esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Estimation and Reliability Calculation of Road Travel Time Distribution after Earthquake
### Impact Factors of Earthquake Damage of Path Travel Time
#### 3.2.2 Calculation of Earthquake Damage Influencing Factors  
The speed \(V(c)\) under the ideal capacity of any road is obtained by fitting method. The fitting result is:  
\[V(c)=exp\left(1.9\times 10^{-3}c+0.477\right) \tag{15}\]  
Type \(c\) is the ideal capacity of the road section (pcu/h/lane).  
For a certain road section \(k\), the error between the theoretical travel time after the earthquake and the normal travel time is expressed as:  
\[d=max\{\frac{\frac{l_{k}}{V(Q_{3})}-\frac{l_{k}}{V(C^{r})}}{l_{k}},0\} \tag{16}\]  
where:  
\(l_{k}\) is the length of the road section.  
\(V\left(\cdot\right)\) is the recommended speed value corresponding to the ideal capacity, \(Q_{3}\) is given by Formula (14).  
\(C^{r}\) is the road capacity under normal conditions.  
Assuming that \(V(C^{r})<V(Q_{3})\) is constant, after finishing, the Formula (16) is obtained:  
\[d=\frac{1}{V(Q_{3})}-\frac{1}{V(C^{r})} \tag{17}\]  
Transform Equation (16), and define the seismic impact factor that changes in the interval (0,1) as \(\Psi_{b}\):  
\[\Psi_{b}=exp\left(-\frac{\sqrt{d}}{\theta_{2}}\right) \tag{18}\]The Formula (18) is substituted into the Formula (17) to obtain:  
\[\Psi_{b}=exp\left(-\frac{1}{\theta_{2}}\sqrt{\frac{1}{V(Q_{3})}-\frac{1}{V(C^{{}^{ \prime}})}}\right) \tag{19}\]  
where:  
\(\theta_{2}\) is a control parameter greater than zero, \(\theta_{2}\) is \(\
icefrac{{s}}{{m}}\), and it is obtained by substituting \(V\left(\cdot\right)\) and \(Q_{3}\left(\cdot\right)\).  
\[\Psi_{b}=exp\left(-\frac{1}{\theta_{2}}\sqrt{\frac{1}{exp\left[1.756\times 10^{ -3}\times(1-ind^{2})\times C^{{}^{\prime}}+0.477\right]}-\frac{1}{V(C^{{}^{ \prime}})}}\right) \tag{20}\]  
Figure 4 shows the \(\Psi_{b}\) curves corresponding to different earthquake damage indexes ind under a given \(\theta=0.3\).
## 2 Theoretical Background
### Guided Wave Based Defect Detection in Complex Structures  
UGWs have been used to inspect plate and pipe structures for decades. These structures have a well-defined yet simple and uniform cross-section, and the principles for generating pure wave modes are well established [20; 21]. Pipes are used to transport fluids, and their hollow cylindrical geometry allows UGWs to form an efficient waveguide for longitudinal, flexural and torsional wave modes [22; 23].  
UGWs have also been applied to pipes with non-accessible areas, such as buried and coated pipes. [PERSON] et al. investigated the attenuation of T(0,1) mode in a coal-tar-enamel-coated pipe over a frequency range of 5 to 30 kHz and up to a soil cover of 1.7 m depth [24]. [PERSON] et al. experimentally studied the guided wave propagation and attenuation in an 8 inch schedule-40 carbon steel pipe buried in the sand over a frequency range of 11 to 34 kHz [25]. [PERSON] et al. investigated the attenuation of T(0,1) mode in petrolatum-anticorrosion-gree-coated steel pipe over a 20 to 40 \({}^{\circ}\)C temperature range [26].  
