esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontological Approach for Automatic Inference of Concrete Crack Cause
## 2 Literature Review
### Crack Diagnosis Practice
#### 2.1.1 Crack Investigation
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Division** & **Required Information** & **Example** \\ \hline \multirow{8}{*}{**Crack Status**} & Generation Period & After concrete placing–Before curing, Within several hours–1 day after curing, Within a few days after curing, More than several ten days after curing etc. \\  & & Slab\_Top\_Edge, Slab\_Top\_Center, Slab\_Bottom\_Edge, Slab\_Bottom\_Center, Wall\_Door\_Edge, Wall\_Door\_Side, Wall\_Edge, Wall\_Center, Beam\_Side\_End, Beam\_Side\_Center, Beam\_Bottom\_End, Beam\_Bottom\_Center, Column\_Top, Column\_Center, Column\_Bottom etc. \\  & & Complexity: Single, Complex \\ \multirow{2}{*}{**Crack Status**} & Crack Patterns & Shape: Horizontal, Vertical, Diagonal, Stepped, Radial, X-Shaped, Grid, Reticulation, \(\Lambda\)-Shaped, V-Shaped \\  & & Regularity: Regularity, Random \\ \multirow{2}{*}{**Crack Status**} & Penetration status & Surface, Part-through, full depth-through \\  & & Exposure/Corrosion: Exposure/Corrosion, Exposure/No\_Corrosion, No\_Exposure/No\_Corrosion \\  & & Occurrence of Other defects & No\_Exposure/No\_Corrosion \\ \multirow{2}{*}{**Crack Status**} & Defects & Leak/Efflorescence: Leak/Efflorescence, No\_Leak/Efflorescence, Leak/No\_EEfflorescence, No\_Leak/No\_EEfflorescence \\ \hline \multirow{8}{*}{**Materials and construction conditions**} & Condition of concrete materials & Cement, aggregate, admixture, water type and trademark, test results, etc. \\  & Condition of concrete mixing & Rich mix, poor mix, planned mix, executed mix \\ \multirow{2}{*}{**Materials and construction conditions**} & Condition of concrete compaction and curing & Concrete mixing time, transportation time, wait time, pouring time, pouring amount, pouring method, pouring order, compaction method, finish method, curing method, etc. \\  & Records of Concrete construction & Test results of slump, air content, compressive strength, chloride, etc. \\  & Quality Test & Data to check displacement such as staging settlement or differential settlement of structures \\ \multirow{2}{*}{**Crack Status**} & Ground Conditions & Backfill time (earth pressure impact) and rising time of groundwater (impact of changes in water pressure variance and heat transfer in underground structures) \\  & Formworks & Impact of stripping time of staging, deformation due to concrete weight during concrete placement, and heat of hydration of concrete \\ \multirow{2}{*}{**Crack Status**} & Environmental Conditions during construction & Climate, temperature, humidity, wind direction, air volume, solar irradiance, rainfall, snowfall etc., during concrete placement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information Required to Estimate the Cause of Cracks.
## 4 Discussion
With respect to the all-chain scenario where all traceability information is stored on the Blockchain in different formats and modalities (i.e., immutable data, events, addresses), response time cannot be guaranteed as it can vary considerably, mainly when state changes are required (i.e., storing information in the blockchain), because blockchains can handle a limited number of transactions per second. As decentralisation and security are prioritised, scalability becomes a challenge ([PERSON]'s trilemma [54]). To increase scalability, consensus times would have to be reduced, which in turn would imply limiting participants and the reliability of consensus mechanisms, which eventually would compromise decentralisation (i.e., control being handed over to a reduced number of participants) and security (i.e., fewer participants mean a higher likelihood of attacks). All considered, storing only the cryptographic digest on the blockchain is generally more efficient and scalable, and provides reliable verification of the integrity of external content, although it requires managing external storage. Storing the complete content on the blockchain guarantees the immutability of traceability data, but at a significantly higher cost in terms of scalability, efficiency, response time, and privacy. In most cases, balancing these two approaches would be the advisable solution, that is, deploying data structures outside the blockchain that allow the trace to be calculated efficiently, while using the blockchain to facilitate notarisation and the reconstruction of the trace autonomously and independently of off-chain data structures, while keeping data on-chain to a minimum. This is the approach followed in the example implementation discussed in Section 3.2. On the other hand, this balanced approach guarantees that the blockchain's storage and query operations related to batch creation and trace generation are limited to specific moments across the value chain. The combination of these two strategies makes it possible to offer a reliable view of production chain operations, from raw material to the final product, while limiting blockchain interactions that could affect scalability and response times.
