esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR  
### Real-Time Systems  
The intuition behind the real-time system operates in accordance with real-world time constraints and requirements such as that the response time should meet some specified restriction of time or systems that adhere to certain deadlines. When considering timing constraints, different types of real-time systems can be identified such as hard real-time systems, soft real-time systems, and firm real-time systems. A hard real-time system is a system characterized by severe time requirements when missing deadlines might have devastating effects. Tardiness, or the delay in performing tasks relative to their deadlines, reduces the utilization of system outcomes. Examples of systems in this category include automated driving systems and avionics systems. Apart from the hard real-time system, soft real-time systems are a well-known method that utilizes real-time systems. This sort of system may occasionally fail to meet its deadline with an acceptable degree of probability. There are no dire penalties for missing the deadline. Examples of systems in this category include streaming media systems, interactive video games, and web servers. Real-time SR also falls into this category. Apart from soft real-time systems, firm real-time systems are a popular method that utilizes real-time systems. The type of systems fall in between hard and soft real time. Missing a deadline is bearable in solid real-time systems, but the utility of the output declines with delay. Systems that fall into this category include telecommunications switching systems, real-time financial trading systems, and manufacturing control systems. Figure 4 demonstrates the relationship between the real-time systems.  
Real-time SR is classified within the category of soft real-time systems. Real-time SR algorithms have the capacity to provide improved digital frames within the required period. These strategies can handle minor delays without causing substantial harm. This compromise enables satisfying time constraints while maintaining image resolution.  
Figure 4: Relationship between the real-time systems based on timing constraints.
## 4 Proposed LOADng Enhancement for IoT Networks
### Internet Route Cache for LOADng-IoT
Please note that at this point, as occurs in the normal processing of RREQ-IoT, the destination of the unicasted RREQ-IoT can be changed and the message can be redirected to another node with an Internet connection. This situation is exemplified in Figure 4. Consider that node \(A\) needs to send an Internet message and does not find an Internet route on its Routing Set; \(A\) checks its IRC and finds an entry to the IN \(E\) with next hop \(B\). Thus, \(A\) generates an RREQ-IoT with a destination equal to the \(E\) address and unicasts it to node \(B\) (the found next hop to \(E\)). \(B\) receives the RREQ-IoT from \(A\) and realizes its processing normally. However, \(B\), at the moment of checking its Routing Set, finds an Internet route to node \(C\). Thus, \(B\) changes the received RREQ-IoT destination to \(C\) and forwards the message in unicast. Node \(C\) then receives the request from \(A\) and sends a reply offering the required Internet route. This behavior is accepted because the intention of an RREQ-IoT is to reach a route to the Internet, independent of the IN providing the connection. In addition, this redirection of the RREQ-IoT ensures that the Internet route discovery process follows with most recent information (considering that the information provided by the Routing Set is frequently newer than the information provided by the IRC). However, if an Internet route is not found in the Routing Set of the intermediate node,the intermediate node should verify its IRC, change the message destination (if necessary), and then forward the message in unicast to an IN that is able to provide an Internet connection to the RREQ-IoT originator. Finally, whether the IRC set is empty, the node should change the destination address of the RREQ-IoT to the same address as its originator and send the message in broadcast. This process \"converts\" the RREQ-IoT received in unicast in a normal RREQ-IoT to be broadcasted and the Internet route discovery process can continue until an IN is reached. The use of the IRC mechanism allows the nodes to reduce the number of control messages required to construct an Internet route. The cache mechanism is used to direct the RREQ-IoTs to a previously known IN. Thus, when adopted, the IRC contributes to the reduction of the number of packet collisions, minimizes energy consumption, and improves network efficiency. As explained, the entries in the IRC set are only removed by lack of memory or the reception of an RERR message. This process is discussed in the description of the new error code proposed in this work in Section 4.6.
