esa-sceva/satcom-chunk-collection huggingface dataset:
# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 5 Sensor Technology in Construction Safety  
The data transmitted by sensors can either carry straightforward information such as coordinates, speed, angle, rotation speed, force, and temperature, or complex information such as sound, images, and videos. Complex information contains large amounts of hidden information, such as the relative position and movement, which can be extracted through some technology methods. Therefore, a literature review about sensor research should focus on both the information being extracted and the methods used to extract it. Among extraction methods, the most popular is based on computer vision. Computer vision is an interdisciplinary research field focusing on extracting information from the real world through images or videos. Through the acquisition, processing, and analysis of digital images, users are provided with the hidden information they need. In the field of construction safety, computer vision has been used to examine specific situations of the construction stage, such as tracking the location of on-site individuals, behaviour monitoring, and PPE checking. The application of sensors in construction safety effectively increases the automation degree of construction monitoring, which significantly reduces repetitive labour and increases work efficiency. The real-time data provided by the sensor also provide the information foundation for DT.
## 6 Concluding Remarks  
In this paper, we have proposed a novel ad hoc routing and relay architecture called RoCoMAR (Robots' Controllable Mobility Aided Routing) that uses robotic nodes' controllable mobility, in order to tackle the problem of low network performance and provide a desired end-to-end data transfer quality. RoCoMAR repeatedly performs link reinforcement process with the objective of maximizing the network throughput, where the link with lowest quality on the path is replaced with high quality links be placing a robotic node as a relay at an optimal position. Once placed as relay, the robotic node performs adaptive link maintenance by adjusting its position according to the movements of regular nodes. According to the simulation results, RoCoMAR outperforms existing ad hoc routing protocols for MSN in terms of network throughput and end-to-end delay.  
For future work, we plan to design an analytical model to estimate the speed of link reinforcement considering RNs' distribution and moving speed. We also intend to extend RoCoMAR to find and establish an optimal path in terms of energy consumption and throughput in a network that consists of multiple RN and regular nodes. The recovery of failure of RNs will also be considered. In addition, we also plan to investigate optimal positions for RNs considering communication parameters such as RSSI and SNR.
# Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties
## 4 Analysis of Examples
The third scenario has weak path uncertainty and strong demand uncertainty, \(\alpha_{ij}/\beta_{ij}=1.11\), \(d_{j}^{\xi,u}=200\). The fourth scenario is the scenario with strong path and demand uncertainties \(\alpha_{ij}/\beta_{ij}=1.35\), \(d_{j}^{\xi,u}=200\). When the dual uncertainty factors rise simultaneously, the increase in the total cost and time of mobile emergency response will be much higher than the result of a single-factor change. As shown in Figures 7-10, the differentiated scenarios and various stages in the mobile emergency process can be mapped according to the corresponding conclusions of the above systems. Different control parameters will lead to the model's preference in terms of cost and time minimization. On the premise of the same external conditions, the changes in demand factors produce more intuitive perturbations to the model results than the path factors. Therefore, when carrying out mobile emergency vehicle scheduling, human resources and material resources should be tilted to the demand points with substantial demand changes to ensure the balanced development of mobile emergency vehicle scheduling within the global scope. For the severely affected areas with high dual uncertainty factors, it is necessary to comprehensively plan the deployment of emergency resources to guarantee their timely arrival and prevent the surge in the total cost and time of local points from causing irreversible damage to the mobile emergency vehicle scheduling network. \begin{table}
\begin{tabular}{c c c c} \hline \(\gamma_{j}^{k,u}\) & \(d_{j}^{\xi,u}\) & **C/CNY** & **T/min** \\ \hline \multirow{2}{*}{0.1} & 100 & 5,675,028 & 1,672,230 \\  & 200 & 5,707,355 & 1,720,270 \\  & 100 & 5,714,462 & 1,713,100 \\  & 200 & 5,709,418 & 1,707,840 \\  & 100 & 5,722,309 & 1,718,520 \\  & 200 & 5,842,387 & 1,851,920 \\  & 100 & 5,763,942 & 1,761,510 \\  & 200 & 5,908,082 & 1,879,060 \\ \hline \end{tabular}
\end{table} 
Table 5: Mobile emergency costs and time for different demand disturbances. Figure 7: Variation of mobile emergency cost and time in first scenario.