## 4 Performance Validation
It divides the sensor network into several parts, known as clusters, and cluster heads are selected in each cluster. Then, using short-distance connections, tree-based data aggregation takes over and collects sensory information directly from cluster heads. The CEPOC optimization determines the shortest path between the sink and the cluster heads. The use of compressive sensing minimizes the size of the packets that will be broadcast in the sensor network. A weakness of the proposed system is the lack of performance in the data aggregation process and underwater object tracking techniques. To address these concerns in the future, hybrid protocols will be utilized to extend the network lifespan by conserving energy more efficiently through data aggregation and object tracking for sensor networks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Number of Rounds for Energy Exhausted (NREE)**} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Nodes** \\ \end{tabular} } & \multirow{2}{*}{**LEACH**} & \multirow{2}{*}{**LEACH-ANT**} & \multirow{2}{*}{**CUHSN**} & \multirow{2}{*}{**EOCA**} & \multirow{2}{*}{**ACOCR**} & \multirow{2}{*}{**MCR-UWSN**} \\ \cline{1-1}  & & & & & & \\ \hline
300 & 463 & 631 & 718 & 775 & 919 & 1000 \\
325 & 523 & 691 & 751 & 859 & 952 & 1045 \\
350 & 619 & 793 & 823 & 904 & 1000 & 1093 \\
375 & 670 & 826 & 919 & 991 & 1111 & 1186 \\
400 & 709 & 913 & 985 & 1027 & 1168 & 1264 \\
425 & 781 & 946 & 1021 & 1111 & 1201 & 1288 \\
450 & 826 & 1045 & 1090 & 1156 & 1252 & 1336 \\
475 & 868 & 1099 & 1138 & 1225 & 1306 & 1387 \\
500 & 928 & 1138 & 1231 & 1267 & 1411 & 1489 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of MCR-UWSN model in terms of the number of rounds for energy exhausted (NREE). Figure 6: NREE analysis of MCR-UWSN method with existing approaches.
## 3 Applied Research
Key strategies include reinforcing dikes with geotextiles, employing soil consolidation through cement or bentonite injections, and utilizing embedded sensors for real-time structural monitoring. Drainage systems reduce freshwater infiltration, mitigating evaporite dissolution and void formation [21, 23]. Remote sensing tools like InSAR enable early detection of subsidence, guiding interventions to prevent structural failures and economic losses [15]. Subsurface stabilization, including cement/clay injections and dynamic compaction, addresses sinkhole risks, while underground barriers maintain hydrogeological balance [16, 26]. Modern monitoring systems, combining deformation sensors, real-time data platforms, and remote sensing technologies, provide predictive capabilities for adaptive risk management. Numerical models incorporating geophysical and structural data enhance the planning and effectiveness of mitigation efforts [15, 27]. Despite their complexity and cost, these solutions are essential for preserving infrastructure and minimizing geotechnical risks. Continuous integration of remote sensing technologies and digital modeling will further improve regional resilience. A case study exemplifies these approaches. The APC operates salt ponds surrounded by dikes in a sinkhole-prone area. In one instance, an unnoticed sinkhole caused a dike breach, draining an entire basin and resulting in USD 38 million in damages. To address such risks, APC partnered with SkyGeo, Oude Delft - Dutch company, to implement InSAR monitoring, providing high-density measurements that detected displacement patterns and enabled timely interventions. These measures prevented significant damage and underscores the effectiveness of modern engineering solutions in mitigating geohazards [21, 34]. By integrating remote sensing technologies with advanced geotechnical practices, the Dead Sea region has taken significant steps toward addressing its unique environmental challenges. These efforts ensure the safety and sustainability of industrial and tourism operations, preserving critical infrastructure in the face of dynamic tectonic and geomorphic pressures.