# Acceleration Techniques for Teeth Movements in Extractive Orthodontic Therapy
## 4 Discussion
### Low-Level Laser Therapy (LLLT)
When LLLT photonic radiation enters the cell nucleus, it increases the production of ribonuclecic acid (RNA), deoxyribonucleic acid (DNA), and protein synthesis. Enhancing the inflammatory response to specific stimuli produces biostimulating effects on the cellular metabolic processes [41]. Inflammatory responses mediate orthodontic movement; some research has sought to identify the primary inflammatory players responsible for increased OTM following LLLT. With the use of the GaAlAs diode laser, [PERSON]'s (2018) study intended to measure and analyse the levels of IL-1\(\beta\) in gingival reticular fluid during OTM [61]. In the split-mouth experiment, the canine retraction on the irradiation side progressed faster than on the C Side. Crevicular gingival fluid samples were examined using ELISA assays, and it was discovered that the amount of IL-1\(\upbeta\) was more significant in the laser-irradiated region. The scientists hypothesised that the dental acceleration was likely caused by the increased IL-1\(\upbeta\) levels induced by LLLT [61]. In contrast, in a previous study, interleukin-6 was analysed as potentially responsible for the acceleration of OTM after LLLT [63]. Patients underwent maxillary canine retraction to close the extraction space, in which only one side had LLLT applied. During orthodontic treatment, gingival circular fluid samples were taken to analyse the IL-6 levels. The results showed that LLLT indeed accelerated OTM. However, the difference in the mean IL-6 concentration was not statistically significant, and it was impossible to attribute a major role to this cytokine [63]. LLLT is a non-invasive approach with potential benefits, including increased angiogenesis, osteoblast/osteoclast activity, and analgesic effects. Studies have explored LLLT's impact on Orthodontic Tooth Movement (OTM), indicating accelerated movement. However, challenges exist, including varying outcomes, precision demands, cost, availability, treatment duration, and frequency. Notably, LLLT's mechanism involves cellular responses and cytokine modulation, yet cytokine roles remain debated. While LLLT holds promise for enhancing OTM, carefully considering its advantages, disadvantages, patient preferences, and further research is essential for informed treatment decisions in orthodontic practice [80].
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 4 The Trip Distribution Forecasting Model Based on Trip Chains  
Based on the trip chain method proposed to analyze the distribution of tourists, this section uses the Entropy-Maximizing theory to model the probability of tourists' trip distribution. Two types of trip chain constraints, i.e., the visiting volume constraint of stay points and the travel distance constraint, were defined based on the characteristics of trip chains. Then, a trip chain-based trip distribution model was built, along with the corresponding model-solving algorithm.
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 7 Conclusions  
This study aimed to assess the potential of IRT as a complementary (or alternative) approach to identify the presence of cavities in a certain type of culvert. Empirical studies in a laboratory setup showed the efficacy of IRT for detecting cavities even of small sizes (a few centimeters). The size and location of cavities are among the aspects that can be extracted from analyzing thermograms. In examining the real specimen, we faced some difficulties including the emissivity issue, which we could effectively deal with by coating. Although this study showed a promising future for the application of IRT in culvert inspection, further research is necessary. In particular, it is essential to verify the power of the technique in real setups despite logistic and technical difficulties of the on-site evaluation. Another direction that future research should consider is the fine tuning of the experimental setup and parameters as discussed in Section 6.  
D.K.: Conceptualization, Methodology, Formal Analysis, Experimental Setup and Data Acquisition, Validation, Software, Writing (original, review and editing); S.E.: Conceptualization, Simulation, Literature Review, Writing (literature review); R.B.T.: Conceptualization, Methodology (statistical method for cavity identification), Software (section statistical method for cavity identification); F.A.M.: Conceptualization, Simulation; Y.B.: Conceptualization, Theoretical Basis; A.M.: Conceptualization, Methodology (section statistical method for cavity identification), Software (section statistical method for cavity identification); X.M.: Conceptualization, Supervision, Project Administration, Resources, Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC)--Discovery Grant Program, the Canadian Foundation for Innovation (CFI) and Canada Research Chair Program (MIVIM Multipolar Infrared Vision Infrarouge Multipolaire).  
Data Availability Statement: Data sharing not applicable.  
We would like to express our gratitude for the precious support of [PERSON] and his colleagues from Ministere des transports du Quebec. The authors are also grateful to [PERSON] for his valuable advice during experiments and thank [PERSON] for his kind help with preparation for the experiment setup.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ACT & Absolute thermal contrast \\ APT & Absolute phase contrast \\ EOF & Empirical orthogonal function \\ LPT & Long pulse thermography \\ LT & Lock-in thermography \\ IRT & Infrared thermography \\ NDT & nondestructive testing \\ PCA & Principal components analysis \\ PCT & Principal components thermography \\ PT & Pulse thermography \\ PPT & Pulsed phase thermography \\ \end{tabular}
## 5 Investigation of Reinforcement Design Scheme  
Anchor cables were used to reinforce the slope. The anchor cables we used were 64 m in length and inclined downward 20\({}^{\circ}\). A total of 16 anchor cables were mounted on the 2D section. Compared with the actual spacing of the anchor cables, the ultimate tensile force for each anchor cable was equivalently 1500 t in the 2D section simulation.  
After applying the anchor cables, the rock mass of the slope became stable, as shown in Figure 11. The simulated internal force of each anchor cable is shown in Figure 12, where the anchor cables are numbered in sequence from the bottom to the top. The internal forces of the fourth and eighth anchor cables are the largest, and the internal force is larger in the middle and lower parts of the slope, whereas the forces of anchor cables in the upper part of the slope is very small. As a consequence, there is an urgent need to strengthen the support in the middle and lower parts of the slope, and the length and pre-stress of the anchor cables in the upper part of the slope can be properly reduced.  