## 3 Proposed Algorithm
### CIM-Based ART with Edge and Age
#### 3.3.2 Winner Node Selection  
Once a data point \(\mathbf{x}_{n}\) is presented to CAEA with the prototype node set \(\mathcal{Y}=\{\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{K}\}\), two nodes that have a similar state to the data point \(\mathbf{x}_{n}\) are selected, namely, winner nodes \(\mathbf{y}_{k_{1}}\) and \(\mathbf{y}_{k_{2}}\). The winner nodes are determined based on the state of the CIM as follows:  
\[k_{1}=\underset{\mathbf{y}_{i}\in\mathcal{Y}\setminus\{\mathbf{y}_{k_{1}}\}} {\text{min}}[\text{CIM}(\mathbf{x}_{n},\mathbf{y}_{i},\text{mean}(\mathcal{S} ))], \tag{10}\]  
\[k_{2}=\underset{\mathbf{y}_{i}\in\mathcal{Y}\setminus\{\mathbf{y}_{k_{1}}\}} {\text{min}}[\text{CIM}(\mathbf{x}_{n},\mathbf{y}_{i},\text{mean}(\mathcal{S} ))], \tag{11}\]  
where \(k_{1}\) and \(k_{2}\) denote the indexes of the 1 st and 2 nd winner nodes, i.e., \(\mathbf{y}_{k_{1}}\) and \(\mathbf{y}_{k_{2}}\), respectively. \(\mathcal{S}\) is the bandwidths of the Gaussian kernel in the CIM for each node.
# Numerical Simulation of Interaction between Plasma and Azithromycin Based on Molecular Dynamics
## 4 Statistics and Analysis of Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Incident** & **C-H** & **C-C** & **C=C** & **C=O** \\
**Plasma** & **Breaking Ratio (\%)** & **Breaking Ratio (\%)** & **Formation Ratio (\%)** & **Formation Ratio (\%)** \\ \hline O (10) & 3.89\% & 2.07\% & 3.79\% & 21.82\% \\ O (20) & 16.39\% & 4.48\% & 8.62\% & 40.00\% \\ O (30) & 29.72\% & 6.21\% & 15.52\% & 53.64\% \\ O (40) & 34.72\% & 7.93\% & 20.69\% & 60.91\% \\ O (50) & 38.61\% & 9.66\% & 27.24\% & 69.09\% \\ \hline OH (10) & 1.81\% & 1.38\% & 1.38\% & 5.45\% \\ OH (20) & 4.17\% & 2.41\% & 5.86\% & 14.55\% \\ OH (30) & 5.56\% & 3.45\% & 6.55\% & 25.45\% \\ OH (40) & 8.06\% & 5.17\% & 9.31\% & 42.73\% \\ OH (50) & 10.69\% & 6.55\% & 15.17\% & 52.73\% \\ \hline O\({}_{3}\) (10) & 7.08\% & 3.45\% & 5.52\% & 30.00\% \\ O\({}_{3}\) (20) & 15.69\% & 5.17\% & 10.00\% & 42.73\% \\ O\({}_{3}\) (30) & 19.86\% & 6.90\% & 12.07\% & 59.09\% \\ O\({}_{3}\) (40) & 22.36\% & 8.28\% & 16.55\% & 69.09\% \\ O\({}_{3}\) (50) & 28.61\% & 11.38\% & 21.72\% & 79.09\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The relationship between the changes of four key chemical bonds of azithromycin molecule and the number of active groups. The results in the table are the mean values from 20 independent simulations. Figure 14: Changes in chemical bonds in azithromycin molecules. (**a**): C-H-bond-breakage percentage varies with the number of reactive groups; (**b**): C-C-bond-breakage percentage varies with the number of reactive groups; (**c**): The rate of C=C bond formation varies with the number of reactive groups; (**d**): The rate of C=O bond formation varies with the number of reactive groups. The reason for forming the C=C bond and C=O bond is that the active particle abstracts the hydrogen atoms, and the carbon atoms produce the lone electron. The above analysis shows that the oxygen atoms have a more robust and specific ability to abstract the hydrogen atoms. More C=C bonds are generated after interacting with active-oxygen atoms, and the bond-formation rate reaches 27.24% when the number of oxygen atoms is the largest. The ozone will be decomposed into oxygen and oxygen atoms, and oxygen will oxidize the hydroxyl groups to form C=O bonds, so the C=O double-bonds-bonding rate can reach 79.09%. The number of C=O and C=C bonds formed in the azithromycin molecule which has reacted with the hydroxyl group is the least, and the bonding rate is only 15.17% and 52.73% when the number of hydroxyl groups is the largest.