## 1 Introduction
First, we perform node clustering using a similarity tree. We calculate the structural similarity between nodes and construct the similarity tree based on the obtained results. Next, we perform a unification operation on each cluster after the initial clustering to prevent attackers from exploiting differences in the number of users across clusters for identification attacks. Finally, we propose an \(\alpha\)-partial isomorphism anonymization algorithm based on the concept of isomorphism. This method addresses different structural privacy requirements of users by using mapping relationships and mapping matrices. It aims to minimize modifications to the graph and improve the utility of the anonymized graph. Specifically, the main contributions of this paper are as follows. * We propose a new combined criterion for node similarity calculation to capture structural features of the 1-neighborhood subgraph from different factors, providing a foundation for subsequent node clustering. * We propose a similarity tree clustering method that constructs a connection relationship tree based on similarity results. This method better reveals correlations between users, achieves node clustering, and effectively mitigates information difference attacks. * We propose an \(\alpha\)-partial isomorphism (\(0<\alpha\leq 1\)) anonymization algorithm based on the concept of isomorphism, which meets users' personalized structural privacy requirements while enhancing the utility of anonymized graphs. * Based on the real datasets, the experimental comparison between the proposed TC\(\alpha\)-PIA scheme and the same type of scheme is implemented. The results show that TC\(\alpha\)-PIA has higher utility. In particular, the excellent performance in information loss fully indicates that TC\(\alpha\)-PIA has less impact on the original graph and better preserves the utility of the anonymized graph. The rest of the paper is organized as follows. Section 2 reviews the representative-related works. Section 3 introduces the privacy protection scenarios and problem definitions for social networks. Section 4 details the newly proposed TC\(\alpha\)-PIA. Section 5 evaluates the protocols and completes the experimental comparisons. Section 6 summarizes the work of this paper and gives future work.
## 6 GPS Simulation  
To verify the performance of the implemented VTL, a simulated GPS signal generated from STR 4500 was applied [20]. The simulation scenario is a rapidly received signal power attenuation on one satellite. This is a widely used simulation method to verify the tracking performance of VTL against momentary signal blockage or attenuation. The purpose of this simulation is to see how well the VTL can handle attenuated channel tracking compared with the conventional signal tracking algorithm. Figure 10 shows the satellite constellation and the signal power command on PRN 19, the target satellite.  
The duration of the attenuation is 10 s, between the 10 and 20 s marks in Figure 10. Figures 11 and 12 show the code and carrier frequency tracking results for PRN 19. The first row of plots shows the simulator command setting, the second row shows the tracked signal power obtained by correlation magnitude, \(\sum\ \mathrm{I}^{2}+\mathrm{Q}^{2}\), where I and Q are in-phase and quadrature-phase accumulation values, respectively, the third row shows pseudorange or range-rate measurements, the fourth row shows discriminator output and the fifth shows tracking control input.  
Figure 10: Sky plot (left) and signal power command on PRN 19 (right), (PRN 6 and PRN 26 are not used because of their low elevation angle).  
Figure 11: Code tracking result of PRN 19: Independent DLL (left) and Vector DLL (right).  
Figure 12: Code tracking result of PRN 19: Independent FLL (left) and Vector FLL (right).  
During the attenuation period, the correlation power level is very low, meaning that the attenuation causes a momentary blockage of the signal. For that period, neither independent DLL nor FLL could generate appropriate tracking control inputs. VDLL and VFLL, however, can continue generating control input and obtain pseudorange and range-rate measurements comparing with diverging measurements of ITLs. Verification of VTL estimation during the period can be identified by the continuous tracking after the attenuation period. As opposed to the independent tracking, VTL recovers correlation power instantly, ensuring that the implemented VTL works well and tracks the momentarily blocked signal successfully.
