{k}+y^{k+1}-\left(\
abla u^{k+1}-p^{k+1}\right)\);
6: Update \(w^{k+1}=w^{k}+z^{k+1}-\varepsilon\Big{(}p^{k+1}\Big{)}\);
7: When \(u^{(}k+1)\) satisfies the given iteration termination condition, record \(k^{*}=k+1\) as the terminal iteration. Otherwise, set \(k:=k+1\), and proceed to step (1) to continue the iterative process. 0: Generate EIT image \(\mathbf{u}^{(k^{*})}\). ```  
**Algorithm 1** The algorithm of the second-order generalized regularization model for EIT.
## 5 Bonded Anchor Tests
### Time-to-Failure Determination  
Prior to analyzing stress versus time-to-failure data, it is important to define exact failure times. These are determined by the time of full load application as discussed in the previous chapter, but also the time at which a specimen is considered to be failed. In this contribution, two different approaches are investigated. The first approach is based on the recorded pressure (or force) drop in time, as it is shown in Figure 7. The measured pressure in the hydraulic jack is extracted and monitored in time, with a frequency of 10 Hz for the first 24 h. Afterwards, the sampling rate was reduced to 1 Hz. The first obvious drop in pressure significantly larger than the above-mentioned \(\pm\)2% hysteresis in the controlled load is defined as the failure time.  
The second approach is based on a nominal definition of failure times as intersection point between two linear regression lines in a displacement versus time plot, as sketched in Figure 8. A linear trend-line is fitted once for the secondary creep stage, for times \(0.3t_{f}\leq t\leq 0.6t_{f}\), and once for the tertiary creep stage \(0.9t_{f}\leq t\leq t_{f}\), where \(t_{f}\) the failure time, and \(t\) the times used for the regression. This approach is discussed in detail in [24]. A comparison between both approaches revealed minor differences. In this contribution, all reported failure times are defined following the first approach.  
Figure 7: Schematic view of pressure drop in time used to determine time-to-failure.
## 1 Introduction
[PERSON] and [PERSON] (2019, 2020) conducted an in-depth investigation of tower failure, along with the lessons learned from a post-disaster inspection. They concluded that a considerable percentage of incidents might be related to bolt strength deficiencies. [PERSON] et al. delineated prevalent issues linked to flanges and bolted connections while proposing potential resolutions ([PERSON] et al., 2020). For instance, seals should shield bolted connections from seawater for the duration of usage. Furthermore, optimizing the force dynamics of the hammer can mitigate peak stresses in the flange, diminish fatigue stresses, and decrease noise emissions during piling operations. Further simplification can be achieved by eschewing intricate sealing configurations and avoiding post-delivery machining of flanges provided by specialist suppliers. Undoubtedly, catastrophic events need to be avoided through prudent design. Despite broad recognition of the significance of the tower bolt fatigue life, to the authors' best knowledge, little research has been quantitatively conducted on the effect of various flange gaps on bolt fatigue. To this end, a finite-element model of a flange segment incorporating bolt pretension and contact elements is herein proposed. Three types of flange range are defined artificially. Consequently, the effects of various flange gaps on bolt fatigue are quantitatively revealed. A particular finding of note is that the bolt fatigue is sensitive to a particular type of the flange gap. The rest of this paper is structured as follows. Section 2 describes the procedure for the Schmidt-Neuper approach and bolt fatigue analysis. Using the FE technique, Section 3 evaluates the effects of various flange gaps on bolt fatigue damage. Section 4 concludes with a succinct summary.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 6 Discussion  
In this article, we analyzed whether it was possible to employ an Isolation Forest model to detect jamming attacks in a 6G drone scenario. We tuned the hyperparameters of the model and tested it against the default IF model. We achieved a considerable improvement in performance thanks to the tuning process (Section 5.2.1), but despite the high recall and accuracy scores, the model experienced a high number of false positives, caused by the inherent noise of the dataset. To mitigate this issue, we proposed the integration of the IF model with a Majority Rule module (Section 5.2.2). The _window_size_ parameter of the MR module was tuned to provide the best performance. The addition of the MR module proved extremely effective in reducing the number of false positives, greatly improving the model's precision.  
Integration of the MR module also impacted total classification time as shown in Figure 17a, but the linear trend [26] that we expected from the IF model was preserved, meaning that the classification time complexity was kept at \(O(n)\). In our opinion, the trade-off between the increased classification time and the improved performance is justified, as the model is now much more reliable in a real-world scenario, where noise from the surrounding environment is almost always present. The linear time complexity and low memory usage make the proposed solution suitable for a resource-constrained environment such as the analyzed 6G drone scenario.  