Figure 11: The slope becomes stable after applying anchor cables. (**a**) Numerical simulation diagram; (**b**) Live pictures.  
Figure 12: Distribution of anchoring force.  
The stability of a slope is not only assessed based on gravity loading but also on earthquake loading. Hence, we further analyzed the stability of the anchored slope considering earthquake action. A real-time seismic recording curve of a magnitude 9.0 earthquake in a certain area was used in our simulation, as shown in Figure 13. By halving or doubling the acceleration factor (the ratio of recorded seismic acceleration to gravity acceleration) in the curve, a magnitude 8.0 or 10.0 earthquake was obtained.  
Under a magnitude 8 earthquake, the internal force of the anchor cables in the middle and lower parts exceeds their ultimate tensile force and the anchor cables break down one after another, finally leading to slope failure. This slope failure process is shown in Figure 14.  
Figure 14: The failure process under a magnitude 8 earthquake.  
Figure 13: Measured seismic acceleration curve in a certain area.  
Based on the above analysis results, the reinforcement scheme was then adjusted: the length of the anchor cables in the upper part of the slope was reduced, the anchoring force was reduced to be 1/3 of the previous designated value, and the quantity of the anchor cable was doubled in the middle and lower parts. Figure 15 shows the final failure state of the slope after the reinforcement scheme was adjusted. Under these conditions, the anchored rock masses are totally stable under a magnitude 7 earthquake, with two anchor cables breaking down in the middle part of the slope. Under a magnitude 8 earthquake, six anchor cables break down one after another when the earthquake lasts for 30 s, and the slope will fail overall; however, there was no evidence of a collapse.
# A New Method for Haul Road Design in Open-Pit Mines to Support Efficient Truck Haulage Operations
###### Abstract  
The design of a haul road for an open-pit mine can significantly affect the cost associated with hauling ore and waste to the surface. This study proposes a new method for haul road design in open-pit mines to support efficient truck haulage operations. The road layout in open-pit mines was optimized by using raster-based least-cost path analysis, and the resulting zigzag road sections were simplified by applying the Douglas-Peucker algorithm. In addition, the road layout was modified by reflecting the radius of curvature suggested in the road design guides. Finally, a three-dimensional model reflecting the results of the road design was created by combining the road layout modification result with the slope of the open-pit mine and the bench design result. The application of the proposed method to an area containing gold deposits made it possible to design a haul road for open-pit mines such that it supported efficient truck haulage operations; furthermore, the time required for truck movement along the road could be estimated. The proposed method is expected to be useful for planning and designing open-pit mines and to facilitate the improvement of the road design function of existing mining software applications.  
Keywords:haul road design; open-pit mine; least-cost path analysis; douglas-peucker algorithm; truck haulage operation +
Footnote †: journal: Applied Sciences
# Coupling Efficiency of a Partially Coherent Radially Polarized Vortex Beam into a Single-Mode Fiber
## 1 Introduction
One can choose suitable spatial coherence and topological charge to obtain optimal results for different applications. We believe that it has important applications in free-space optical communication.
## 1 Introduction
### Aim and Scope  
In this work, a new low-temperature encapsulation method, based on the use of EVA, has been developed to protect PSCs from UV irradiation and environment humidity. In order to analyze it, a research procedure has been carried out in three stages. The first one consists of the fabrication of four sets of samples: one composed of non encapsulated perovskite, one composed of non-encapsulated perovskite and ETL, one composed of encapsulated perovskite and one composed of encapsulated perovskite and ETL. In the second stage, all the samples were exposed to UV irradiation and full solar irradiation in humid environments. In the third and last stage, the samples were characterized in order to infer their degree of degradation.
## 2 Related Works  
### He-Aac  
HE-AAC is an extension of low complexity AAC (AAC-LC), which provided an optimization for those applications of low bit rate like streaming audio. It has the characteristics of standardization and has become a configuration file for the MPEG-4 audio standard. Two versions of HE-AAC are available: HE-AAC v1 and HE-AAC v2. HE-AAC v1 includes two principal technologies, spectral band replication (SBR) and AAC-LC. In contrast to version 1, version 2 additionally uses a technique named parametric stereo (PS) that compresses stereo signals more effectively. Figure 1 shows the HE-AAC techniques family.  
AAC-LC is a commonly used audio compress codec. It removes the audio signal by utilizing a psychoacoustic model and only keep auditory information. Though it has good audio quality at a bit rate of 128 kbps for mono audio, the audio quality begins to significantly decline below this bit rate. In order to achieve good audio quality as well as a low compression bit rate, two complementary technologies, SBR and PS, are exploited.  