Guided waves have also been employed for other complex structures, such as rails, wire rope, composite structures and commercial shipping containers. These structures have irregular cross-sectional areas of their profiles. There are many challenges faced in implementing guided waves for complex structures, such as poor signal-to-noise ratio (SNR), high attenuation, poor defect sensitivity and defect localization. Many researchers have proposed signal processing techniques to overcome these challenges. [PERSON] et al. demonstrated the use of UGW torsional mode in the non-dispersive region to detect defects in a section of rail [13]. [PERSON] et al. demonstrated a rail inspection prototype based on UGWs and non-contact probing combined with a distinct signal processing algorithm [27]. [PERSON] et al. applied UGWs on steel wire ropes made of a polymer core and encapsulated by twisted wires [28]. Application of band-pass filtering and discrete wavelet transform on  
Figure 4: Group velocity dispersion curves for U-pile.  
Figure 3: Phase velocity dispersion curves for U-pile.  
the received signals significantly improved the defect sensitivity and SNR. [PERSON] et al. illustrated the use of the split-spectrum signal processing technique to improve the defect sensitivity and spatial resolution of UGWs [11]. Results showed a 30 dB improvement in SNR. [PERSON] et al. demonstrated a multipath UGW imaging technique to localise defect detection in two samples: an aluminium plate with three fasteners and sixteen through holes, and a carbon-fibre-reinforced plastic composite panel, each of which had three defects [29]. [PERSON] et al. evaluated the defect detection ability of a UGW-based SHM system on the door of a commercial shipping container prior to implementing a strategy that involved gating of signals and combining multiple images to significantly improve the defect localisation capability [30; 31].
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 3 The Proposed Method
### Pixel-Wise Attention Module  
We design a PAM as shown in Figure 3 to provide supervision at each stage of the network, enabling the network to learn useful features progressively. As a lateral structure, PAM generates a guide map to enhance the features obtained by dilated convolution layers. In contrast to the existing attention strategies for image enhancement, we utilize information learned from ground truth (GT) to provide pixel-wise guidance for feature learning. This way of enhancing features with a guidance map provides a simple and effective pixel-wise attention mechanism for the network to focus on key regions of an image. This strategy introduces supervision at every stage rather than focusing only on the final output, and makes the stage learning process of the network controllable and reasonable. This module makes the network progressively deepen the understanding of image details and structure as the number of FRBs increases.  
The input of PAM is the output feature map \(F^{n-1}\) of the previous PRB. The feature map is first fed to a convolution layer to generate an image \(\hat{f}_{n}\). We calculate the \(\ell_{2}\) loss between \(\hat{f}_{n}\) and GT to make \(\hat{f}_{n}\) close to the corresponding clear image. Then, we learn features from \(\hat{f}_{n}\) using 2 standard convolution layers. Finally, we apply a squeeze-and-excitation (SE) block [31] to further improve understanding of the features in the lateral structure. The SE block can exploit the inter-dependencies among channels and enhance the representational power of features. The output feature of the SE block is the guidance map \(M^{n}\). \(M^{n}\) providing pixel-wise guidance for \(F^{n}_{d}\), and thus has the same size as \(F^{n}_{d}\).
## 4 Conclusions  
The endogenous opioid system is an attractive target in the search for new forms of IBD therapy. However, it is not an easy task to tune the opioid homeostasis through direct stimulators, like opioid agonists, due to--among others--the risk of the development of adverse side-effects upon the activation of opioid receptors in the central nervous system. Therefore, a different approach has been investigated, i.e., by tuning enzyme activity through specific blockers (opiorphin, sialorphin and spinorphin), which increase the levels of endogenous opioids, and thus stimulate opioid receptors [15; 16]. We have shown in our previous work that sialorphin exerts a potent anti-inflammatory activity in the TNBS-induced acute mouse model of experimental colitis. This effect was mediated by MOR and KOR receptors, which play a major role in the regulation of the GI transit and the mucosal transport of fluids, maintaining GI homeostasis [5; 16]. We also showed that sialorphin is effective in the model of chronic relapsing colitis [16]. Our observations made sialorphin a very attractive molecule for further structure-activity relationship studies in the field of IBD treatment. However, there are some limitations to its use in systemic therapeutic application, namely the proteolytic degradation of peptide bonds in this molecule and the poor stability of its \(N\)-terminal glutamine residue.  