## 4 Experimental Results  
### Dataset and Experimental Environment  
BDD100K (Berkeley DeepDrive 100K) [53] is a large-scale, diverse autonomous driving video dataset released by the Berkeley Deep Driving Lab, University of California, Berkeley, USA, in 2018. The data were collected at different times of the day and night and in various weather conditions, covering multiple cities and various scene types, and including common targets such as vehicles, traffic signs, and pedestrians during driving. Compared with other autonomous driving datasets, BDD100K can better simulate real-world driving scenarios and provide richer background information for the training and evaluation of autonomous driving systems. To alleviate the problem of imbalanced number of labeled instances of different categories in the dataset, we adopted a stratified sampling strategy to construct the training subset: first, we completely retained the images of categories with fewer labeled instances (such as motor) to ensure their representativeness; then, we randomly sampled from the remaining data and finally constructed a balanced subset containing 40,000 images. The new dataset was randomly allocated in an 8:1:1 ratio, and after division, 32,000 training sets, 4000 validation sets, and 4000 test sets were obtained, covering nine common types of targets in driving scenarios, including pedestrians, cars, traffic lights, traffic signs, etc.  
The experimental environment was as follows: the operating system was Ubuntu 22.04.2 with a 64-bit operating system, the CPU was Intel(R) Xeon(R) Gold 6248R, the GPU was NVIDIA A100, the deep learning framework pytorch1.12.0, and python 3.8.19. We trained from scratch without using pre-trained weights. The training configuration was as follows: the input image size was 640 \(\times\) 640, the batch size was 80, the optimizer was SGD, the initial learning rate was 0.01, the weight decay coefficient was 0.0005, the momentum was 0.937, the training round was 200, and the random seed was 0.
# An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks
###### Abstract  
Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure system availability by increasing the lifetime of WSNs. Clustering is one of the routing techniques that benefits energy efficiency in WSNs. This paper provides an evolutionary clustering and routing method which is capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. The proposed method consists of two phases. First, we present a model with improved hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for cluster head (CH) selection. Second, we design a PSO-based multi-hop routing system with enhanced tree encoding and a modified data packet format. The simulation results for disaster scenarios prove the efficiency of the proposed method in comparison with the state-of-the-art approaches in terms of the overall residual energy, number of live nodes, network coverage, and the packet delivery ratio.  
Keywords:IoT networks; energy-efficient clustering and routing; disaster management; non-uniform distribution of events; evolutionary algorithms; tree encoding; forest fire +
Footnote †: journal: Sensors
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 4 Results and Discussion
### Evaluation Results
#### 4.2.2 Qualitative Results  
Figure 8 shows the predicted depth maps of the networks trained on the NYU-depth v2 dataset using an Eigen split [5]. The images were chosen for the visual inspection because they highlight the different strengths and weaknesses of the networks. All images were taken from the test set to assess the networks' ability to generalize to data they had never seen before. The results were also compared to different state-of-the-art methods. It can be seen in last column of the Figure 8 that our depth maps are clear with crisp boundaries.  
The maximum predicted depth values vary between networks, and each prediction has its own color scale. The colors in depth maps represent the predicted depth in meters, with red corresponding to large depth values and blue corresponding to small depth values. Each figure depicts the input image, the corresponding depth ground truth, and the prediction of each network.  
Figure 8: Comparison with other state-of-the-art methods. The first column is the RGB images, the second column is the ground truth, the third is [PERSON] et al. [32], the fourth column is [PERSON] and [PERSON] [9], and the last column shows our predictions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**High Is Better \(\uparrow\)**} & \multicolumn{3}{c}{**Low Is Better \(\downarrow\)**} \\ \cline{2-7}  & \(\delta_{1}\uparrow\) & \(\delta_{2}\uparrow\) & \(\delta_{3}\uparrow\) & \(Rel\downarrow\) & \(RMS\downarrow\) & \(Log10\downarrow\) \\ \hline [PERSON] et al. [5] & 0.769 & 0.950 & 0.988 & 0.158 & 0.641 & - \\ \hline [PERSON] et al. [27] & 0.811 & 0.953 & 0.988 & 0.127 & 0.573 & 0.055 \\ \hline [PERSON] et al. [32] & 0.828 & 0.965 & 0.992 & **0.115** & 0.509 & 0.051 \\ \hline [PERSON] and [PERSON] [9] & 0.846 & 0.974 & **0.994** & 0.123 & 0.465 & 0.053 \\ \hline
**Ours (scaled)** & **0.917** & **0.987** & 0.993 & 0.117 & **0.398** & **0.047** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative comparison of different state-of-the-art methods on the NYU-depth v2 dataset.  