SBR can improve compression efficiency in the frequency domain. At low frequencies, there is a lot of important audio information. SBR utilizes the strong relevance between high frequency and low frequency audio signals to reconstruct the high frequency signals through approximation as well as the transposition of low frequency signals rather than transfer the data of high frequency audio.  
Figure 1: HE-AAC techniques  
The compression efficiency of stereo signal is improved by PS. PS technology mixes stereo signals downward into mono-channel signals, extracts parametric stereo data, and describes the differences as well as similarities of two channels. When decoding, the original stereo signal is reconstructed using parametric stereo data and a mono-channel signal.
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 4 Results
### Comparative Analysis of Other Classifiers  
Table 2 and Figure 11 report the performance of our SafeNet model with five other benchmarking classifiers for DataSet 03. The performance of the existing methods ranged from F1 = 0.742 to 0.846 and MCC = 0.450 to 0.717. However, SafeNet had the best performance, improving MCC by 8.6% over that of the next-best DNN model. Figure 11A-C compares the ROC curves obtained for the SafeNet model with those of the five other classifiers, and SafeNet again demonstrated the best performance with a 4.6% improvement in AUC for class 0, a 1.7% improvement for class 1, and a 7.1% improvement for class 2 over the second-best DNN model.  
To further confirm the performance of SafeNet, ternary probability diagrams and a confusion matrix were used to indicate the distribution of the predicted and true values and to allow for more profound insight into the classification performance of the model. Figure 12 shows the ternary probability diagrams and confusion matrix for the three classes obtained from the SafeNet model. Ternary probability diagrams allow for a qualitative evaluation of classification results. From Figure 12A-C as a whole, most of the samples in class 0 and class 1 were correctly predicted, and classes predicted by SafeNet could be classified into the correct classes, while the performance of class 2 was slightly worse, with some samples having a prediction probability concentrated around 0.5. The confusion matrices shown in Figure 12D quantitatively present the prediction accuracy for each class, with the correct prediction accuracy for class 0, class 1, and class 2 of 89.5%, 85.5%, and 87.9%, respectively. In general, SafeNet provides a good classification performance for identifying seismic signals and space weather events from Swarm satellite data.  
Figure 10: The ROC curves comparing model performance for (**A**) class 0, (**B**) class 1, and (**C**) class 2 of datasets with the positive to negative ratio of 1:1 (DataSet 3), 1:2 (DataSet 14), 1:3 (DataSet 15), 1:4 (DataSet 16), and 1:5 (DataSet 17). (**D**) Bar graphs displaying the MCC, F1 score, and accuracy.  
Figure 11: The ROC curves comparing the proposed method, gradient boosting machine (GBM), deep neural network (DNN), random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM) models for (**A**) class 0, (**B**) class 1, and (**C**) class 2. (**D**) The MCC, F1 score, and accuracy bar plot curves.
# Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution
## 4 Experimental and Analysis
### Ablation Experiment
#### 4.3.2 Verification of the Effectiveness of Structure Prior  
To verify the validity of the structure prior, we also visualize the output of the gradient branch, as shown in Figure 6. For a given original HR image (as shown in Figure 6a), the corresponding edge gradient map is sharp and clear (as shown in Figure 6b). However, when we observe the gradient map corresponding to the LR image (as shown in Figure 6c), it can be found that its edges are relatively rough. While, from the gradient map obtained by our GFSR (as shown in Figure 6d), we can see that the gradient branch effectively restores the structural information of the building, which is accurate and finer. This is because the gradient map is able to be used as a structure prior to provide rich structural information for the super-resolution process on the trunk branch, thereby effectively restoring more abundant and more accurate structure, edge and texture details.  
Figure 7 presents the comparison of the reconstructed images obtained by the models without or with the gradient branch. As can be seen from Figure 7, compared with the model without the gradient branch, the details of the reconstructed image obtained by the model with the gradient branch are closer to that of the reference image. Specifically, the edges and the textures around the toys are sharper and clearer in the reconstructed images obtained by the model with the gradient branch. In contrast, the corresponding edges and the textures are blurred in the reconstructed images obtained by the model without the gradient branch. Consequently, it can be seen that the gradient branch facilitates the model to reconstruct more abundant and accurate edge and texture details.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Single Scale** & \
\begin{tabular}{c} **Multi-Scale** \\ **Convolution w/o** \\ **RRFB** \\ \end{tabular} &
\begin{tabular}{c} **Multi-Scale** \\ **Convolution** \\ **Convolution** \\ \end{tabular} \\ \hline PSNR & 32.97 & 33.00 & 33.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effects of different convolution units. We report the best average PSNR(dB) on Set14 (\(\times 2\)) in \(2.5\times 10^{4}\) iterations.  
Figure 6: Visualization of the feature map of Img_001 (Urban100).