Dimeric forms of the peptides have the potential to be more resistant to proteases compared to monomers. In this paper, we demonstrated that the S-S bond homo- and heterodimerization of analogs of sialorphin, spinorphin and opiorphin containing Cys residue at the \(N\)-terminal position resulted in dimeric forms which in vitro exhibited higher inhibitory activity against NEP than their monomeric forms. The heterodimer of Cys containing analogs of sialorphin and spinorphin (peptide **X**) exerted potent anti-inflammatory effect in the mouse model of TNBS-induced colitis after systemic administration. Peptide **X** exhibited comparable anti-inflammatory activity as its endogenous parent compound (sialorphin) [16] simultaneously it was more resistant to plasma peptidase degradation.  
In summary, our results suggest that the design and synthesis of novel enephalinase inhibitors is a promising direction in the search for novel anti-IBD drugs and could become an alternative for patients for whom current treatments (NSAIDs, corticosteroids) do not provide sufficient relief.  
Conceptualization, E.K. and J.F.; methodology, M.S. ([PERSON]), M.S. ([PERSON]), J.F. and E.K.; investigation, M.S. ([PERSON]), M.S. ([PERSON]), J.F. and E.K.; writing--original draft preparation, M.S. ([PERSON]), M.S. ([PERSON]), J.F. and E.K.; funding acquisition, J.F. and E.K.; supervision, J.F. and E.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Gdansk (531-T040-D741-20 to E.K.) and the Medical University of Lodz (503/1-156-04/503-11-001-19-00 to J.F.).  
The authors declare that they have no conflict of interest.
## 3 Results
### Spatio-Temporal Variations of Seasonal GPP and Annual GPP  
#### 3.3.1 Spatial Variations  
The spatial distribution of average annual GPP (GPP\({}_{\text{ann}}\)) in the SRYR from 2001 to 2019 is shown in Figure 5a. The average GPP\({}_{\text{ann}}\) in the SRYR was 324.0 \(\pm\) 24.4 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) and increased gradually from west to east. The region with a GPP\({}_{\text{ann}}\) less than 200 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) was primarily in the western SRYR, while that with more than 600.0 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) was primarily in the eastern SRYR. The average GPP\({}_{\text{max}}\) in the SRYR was 4.5 \(\pm\) 0.5 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) and shared a similar spatial pattern with the average GPP\({}_{\text{ann}}\) (Figure 5b). The annual GPP\({}_{\text{max}}\) mainly occurred during the period from July to August, with the higher GPP\({}_{\text{max}}\) (>7.0 g\(\cdot\)C\(\cdot\)m\({}^{-2}\)) in the eastern SRYR. The average annual values of spring GPP (GPP\({}_{\text{spr}}\)) (Figure 5c), summer GPP (GPP\({}_{\text{sum}}\)) (Figure 5d) and autumn GPP (GPP\({}_{\text{aut}}\)) (Figure 5e) were, respectively, 28.1 \(\pm\) 3.8 g\(\cdot\)C\(\cdot\)m\({}^{-2}\), 235.9 \(\pm\) 24.0 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) and 59.9 \(\pm\) 5.8 g\(\cdot\)C\(\cdot\)m\({}^{-2}\), and shared similar spatial patterns with GPP\({}_{\text{ann}}\) and GPP\({}_{\text{max}}\). As the average proportion of GPP\({}_{\text{sum}}\) to GPP\({}_{\text{ann}}\) reached 72.8%, GPP\({}_{\text{sum}}\) played an important role in regulating the spatio-temporal variations of GPP\({}_{\text{ann}}\). The average winter GPP (GPP\({}_{\text{win}}\)) was close to 0 g\(\cdot\)C\(\cdot\)m\({}^{-2}\) and could be neglected (Figure 5f), and thus GPP\({}_{\text{win}}\) was not further analyzed in this study.  