We also tested our model on our random indoor scene images, and it shows quality results, as can be seen in Figure 9.
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 3 Data Simulation and Testing  
### Data Simulation  
In order to solve the T-L model, this paper simulated the magnetic interference generated by the UAV during flight according to a standardized flight method (FOM) designed by [PERSON] and used the corresponding compensation algorithm to solve the simulated data, calculate the corresponding 16 compensation coefficients, and apply them to another set of data simulated by the standardized flight method to test the generalization ability of the model.  
The FOM flight method is as follows: the aircraft flies sequentially in the order of north, east, south and west, each of which includes three \(\pm 5^{\circ}\) yaws, three \(\pm 5^{\circ}\) pitches and three \(\pm 10^{\circ}\) rolls, and the duration of each group of maneuvers is about 5-10 s, and 5 s of flat flight are interspersed between each group of maneuvers. The magnetic field in the flight area changes steadily, and in order to reduce the interference of shallow geological bodies on the aircraft, the flight altitude is generally set to 2000-3000 m.  
This paper simulated two different sets of FOM flight data, denoted as Flight A and Flight B. Assuming that the aircraft is compensated in strict accordance with the FOM flight method, the geomagnetic field \(T\) is 46,862 nT, the magnetization inclination angle is \(-1.298^{\circ}\), and the magnetization declination angle is \(36.663^{\circ}\), \(T_{gx}\), \(T_{gy}\) and \(T_{gz}\) can be calculated according to Formulas (8)-(10), and then the fluxgate three-component, \(T_{bx}\), \(T_{by}\) and \(T_{bz}\) can be calculated by correcting the obtained data according to Formulas (6) and (7). The three-axis fluxgate and the corresponding aeromagnetic interference model obtained by simulation are shown in Figures 5 and 6.
# Expectation vs. Reality: Attitudes Towards a Socially Assistive Robot in Cardiac Rehabilitation
## 2 Background
### Robot Therapy in Cardiac Rehabilitation
#### 2.2.2 Robot-Patient Interaction  
The interaction can be classified in three types: (1) There is a normal social interaction type, where the robot talks to the patient motivating and encouraging during the exercise, the robot uses its speech and body motions to express motivation and support. This interaction goes only in one direction, as the robot only talks to the patient without expecting any response. (2) The second type of interaction includes the GUI, where the robot performs a request that the patient must respond. In this case, the GUI enables a keyboard once the robot asked for this value. When the patient delivers the value, the keyboard is locked, and the GUI sends this information to the robot, which performs different behaviors considering the case (Table 2). Unlike the first mode of interaction, here there is a closed loop facilitated through the GUI having a bidirectional communication. Finally, (3) the third mode of interaction includes clinicians. In this case, the robot decides that clinicians must intervene in the therapy, where an alert has been detected due to an unexpected increase of the heart rate and the patient requires assistance by the clinicians to monitor their health condition. In this scenario, the patient is informed by the robot of the existence of an unusual condition and then clinicians are reported about the situation. The behaviors performed by the robot on each type of interaction are described in the Table 2.  
These interactions can potentially improve cardiac rehabilitation results as the patients receive motivation constantly and they are monitored each session, unlike the conventional rehabilitation. The emergency state allows the therapist to control the session according the physiological parameters and also prevent a risk event. Finally, it is expected that the robot increases the adherence to the program, taking into account that motivational and cognitive aspects are fundamental to achieve a comprehensive rehabilitation [24; 25].