esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Cement-Based Smart Composites to Civil Structural Health Monitoring: A Review
## 3 Applications to SHM of Structural Prototypes
### Medium and Full-Scale Members
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sensor Dimensions**} & \multicolumn{4}{c}{**Installation**} & \multicolumn{4}{c}{**Prototype**} \\ \hline
**Reference** & **B\({}_{\text{s}}\)2** & **H\({}_{\text{s}}\)2** & **L\({}_{\text{s}}\)2** & **L\({}_{\text{s}}\)2** & **Type 3** & **Number 4** & **Member** & **B\({}_{\text{p}}\)5** & **H\({}_{\text{p}}\)5** & **L\({}_{\text{p}}\)5** & **Material** & **Loading condition** \\ \hline
**(c)** & **(mm)** & **(mm)** & **(mm)** & **(c)** & **(c)** & **(mm)** & **(mm)** & **(mm)** & **(c)** & **(c)** \\ \hline
[71] & 40 & 30 & 50 & Embedded & 1 & Column & 100 & 100 & 300 & Concrete & Cyclic axial load \\ \hline
[72] & 10 & 10 & 30 & Embedded & 12 & Beam & 40 & 40 & 160 & Cement paste & Monotonic bending \\  & & & & & & & & & & Cyclle bending \\ \hline
[73] & 50 & 20 & 50 & Embedded & 1 & Beam & 100 & 100 & 500 & Concrete & Monotonic bending \\  & & & & & & & & & & Monotonic axial load \\ \hline
[74] & 20 & 20 & 50 & Embedded & 1 & Column & 150 & 150 & 300 & Concrete & Monotonic axial load \\  & & & & & & & & & & Cyclle axial load \\ \hline
[75] & 200 & 30/60/90 & 3000 & Embedded & & Beam & & & & RC & Monotonic bending \\ \hline
[76] & 305 & 305 & 507 & Embedded & & Column & & & RC & Cyclic lateral load \\ \hline
[77] & 20 & 20 & 80 & Attached & 18 & Beam & 200 & 300 & 3900 & RC & Cyclic bending \\  & & & & & & & & & & & \\ \hline
[78] & 25.4 & 25.4 & 25.4 & Embedded & 6 & Column & 450 & 450 & 1600 & RC & Cyclic lateral load \\ \hline
[79] & 125 & 78 & 350 & Embedded & 2 & Beam & 125 & 350 & 1500 & RC & Monotonic bending \\ \hline
[53] & 50 & 50 & 50 & Embedded & 7 & Beam & 250 & 250 & 2000 & RC & Cyclic bending, Vibration \\
[62,80] & 50 & 50 & 50 & Embedded & 7 & Beam & 250 & 250 & 2000 & RC & Vibration test, monotonic bending and environment action \\ \hline
[54] & 50 & 50 & 50 & Mechanical & 1 & Beam & 600 & 245 & 4500 & RC & Vibration test \\ \hline
[81] & 52 & 52 & 150 & Embedded & 1 & Beam & 130 & 160 & 650 & Reinforced mortar & Monotonic bending \\ \hline \hline \end{tabular}  
* \({}^{1}\) Void cell means that no specific information is provided. 2 B, H, L\({}_{\text{s}}\)- cross-section width, cross-sectional depth, length, of the CSC. 3 Instillation type: Embedded (E): Attached with a bonding agent (A): Mechanical connected (MC). 4 Number of CSCs installed in each monitored structural prototype. 5 B, H\({}_{\text{p}}\), L\({}_{\text{p}}\)- cross-section width, cross-sectional depth, length, of the structural prototype.  
\end{table} 
Table 2: Features of CSC sensors and structural prototypes tested for civil SHM purposes 1.