Figure 4: Spatial distribution of the trends of vegetation: (**a**) SOS, (**b**) EOS, (**c**) LOS and (**d**) POS in the SRYR from 2001 to 2019. The M-K test results of vegetation phenology are shown in the upper right corner, with red representing an advanced/extended trend and blue representing a delayed/shortened trend at a significance level of 0.05 (\(p\) < 0.05). The inter-annual variations of vegetation phenology in the SRYR from 2001 to 2019 are shown in the lower-left corner.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Application Challenges
#### 5.1.3 System Complexity  
Water supply systems are large-scale infrastructure, thus CPSs applied to provide autonomous monitoring of the system facilities are apparently made of thousands of nodes with devices (such as sensors and actuators), which makes the configuration very complex. Modern-day water supply systems require a system to provide measurement and update for water quality, leakage detection and localization, pressure monitoring, and water demand monitoring among other factors. As such, a plethora of different sensors, each for the specific application, are needed for deployment. This increases system complexity. In addition, the system complexity is also increasing in a bid to improve the energy consumption of the sensing device and nodes by incorporating energy harvesters and some other methods for improving energy efficiency.
## 4 Concluding Remarks  
In the context of growing concern for environmental protection and reduction in fuel consumption, flow control for gust load alleviation can play a vital role for future greener aircraft design. Some main conclusions can be drawn from the review.  
Currently aircraft flow control for gust load control are achieved by deflecting control flaps, such as spoilers or tailing-edge flaps to create forces and moments that needed to alleviate the gust load. Being unsteady aerodynamic disturbances, gusts can have very high frequencies. For an effective gust load alleviation system, the capability of fast responses is the key factor. However, as the traditional flaps acting mainly for high-lift devices and flight manoeuvring, they are normally large sized and therefore heavy. These traditional flaps tend to exhibit low-frequency responses, which are ineffective for high-frequency gusts due to their large inertia.  
In order to achieve faster responses, efforts towards smaller flap sizes have been made. However, these efforts have focused only on wind turbines up to now. To make a simpler system compared to the active flow control means which usually require complex actuating systems, passive wing-tip devices have also been studied for gust load alleviation. Passive control means can be designed to be reliable and effective at some flight conditions. It is hard to make them reliable and effective for a wide range of gust conditions.  
The flow control methodologies using fluidic actuators are shown to have good potential for load control and flight control to replace the traditional flaps. This results in fewer moving parts, possibly less weight, less maintenance, high-frequency actuations and thus improved aerodynamic performance. This article has introduced two of the most promising actuators, i.e., surface jet blowing and circulation control. It has been demonstrated that both CC and surface jet blowing have a fast response characteristic and the capability for adaptive gust load controls. These actuators still face many engineering challenges. For CC, the main issue is the sharp decrease in load control capability with the increase in freestream velocities. For surface jet blowing including normal blowing and upstream blowing, the load control capability in terms of lift modulation is not as effective as CC at low speeds. Therefore, for industrial applications, more efforts are needed with in-depth investigations on these actuators.  
Conceptualization, N.Q. and Y.L.; Investigation, Y.L.; resources, N.Q.; writing--original draft preparation, Y.L.; writing--review and editing, N.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Performance Evaluation
### Routing Performance Analysis
Also, the paths found by SCMR are optimized in terms of number of hops since the routing algorithm depends on the hop-count as one of its metrics along with preventing path loops and path cycles, which lead to minimum number of packet forwarding from a source to the sink. Moreover, the aggregation process and data fusion done at CHs reduce the size of correlated data within a cluster and thus decrease the needed amount of energy to deliver them.
## 2 Role of AI in Dentistry
### Endodontics  
Endodontics, the branch of dentistry focused on the treatment of dental pulp and root canal systems, presents unique challenges due to the complexity and variability of tooth anatomy. While the basic structure of the root canals is relatively consistent, there are numerous anatomical variations that can complicate treatment. These variations can include extra canals, unusual curvatures, or atypical configurations that may not be immediately apparent through traditional diagnostic methods. AI and machine learning technologies are proving to be invaluable tools in navigating these challenges. By analyzing large datasets of dental images, AI can identify patterns and predict anatomical variations with high accuracy. This predictive capability is particularly useful in endodontic treatment planning, where understanding the precise anatomy of a tooth's root canals is critical to the success of procedures such as root canal therapy. AI can assist in the visualization ofthese structures, providing detailed 3D models that help clinicians anticipate and address potential complications before they arise. Furthermore, AI-driven tools can enhance the detection of root canal infections, which are often challenging to diagnose in their early stages. By analyzing subtle changes in dental radiographs, AI can help identify areas of infection or inflammation that might be missed during a manual examination. This early detection is crucial in preventing the spread of infection and ensuring the long-term success of endodontic treatments. As AI continues to evolve, its role in endodontics is likely to expand, offering even greater precision and reliability in the diagnosis and treatment of complex dental conditions.