As shown in Section 5.2.3, the proposed solution outperformed the solution proposed by [12] in both the detection of periodic jamming and the classification of normal traffic, while achieving the same performance against constant jamming attacks. We believe that the importance of correct classification of normal traffic is often underestimated, as a high number of false positives can lead to the activation of resource-intensive countermeasures, even at times when they are not needed.
## 2 Materials and Methods
### Computational Fluid Dynamics Setup
#### 2.1.2 Clean Inlet Flow Boundary Conditions  
Three-dimensional steady compressible Reynolds-averaged Navier-Stoke equations have been solved using the k-\(\varepsilon\) turbulence model. At the rotor inlet and outlet boundary, the P-total inlet P-static outlet is considered. Furthermore, the flow direction is specified for a clean inlet at design RPM 16,043, whereas no-slip conditions are used on walls, and periodic conditions are applied at the periodic surfaces. Numerical computations were carried out from choking conditions to near stall conditions by gradually rising outlet  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{Rotor inlet hub-tip diameter ratio} & 0.75 \\ \multicolumn{1}{c}{Rotor blade Inlet tip radius} & 257 mm \\ \multicolumn{1}{c}{Rotor blade aspect ratio} & 1.56 \\ \multicolumn{1}{c}{Tip clearance} & 1.016 mm \\ \multicolumn{1}{c}{Tip solidity} & 1.29 \\ \multicolumn{1}{c}{Hub solidity} & 3.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of axial compressor rotor blade [20].  
Figure 1: Hexahedral grid mesh of a single blade for rotor-67 [13].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Sr. No.**} & **Mesh** & **No. of Nodes (Million)** & **Design Mass Flow Rate (Kg/S)** \\ \hline
1 & Coarse & 0.037 & 33.43 \\ \hline
2 & Medium & 0.16 & 33.64 \\ \hline
3 & Fine & 0.43 & 33.54 \\ \hline
4 & Super Fine & 0.97 & 33.14 \\ \hline
5 & Very Fine & 1.47 & 33.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mesh dependence study [14].  
average static pressure to obtain a compressor rotor characteristic map. The near stall point is predicted at the last stable condition of rotor 67. The following equation of Sutherland viscosity law with three coefficients for ideal gas has been applied [21].  
\[\mu=\ \mu\omega\left(\frac{T}{To}\right)^{\frac{3}{2}}\left(\frac{To+S}{T+S}\right) \tag{1}\]
## 3 Results
\begin{table}
\begin{tabular}{l c c} \hline
**Comparison** &
\begin{tabular}{c} **Mean** \\ **Difference (\(\upmu\)m)** \\ \end{tabular} & **95\% CI** & **Cohen’s d** \\ \hline CBCT vs. IOS (Marginal) & 72.27 & [64.85, 79.68] & 2.17 \\ CBCT vs. Model (Marginal) & 59.6 & [52.18, 67.01] & 2.0 \\ IOS vs. Model (Marginal) & \(-\)12.67 & [\(-\)20.08, \(-\)5.25] & \(-\)0.55 \\ CBCT vs. IOS (Internal) & 100.09 & [87.96, 112.22] & 2.52 \\ CBCT vs. Model (Internal) & 70.91 & [58.79, 83.03] & 2.01 \\ IOS vs. Model (Internal) & \(-\)29.18 & [\(-\)41.30, \(-\)17.06] & \(-\)0.81 \\ \hline \end{tabular} CBCT = Cone Beam Computed Tomography; IOS = Intraoral Scanner; CI = Confidence Interval; \(\upmu\)m = Micrometer; Cohen’s d = Standardized measure of effect size.  
\end{table} 
Table 2: Pairwise comparisons with 95% confidence intervals and effect sizes. \begin{table}
\begin{tabular}{l c c c c c} \hline  & & \multicolumn{3}{c}{**AIG**} & \multicolumn{3}{c}{**OIG**} \\ \hline
**Group** & **N** & \
\begin{tabular}{c} **Mean \(\pm\) SD** \\ **(\(\upmu\)m)** \\ \end{tabular} & **95\% CIM (\(\upmu\)m)** &
\begin{tabular}{c} **Mean \(\pm\) SD** \\ **(\(\upmu\)m)** \\ \end{tabular} & **95\% CIM (\(\upmu\)m)** \\ \hline \multicolumn{10}{c}{} \\ CBCT & 11 & 219.45 \(\pm\) 51.48 \({}^{\text{a}}\) & 184.86 & 254.03 & 235.45 \(\pm\) 62.74 \({}^{\text{a}}\) & 193.3 & 277.6 \\ Intraoral Scanner & 11 & 140.45 \(\pm\) 31.63 \({}^{\text{b}}\) & 119.2 & 161.7 & 93 \(\pm\) 29.89 \({}^{\text{b}}\) & 72.91 & 113.08 \\ Model Scanner & 11 & 167.72 \(\pm\) 21.43 \({}^{\text{b}}\) & 153.32 & 182.13 & 125.90 \(\pm\) 39.67 \({}^{\text{b}}\) & 99.25 & 152.56 \\ \hline \end{tabular} The mean difference is significant at the 0.05 level. Groups with the same letters do not exhibit a statistically significant difference. SD: Standard deviation; CIM: Confidence Interval for Mean; LB: Lower bound; UB: Upper bound.  