# A New Linear Model for the Calculation of Routing Metrics in 802.11s Using ns-3 and RStudio
###### Abstract  
Wireless mesh networks (WMNs) offer a pragmatic solution with a cost-effective ratio when provisioning ubiquitous broadband internet access and diverse telecommunication systems. The conceptual underpinning of mesh networks finds application not only in IEEE networks, but also in 3 GPP networks like LTE and the low-power wide area network (LPWAN) tailored for the burgeoning Internet of Things (IoT) landscape. IEEE 802.11s is well known for its facto standard for WMN, which defines the hybrid wireless mesh protocol (HWMP) as a layer-2 routing protocol and airtime link (ALM) as a metric. In this intricate landscape, artificial intelligence (AI) plays a prominent role in the industry, particularly within the technology and telecommunication realms. This study presents a novel methodology for the computation of routing metrics, specifically the ALM. This methodology implements the network simulator ns-3 and the RStudio as a statistical computing environment for data analysis. The former has enabled for the creation of scripts that elicit a variety of scenarios for WMN where information is gathered and stored in databases. The latter (RStudio) takes this information, and at this point, two linear predictions are supported. The first uses linear models (lm) and the second employs general linear models (glm). To conclude this process, statistical tests are applied to the original model, as well as to the new suggested ones. This work substantially contributes in two ways: first, through the methodological tool for the metric calculation of the HWMP protocol that belongs to the IEEE 802.11s standard, using lm and glm for the selection and validation of the model regressors. At this stage the ANOVA and STEPWIZE tools of RStudio are used. The second contribution is a linear predictor that improves the WMN's performance as a priori mechanism before the use of the ns-3 simulator. The ANCOVA tool of RStudio is employed in the latter.  
W 802.11s; HWMP; ALM; ns-3; RStudio Article  
A New Linear Model for the Calculation of Routing  
802.11s Using ns-3 and RStudio  
1
# Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
## 6 Discussion  
We have presented a framework to support collaborative learning by a group of robots. The system provides a robot collective with the capability of improved learning. The usability test suggests that the ontology can be used to generate useful cooperative robot learning data. The limitation of robot learning is that is difficult to bring knowledge from multiple robots, environments, and applications together. For example, two robot users may have different words to describe the same object, making them distinct and preventing sharing of the data. The ontology enables description of objects to create a common understanding a classes in a hierarchy. Our framework uses cloud storage so that the robots can effectively share knowledge for object recognition and object grasping strategies. This solves the problem of memory and processing limitations of individual robots.  
Grasping and moving an object requires complex computation, because physical robot information specified in URDF is necessary to calculate a relative position between the robot and the object, in order to move the robot arm to the object. The computation results in power consumption. Using an ontology to store grasping strategies allows robots to retrieve grasping strategies without performing extensive computation. This will reduce the robot's energy consumption and computation time profile.  
In the future, we plan to improve the framework by adding more capabilities to support cooperative learning in which many robots share knowledge without the need to re-learn. This reduces the problem of storing large amounts of data and reduces the burden of processing, which is one of the major problems in the practical development  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Template Matching** & **Iterative Closest Point** \\ \hline Accuracy & 85.00 & 91.50 \\ F1-Score & 79.96 & 87.24 \\ IoU & 62.60 & 64.42 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of object recognition algorithms for bottles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Template Matching** & **Iterative Closest Point** \\ \hline Accuracy & 84.00 & 94.50 \\ F1-Score & 80.40 & 91.14 \\ IoU & 61.64 & 68.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of object recognition algorithms for drinking cups.  
of robots. We plan to extend our ROD ontology to support more types of objects that are not described in the SUMO ontology. As pointed out by one of this paper's reviewers, self-learning of object ontologies and semantic representation of 3D object structure are useful and interesting ideas to explore as part of the future work. Modeling a semantic representation of 3D objects would enable a wider application of the proposed framework beyond simple 3D object recognition; see [39] for an example of an application in the 3D cultural artifact domain. We also plan to incorporate voice commands to facilitate the commanding of robots, avoiding the need of use a web browser to execute training and detection operations.