# A Geospatial Approach to Improving Fish Species Detection in Maumee Bay, Lake Erie
## 3 Results
### Species Accumulation
Sampling efficiency of ten traditional sites with five hot spot sites were 69% for electrofishing, 62% for paired fyke nets, and 93% for bottom trawling. It was also estimated based on 2017 data that reaching 95% sampling efficiency with traditional random sampling would require approximately 85 samples for electrofishing and 105 samples for paired fyke nets while using the hybrid sampling approach would require 55 samples for electrofishing and 70 samples for paired fyke nets.
# Flavor Chemistry of Virgin Olive Oil: An Overview
## 3 Factors Affecting the Flavor of VOO
### Botanical, Agronomical and Technological Process
#### 3.1.3 Harvest and Olive Crushing System  
The harvest, post-harvest practices and the olive milling have a significant impact on the composition of VOO and therefore its flavor. All stages of the extraction process modulate the volatile composition of olive oil [103]. The crushing step is of paramount importance as it leads to breaking the drupe cells such that the enzymes (Beta-glycosidase, LOX, PPO, esterase, etc.) can act. The type of milling system thus influences the final oil quality, both in term of biophenols and volatiles, with the main difference being from the traditional stone mill and the metallic hammer crushers with higher rotation rate [13; 104].
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 1 Introduction
### Motivation and Contributions  
Prior work on Wi-Fi sensing has demonstrated a growing threat to everyday civilians, as their gestures and personal information can be monitored by malicious attackers armed only with commercial Wi-Fi hardware. Hence, we are motivated to develop protection methods to thwart attempts to monitor unwilling patrons. We have identified some prior works that show that a brute forced jamming mechanism can diminish the attacker's threat; however, this approach entails severe degradation of the wireless communications link. This paper first experimentally validates the threat of human gesture recognition attacks with Wi-Fi sensing. We then present novel insights into a contention-based defence mechanism for thwarting said attacks. We make the following contributions:  
1. In this study, we replicated keystroke recognition attacks from previous research and discovered new information on the minimum CSI data rates needed to identify and categorize keystrokes through Wi-Fi sensing.
2. After verifying that the accuracy of Wi-Fi sensing drops as a function of the CSI data rate, we extend [PERSON]'s [9] model of saturated CSMA/CA systems to create a mathematical relationship between the channel contention and the accessible CSI data rate.
3. We conducted an experimental validation of our mathematical model that describes the available CSI data rate in relation to the level of contention. Our findings show that as more devices access the Wi-Fi channel, the CSI data rate decreases, which implies that we may not have sufficient information to detect keystrokes.
4. Using this contention-based drop in the CSI rate, we motivate a defence strategy against Wi-Fi-based keystroke inference attacks. We achieve this by injecting controlled interference through another device that transmits ping packets to reduce the CSI sensing rate below the required threshold for accurate sensing.
5. We present a trade-off study that details the consequences of such contention-based defence strategies on the available Wi-Fi bandwidth for normal users.  
Figure 1: Interference topology.  
The paper is structured as follows. In Section 2, we summarise state-of-the-art Wi-Fi sensing and its defence strategies. In Section 3, we provide a more detailed description of the Wi-Fi sensing system model and the importance of data rates for the system accuracy. We subsequently demonstrate a working test-bed of Wi-Fi-based keystroke detection in Section 4 and experimentally highlight the importance of CSI data rates. In Section 5, we propose a model to exploit Wi-Fi contention protocols to reduce CSI data rates in defensive endeavours. This contention model is then applied in Section 6 to thwart the accuracy of our keystroke detection test-bed. Finally, in Section 7, we provide insights on the trade-off between security improvement and channel bandwidth.