## 2 Radar Jamming Signal Analysis and Modeling
### Deception Jamming
#### 2.3.1 Comb Spectrum Jamming  
Linear frequency modulated (LFM) radar's comb spectrum jamming (CSJ) mainly generates jamming through the product modulation of comb spectrum signals and linear frequency modulation signals. Comb spectrum jamming can generate a series of dense comb teeth in the frequency domain, which has the effect of deception or suppression. The expression of the comb spectrum signal is as follows:  
\[\text{comb}(t)=\sum_{i=1}^{M}a_{i}e^{i2\pi f_{i}t} \tag{6}\]  
where \(f_{i}\) corresponds to the frequency point where each comb tooth appears, \(a_{i}\) is the amplitude at the corresponding \(i^{th}\) frequency point.The mathematical model of LFM radar comb spectrum jamming is as follows:  
\[J(t)=s(t)\cdot\text{comb}(t)=\text{rect}\bigg{(}\frac{t}{T}\bigg{)}\sum_{i=1} ^{M}a_{i}e^{i2\pi\big{[}(f_{0}+f_{i})t+\frac{\kappa}{2}t^{2}\big{]}} \tag{7}\]  
where \(f_{0}\) is the initial frequency, t is the time variable, \(K=\frac{B}{T}\) is the frequency modulation slope, \(B\) is the bandwidth, \(T\) is the pulse width of the signal.
# An Investigation of the Temperature-Drift Effect on Strain Measurement of Concrete Beams
## 4 Residual Strain Monitoring Test for Nonelastic Response
The preliminary test in this section already proved the proposed concept: given that temperature changes are slow and mechanical strain changes are momentary, application of adjacent data subtraction to mechanical strain data can eliminate the effects of temperature drift. In addition to accurately analyzing the mechanical strains of load application and removal, this type of concept could also be used to analyze the residual strain of the nonelastic response of the concrete beam generated by the application of substantial short-term loads.
## 3 Drone Routing Algorithms
### Trajectory Planning
#### 3.1.3 Flying Sidekick Traveling Salesman Problem with Stochastic Travel Time  
(FSTSP-STT)  
The concept of truck-drone pair has been on the rising tide to provide logistics support for the last mile. In [41], authors proposed the (FSTSP-STT) scheme to address the parcel delivery problem as a modified FSTSP, which is similar to the vehicular routing problem (VRP), and formulated it using Markov decision process (MDP) by considering the stochasticity of the travel time. The proposed formulated scheme is answered by using deep Q-networks (DQN), which combines reinforcement learning (RL) and deep neural networks, and further refined by the attention mechanism. Moreover, the actor-critic (A2C) network was used to address the problem of dimensionality, which is generally observed in machine learning techniques. This scheme tries to address the fact that the DRL based algorithm performs better than traditional heuristic approaches.
# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
## 2 LoRa and LoRaWAN  
LoRa is a physical layer with the Chirp Spread Spectrum (CSS) modulation technique operated by Semtech and usable within the license-free spectrum from 863 MHz to 870 MHz in Europe and from 902 MHz to 928 MHz in the USA [13]. The connection of the wide-area network of LoRa is called LoRaWAN and is a network protocol stack and offers the architecture of LoRa technology on the MAC layer. A LoRaWAN network is made up of LoRa end devices connected in a star topology that sends information to one or more LoRaWAN gateways, which, in turn, send the received message along with a recordedunique message's metadata information to a network server, as shown in Figure 1. This metadata information is used for localization services in LoRaWAN networks, whereby gateways serve as anchor points to predict end device locations. LoRaWAN networks use RSSI values as a critical metric for fingerprint localization algorithms, whereas TDoA algorithms use timestamps at which gateways receive the same message. Furthermore, localization accuracy of any of the methods depends on more gateways receiving the same message [14].