# Effects of a Dual-Task Paradigm and Gait Velocity on Dynamic Gait Stability during Stair Descent
## 2 Materials and Methods
### Statistical Analysis  
Gait velocity and IA normality were tested using the Shapiro-Wilk test. One-way repeated-measures ANOVAs were employed to examine the first and second hypotheses. Multiple regression analysis was used to predict the independent variables of IA (y) based on the dependent variable of velocity (x) during ST, CT, and MT conditions. Categorical variables were used to indicate the various conditions with the values set to A = 0/B = 0, A = 1/B = 0, and A = 0/B = 1 for the ST, CT, and MT conditions, respectively. Regression analysis was conducted for IA in the anterior/posterior and medial/lateral directions separately. The full regression model was  
\[\text{y}=\text{a}_{0}+\text{b}_{0}\text{x}+\text{A}\text{a}_{1}+\text{A}\text{ b}_{1}\text{x}+\text{B}\text{a}_{2}+\text{B}\text{b}_{2}\text{x}+\text{e} \tag{4}\]  
where a\({}_{0}\), a\({}_{1}\), a\({}_{2}\), b\({}_{0}\), b\({}_{1}\), and b\({}_{2}\) are regression coefficients; A and B are dummy variables denoting different conditions; and e is the error term. Dummy variables in this regression analysis procedure were used to distinguish regression equations under different conditions.  
Figure 1: (**a**) Graphical representation of the right foot single-support phase beginning with the left toe-off the fourth step of the staircase and ending with the left foot contacting the second step, also showing the anterior/posterior center of mass (COM)–center of pressure (COP) inclination angle \(\alpha\). (**b**) The medial/lateral COM–COP inclination angle \(\beta\).  
A stepwise forward elimination procedure was used to determine the optimum regression equation in each condition. For a term to be retained, the term should significantly contribute to the prediction of y (\(p<0.05\)). The collinearity of the variables was checked simultaneously, and it was confirmed if the variance inflation factor (VIF) was greater than 3.
## 3 Background and Preliminaries  
The fast advancement in communication technologies has introduced cheap, low-power and multifunctional devices which leverage the idea of the sensor [1]. Wireless Sensor Networks (WSNs) can be defined as a kind of networks that is formed by tens to thousands of tiny sensors which are densely deployed in an unattended environment. The most critical constraint on the sensor nodes is the energy consumption, since these nodes have very limited and unchangeable power sources. As a result, this restriction influences the design of WSN protocols or algorithms.  
The main function of sensor node is to detect events in the sensor field, perform some simple processing tasks and send the data to some authorized party, such as a network administrator. Based on that, the energy is consumed in three forms: sensing, data processing, and communication operations. The most energy is spent on the communication part which involves the transmission and reception of data among the nodes. According to [32], the amount of energy needed to transmit one bit equals the energy needed to perform thousands of CPU operations inside the sensor. Two unique features that differentiate WSNs from other networks were mentioned in [1]: first, the position of sensor nodes is not determined or engineered beforehand and this allows the random deployment at inaccessible environments and disaster relief operations; this feature poses a challenge for designing a self-organizing algorithms and protocols; second, the distributed data processing; instead of sending raw data to fusion node (base station or cluster head), the nodes can use their processing capabilities to locally carry out some simple required computations and send only the summary that is beneficial for decision making.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods
### Fabrication of Carbon Nanotube RF Transistors  
The fabrication of RF transistors is a process based on a lift-off process. In this process, patterning techniques such as electron-beam lithography (Raith voyager, Dortmund, Germany), thin film deposition techniques such as electron-beam deposition (DE400) and atomic-layer deposition (Ultratech Cambridge Savannah G2, Waltham, MA, USA), etching equipment (Trion Minilock-Phantom III, Miami, FL, USA), and an optical microscope (Carl Zeiss Axio Imager, Gottingen, Germany) are mainly used. Here, the fabrication of two-finger as well as multi-finger top-gate carbon nanotube transistors with an air-spacer between the source and drain took place. First, the source-drain contacts (Pd/Au = 20/10 nm) were deposited by electron-beam evaporation on pretreated CNT films. The channel region was then patterned by electron beam lithography followed by oxygen plasma etching. A Ti/Au = 5/90 nm stacked film was deposited as the lower interconnection line. The overlap of the interconnects was covered by overexposed PMMA 200 K bridges as jumpers. HfO\({}_{2}\) films with a thickness of 5 nm were then grown by ALD at 90 \({}^{\circ}\)C (40 cycles) as oxide dielectrics. A Ti/Pd/Au (0.3/5/180 nm) stacked film was deposited in the center of the channel as the gate. Finally, a Ti/Au = 10/400 nm metal film was deposited in the area to be connected as the gate electrode, the top metal, and the GSG structure. The details of the fabrication process are shown in Figure S1 in the Supplementary Materials. The as-fabricated two-finger-configuration RF transistor based on randomly oriented carbon nanotube films is shown in Figure 1b of the SEM picture.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 2 Basic Theory  
The range localization method includes three steps. First, DEMON spectrum analysis is employed to extract the low-frequency line spectrum components from the wideband ship noise. Second,the range localization can be achieved based on the intensity of the sound pressure difference over the time interval. Finally, the weighted least square (WLS) fusion method is applied to fuse the multi-hydrophone ranging results. The flow diagram is as shown in Figure 1.
## 4 Simulation Results and Analysis
### Stress Analysis of the Surrounding Rock Induced by Mining Activities  
#### 4.2.1 Global Stress Distribution of the Surrounding Rock  
As shown in Figure 7, each excavation phase induced significant stress redistribution. After the excavation of the 43 lower01 working face, a dramatic change in surrounding rock stress was observed, with a distinct high-stress zone located at the edge of the mined-out area. Local stress in this area could exceed 70 MPa, though it remained largely outside the range of excavation activities. The stress in the surrounding rock beneath the mined-out area generally decreased, with values ranging from \(-0.8\) MPa to 1 MPa. Moving from the edge of the mined-out area toward unmined strata, stress gradually transitioned from high to initial levels.  
Figure 5: Broken status after finishing 43 lower01 working face excavation.  
Figure 6: Broken status after finishing 43 lower02 working face excavation.  
Figure 7: Stress distribution of surrounding rock after each excavation period.  
Upon completion of mining at the 43 upper02 working face, the stress redistribution followed a similar pattern to that of the 43 lower01 working face. However, by this stage, the fractured rock from the 43 upper01 mined-out area had largely collapsed, causing irregular rock block contact, which induced localized stress concentration primarily in the secondary key strata and siltstone interlayer, with stress levels exceeding 56 MPa.  
By the completion of mining at the 43 lower01 working face, it became difficult to distinguish the stress redistribution characteristics typical of individual coal seam extractions. Local and multi-point stress concentrations, resulting from rock collapses and irregular block contacts, led to an overall stress state in the coal seam range, with the highest stress remaining below 30 MPa.  
After the 43 lower02 working face was mined, localized, multi-point stress concentrations in the mined-out area persisted. It is noteworthy that the stress distribution at the edge of the mined-out area varied due to the different collapse states of the primary and secondary key strata. On the 43 lower02 side, where the primary and secondary key strata had completely collapsed, rock block contact maintained a high-stress transmission state, and the stress concentration was primarily found at the fracture zones above the mined-out area. On the 43 lower01 side, where the primary and secondary key strata remained in a cantilevered state, a significant stress concentration was observed at the edge of the mined-out area.  
Comparing the fracture state diagrams for each excavation stage, it is evident that the high-stress regions are typically located at the edges of the mined-out area, with high-stress zones within the mined-out area found between relatively intact rock blocks or at the contact points between rock blocks and surrounding strata.