\end{table} 
Table 3: Two-way ANOVA results for the comparison of wear values. used to evaluate internal fit. For both marginal fit and internal fit, the mean, standard deviation, and upper and lower bound values are shown in Table 4. According to the Bonferroni test results, the CBCT group (169.27 \(\upmu\)m \(\pm\) 38.64 \(\upmu\)m) demonstrated greater marginal and internal gap dimensions compared to the intraoral scanner group (97.00 \(\upmu\)m \(\pm\) 10.12 \(\upmu\)m) and the model scanner group (109.67 \(\upmu\)m \(\pm\) 9.72 \(\upmu\)m), and these differences were statistically significant (\(p<0.05\)).
## 3 Methodology
### Dual-Branch Graph Neural Network for Bubble Diagram Link Prediction
#### 3.2.2 Decentralized Node Subgraph Sampling and Centralized Node Subgraph Sampling  
We propose DNS and CNS to obtain multiple local subgraphs with common features. By learning the characteristics of these local subgraphs, the model's prediction performance can be enhanced. Both task feature encoders involve two steps: sampling and aggregation, utilizing the same SAGEConv aggregator but differing in their sampling strategies. SAGEConv is the feature encoder of GraphSAGE, which is an inductive learning model designed for large graphs [26]. The core idea behind GraphSAGE is to generate embeddings for nodes by sampling and aggregating information from their local neighborhoods. While GraphSAGE is optimized for large graphs using neighbor sampling to address memory limitations and global feature learning issues, bubble diagrams are smaller graphs. Multi-hop neighbor sampling in this context may yield subgraphs that are too similar to the input, increasing the risk of overfitting. Additionally, the diversity and inherent patterns of bubble diagrams, such as varying graph orders or random subgraph structures, can complicate feature learning.  
As shown in Figure 3, the nodes in the bubble diagram are divided into two categories: central node (living room node) and non-central nodes (bedrooms, toilets, kitchens, etc.). We observe that the bubble diagram has significant centrality, and the living room node usually connects to all other room nodes and has the highest degree. Additionally, non-central nodes are more likely to have wall links between them. The details of DNS and CNS are illustrated in Figure 4. DNS removes the central node and its edges, and it samples multiple subgraphs from the graph composed of non-central nodes. To enhance the embedding learning for non-central nodes, the subgraphs without a central node are used to train the wall link prediction branch. CNS samples multiple subgraphs based on the central node, and each subgraph must contain the central node. The subgraph containing the central node is used for training the door link prediction branch to fully perceive the intrinsic patterns of local subgraphs.  
Figure 3: Classification of bubble diagram nodes.  
All subgraphs are input into SAGEConv to learn node embeddings. GCN and GAT are not good at handling flexible bubble diagrams and tend to learn fixed node embeddings. Therefore, SAGEConv is used as graph feature encoder to extract the topology information of bubble diagrams. SAGEConv can be described as follows:  
\[h_{N(v)}^{k}=\text{AGGREGATE}_{k}\Big{(}\Big{\{}h_{u}^{k-1},\forall u\in\mathcal{ N}(v)\Big{\}}\Big{)} \tag{5}\]  
\[h_{v}^{k}=W^{k}\cdot\text{CONCAT}\Big{(}h_{v}^{k-1},h_{N(v)}^{k}\Big{)} \tag{6}\]  
where \(k\) denotes depth, \(v\) denotes the destination node, \(u\) is the neighbor of node \(v\), \(W\) is the weight matrix, \(h_{N(v)}\) is the neighborhood vector, and \(h_{v}\) is the node's embedding.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 2 Matrix Theory in Modern Wireless Communication Systems  
Nowadays, wireless communications have emerged as the fastest growing segment of the telecommunications industry. For example, cellular communication systems have exhibited an explosive growth over the last several decades and currently present over 6 billion users all over the world. Existing and future applications of wireless communications include multimedia Internet-based cell phones, high-quality real-time streaming of video and data signals, and transmission, reception and processing of all sorts of data collected by sensors and devices. The requirements imposed by these communication systems are subject to many technical challenges. In this section, two modern signaling schemes: MIMO and OFDM, which are currently adopted in many wireless communications standards to improve the performance of current wireless communication systems, are discussed. The presentation will focus mainly on how the matrix theory concepts and results can be applied to implement and assess the performance of MIMO and OFDM-based communications systems.