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 1 Introduction
### Motivation  
To deal with selfishness (or packet dropping misbehavior), cooperation enforcement schemes have been proposed in [20; 21]. Among those schemes, reputation-based schemes are considered more promising and scalable due to their distributed nature and suitability for ad hoc networks. However, there are three key problems in the existing reputation-based schemes that motivate us to contribute to this work:  
* How to detect malicious nodes that evade the detection process? Nodes that do not participate in route discovery (i.e., dropping route request packets) cannot be included in the path construction process; therefore, they are not recorded in any routing path; as a result, they are not acting as forwarders or contributors, rather always acting as the traffic sources, i.e., the network service consumers. Reputation-based systems monitor the packet forwarding behavior only while these nodes maliciously excluded themselves from the routing paths; hence, they go undetected [14; 20].
* How to capture nodes that intentionally select bad locations? Some malicious nodes strategically position themselves in the network, such as at corners or network boundaries, so that they always act as source nodes; in other words, they are never chosen as forwarders [20; 22].
* How to prevent nodes from exploiting reputation thresholds? As soon as a node gains substantial reputation value, it starts dropping packets again for a specific time, i.e., nodes can contribute less and consume more without being detected. These sorts of attackers are partial droppers.  
Unlike the existing works, where the reputation of a node was only monitored by its neighbors, involving the source node and forwarding nodes in the reputation calculation process and keeping track of the node's consumption can mitigate these problems. The specific contributions of this work are listed below.
## 3 Results  
### Datasets and Experimental Setting  
The proposed network was validated with two public datasets provided by The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) [29] and Mayo Clinic for the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge [30].  
Both datasets consist of 2000 images of the lungs from ten patients (200 images per patient). CT images are squares of size 512. The same data pre-processing method was applied to the two datasets. Full-view sinogram domain data were derived from full-view CT images by the forward projection procedure [31], which can be implemented using the Matlab 2020a function \(fanbeam\). The size of the full-view sinograms obtained by the fan-beam projection is \(605\times 360\), i.e., 605 detectors with 360 projection views (sampling interval = 1 degree). The sparse-view sinograms for sparse levels \(S=2,4,8,15\) were obtained by downsampling the full-view sinograms in the view dimension with the corresponding sparse levels. For each dataset, 1800 samples were used to train the networks and the remaining 200 samples were used for testing. Figure 3a,b shows the samples from both datasets, which are from left to right, original (normal dose) CT images, full-view sinograms and sparse-view sinograms with various sparse levels (\(S=2,4,8,15\)).  
The proposed network was compared with a linear interpolation method and two sinogram domain baseline networks: Res-Unet [24] and SDS-net [13]. Res-Unet has a classical network structure, and was used in a sinogram domain inpainting network in [24]. SDS-net is the sinogram domain subnetwork from the end-to-end network proposed in [13]. Both Res-Unet and SDS-net have a pre-linear-interpolation layer, and the interpolated full-view sinograms are then input to the sinogram domain network.  
The hyperparameters for the baseline networks were set as described in their original papers. In the CT image domain, both the PSNR and the Structural Similarity (SSIM) metrics were calculated to quantitatively evaluate the quality of the reconstructed CT images, while in the sinogram domain, only the PSNR was calculated to quantitatively evaluate the difference between the estimated sinogram and the full-dose sinogram. This is because the estimated sinogram is an intermediate result in the CT reconstruction process, and is not visually inspected by humans and thus is not suitable for judging with a perception-based similarity metric such as SSIM.  
In addition, the effects of different loss functions (sinogram domain loss \(l_{sine}\) and image domain loss \(l_{image}\)) on the performance of the sinogram domain interpolation model were compared.  
All experiments were performed with a Tensorflow 2.3 @ Intel Xeon Gold 6154 CPU and NVIDIA GeForce RTX 3090 GPU. All networks were trained with the Adam algorithm.