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
###### Abstract  
The compressive sensing method is an effective way of under-sampling array signal processing, and the measurement matrix is a key technology of compressive sensing processing, which is of decisive significance to improve the performance of under-sampling array signal processing. In this paper, an under-sampling array signal processing method based on an improved Hadamard measurement matrix was proposed. This method improved the construction of the measurement matrix by a compressing zero method capable of reducing the redundant data, enhancing the non-correlation between columns, improving the RIP condition, and improving the performance of the compressive sensing method. The performance was verified by simulation and real data from the lake, and the results showed that the method proposed in this paper has obvious performance advantages compared with the Toeplitz Circular Matrix, the m-sequence-based matrix, and the partial Hadamard matrix under the same conditions.  
compressive sensing; measurement matrix; array signal processing; improved Hadamard matrix; underwater acoustic signal +
Footnote †: journal: Applied Sciences
###### Abstract  
The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications.  
Keywords:6G; AI/ML; deep reinforcement learning; maritime communications; non-terrestrial network; space-air-ground networks; UAV relaying; underwater network
# A Quantum-Based Beetle Swarm Optimization Algorithm for Numerical Optimization
## 4 Experiment
### Multimodal Unconstrained Optimization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Algorithm** & **Best** & **Worst** & **Average** & **Variance** & **Time(s)** \\ \hline \multirow{6}{*}{Ackley} & PIO & 0.0694 & 8.2592 & 4.3592 & 4.2878 & 0.023 \\  & SOA & 11.3070 & 21.3684 & 21.0656 & 1.1158 & 0.016 \\  & GWO & 20.7051 & 21.1816 & 21.0613 & 0.0065 & 0.015 \\  & **QBSO** & **10\({}^{\mathbf{-4}}\)** & **0.0015** & **10\({}^{\mathbf{-4}}\)** & **10\({}^{\mathbf{-8}}\)** & 0.097 \\  & BSO & 10\({}^{\mathbf{-5}}\) & 20 & 3.5073 & 25.7410 & 0.006 \\  & PIO & 0.0011 & 1.0355 & 0.6206 & 0.1467 & 0.029 \\  & SOA & 1.0423 & 13.6958 & 5.2995 & 10.9620 & 0.017 \\  & GWO & 0.1494 & 0.9737 & 0.5470 & 0.0272 & 0.016 \\  & **QBSO** & **10\({}^{\mathbf{-10}}\)** & **10\({}^{\mathbf{-8}}\)** & **10\({}^{\mathbf{-8}}\)** & **10\({}^{\mathbf{-16}}\)** & 0.095 \\  & BSO & 1.2868 & 10.8748 & 3.9758 & 2.7785 & 0.007 \\  & PIO & 13.1031 & 312.7558 & 131.6508 & 10\({}^{\mathbf{3}}\) & 0.028 \\  & SOA & 159.2776 & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{8}}\) & 0.011 \\  & GWO & 85.6812 & 329.1141 & 193.3601 & 10\({}^{\mathbf{3}}\) & 0.016 \\  & **QBSO** & **10\({}^{\mathbf{-6}}\)** & **10\({}^{\mathbf{-4}}\)** & **10\({}^{\mathbf{-5}}\)** & **10\({}^{\mathbf{-9}}\)** & 0.093 \\  & BSO & 229.76 & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{7}}\) & 0.006 \\  & PIO & 0.0047 & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{3}}\) & 10\({}^{\mathbf{7}}\) & 0.006 \\  & SOA & 11.1423 & 10\({}^{\mathbf{9}}\) & 10\({}^{\mathbf{8}}\) & 10\({}^{\mathbf{17}}\) & 0.018 \\  & GWO & 62.0423 & 10\({}^{\mathbf{4}}\) & 10\({}^{\mathbf{3}}\) & 10\({}^{\mathbf{7}}\) & 0.018 \\  & **QBSO** & **10\({}^{\mathbf{-5}}\)** & **0.0109** & **0.0023** & **10\({}^{\mathbf{-6}}\)** & 0.109 \\  & BSO & 10.51 & 10\({}^{\mathbf{9}}\) & 10\({}^{\mathbf{8}}\) & 10\({}^{\mathbf{18}}\) & 0.013 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the multimodal benchmark function experiments (population size = 8).