###### Abstract  
A combination of drought and high temperatures (\"global-change-type drought\") is projected to become increasingly common in Mediterranean climate regions. Recently, Southern California has experienced record-breaking high temperatures coupled with significant precipitation deficits, which provides opportunities to investigate the impacts of high temperatures on the drought sensitivity of Mediterranean climate vegetation. Responses of different vegetation types to drought are quantified using the Moderate Resolution Imaging Spectroradiometer (MODIS) data for the period 2000-2017. The contrasting responses of the vegetation types to drought are captured by the correlation and regression coefficients between Normalized Difference Vegetation Index (NDVI) anomalies and the Palmer Drought Severity Index (PDSI). A novel bootstrapping regression approach is used to decompose the relationships between the vegetation sensitivity (NDVI-PDSI regression slopes) and the principle climate factors (temperature and precipitation) associated with the drought. Significantly increased sensitivity to drought in warmer locations indicates the important role of temperature in exacerbating vulnerability; however, spatial precipitation variations do not demonstrate significant effects in modulating drought sensitivity. Based on annual NDVI response, chaparral is the most vulnerable community to warming, which will probably be severely affected by hotter droughts in the future. Drought sensitivity of coastal sage scrub (CSS) is also shown to be very responsive to warming in fall and winter. Grassland and developed land will likely be less affected by this warming. The sensitivity of the overall vegetation to temperature increases is particularly concerning, as it is the variable that has had the strongest secular trend in recent decades, which is expected to continue or strengthen in the future. Increased temperatures will probably alter vegetation distribution, as well as possibly increase annual grassland cover, and decrease the extent and ecological services provided by perennial woody Mediterranean climate ecosystems as well.  
drought; Mediterranean-type ecosystems; NDVI; PDSI; Southern California; climate change 1
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 4 Proposed Fuzzy Logic Dijkstra-based Routing Algorithm
### Data Transmission Phase
Step IV: Compare the link weights of each neighbor node, select the link with the minimum weight value, and update set \(SL\) with the node and link information having the least link cost. Step V: Repeat steps II to IV with the selected least link cost neighbor node until the final node is reached. The flowchart of the proposed algorithm is presented in Figure 5.
## 3 Results and Discussion
### Maximum Displacements and Von Mises Stresses of All Three Risers
From Figure 11, for all cases riser 3 had the maximum von Mises stresses and riser 2 had the minimum von Mises stresses. Here we have to note that, only the von Mises stresses for steel riser (riser 1) could be utilized to verify the failure, more specifically, the 67% of the yield stress of X80 steel (371 MPa) was identified as the allowable stress according to American Bureau of Shipping (ABS) standard [45]. Therefore, for all cases, steel risers would not incur stress failure. The minimum factors of safety, which are defined as allowable strength/stress, were 82.4, 31.6 and 12.1 for steel riser 1 with current velocities of 0.36 m/s, 1.22 m/s and 2.13 m/s, respectively. However, the von Mises stresses for FRP composite risers (risers 2 and 3) could not be utilized to verify the failure because the global stresses could not represent the stress distributions in each composite laminae. If the equivalent stresses of a composite riser is required, the effective modulus method (EMM) rather than the layered-structure method (LSM) should be utilized [19]. In this paper, the global stress distributions for FRP composite risers here were only used to find the locations and time of the maximum stresses occurred, and this information was used for failure verification of FRP composite risers in the next section. For all cases, the maximum von Mises stresses occurred at the top/bottom end of all risers, followed by the middle parts (above the middle point) of the risers, while at about \(-19\) m, the minimum von Mises stresses happened. The reasons for this trend were that the fixed support at the bottom lead to the maximum stresses and the middle parts had the largest deformation attributed to large stresses as well. For the small velocity situation, top-tension force lead to the maximum von Mises stress at the top end, especially for the steel riser whose tension force was the largest. For the location around \(-19\) m, all risers had comparatively small deformation and it was relatively far from the fixed support, therefore, minimum von Mises stresses occurred. Based on Figure 11 and the procedure of creating it, the maximum von Mises stresses for risers 1, 2 and 3 for 0.36 m/s were 4.5 MPa at 0 m (72.6 s), 3.0 MPa at \(-25\) m (30.6 s) and 5.7 MPa at \(-7\) m (24.6 s), respectively. The maximum von Mises stresses for risers 1, 2 and 3 for 1.22 m/s were 11.7 MPa at \(-25\) m (71.0 s), 11.3 MPa at \(-25\) m (52.6 s) and 22.7 MPa at \(-25\) m (68.0 s), respectively. The maximum von Mises stresses for risers 1, 2 and 3 for 2.13 m/s were 30.7 MPa at \(-25\) m (46.6 s), 29.4 MPa at \(-25\) m (39.8 s) and 59.2 MPa at \(-25\) m (54.4 s), respectively. According to the time when the maximum von Mises stresses occurred for all cases, Figure 12 presents the stress distributions of all risers at those specified time. Figure 12: _Cont._