esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Isolated Single Tree Radio Channel Characterization
### Experimental Measurements
#### 2.4.2 Signal to Noise Ratio  
Following the procedure described in [15], it is possible to estimate the coverage distance for an IEEE 802.15.4 connection if the Signal to Noise Ratio (SNR) is obtained as a function of distance. As described in [15], for a 22 byte frame length and a PER of 2%, then a BER \(<1.14\times 10^{-4}\) would be needed. According to [15] and Figure 9 the SNR required is 0 dB approximately. The noise power is measured using the SA, at a central frequency of 2.45 GHz and for a span of 5 MHz that simulates a 5 MHz bandwidth ZigBee channel. The value measured was \(-82\) dBm.  
The resulting SNR is shown in Figure 9. It can be deduced that for a ZigBee system with a threshold of SNR = 0 dB connectivity is possible in the vicinity of the tree. The connectivity is kept for both simulated and experimental cases, even if 2.5.5 dB are subtracted to compensate the antenna gain used in the measurements. This result agrees with [15; 20].  
This result reinforces the conclusion given in Section 2.3.2 in which it was indicated that the presence of the tree, thanks to the diffraction phenomenon, reinforced the signal reached in certain points around the tree, as also stated in [45].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Height** & **Zone** & **Linear** & **FI model** & **RMSE** \\ \hline \multirow{4}{*}{Measured} & 2 m & Diffuse & \(P_{0}=-42.81,n=7.412\) & — & 1.491 \\  & & OLoS & \(P_{0}=-29.07,n=-1.275\) & \(\alpha=-29.18,\beta=-0.818\) & 0.9687/1.053 \\ \cline{2-6}  & 3 m & Diffuse & \(P_{0}=-40.64,n=6.413\) & — & 1.329 \\  & & OLoS & \(P_{0}=-27.81,n=-1.037\) & \(\alpha=-27.68,\beta=-0.7047\) & 0.188/0.2681 \\ \hline \multirow{4}{*}{Simulation} & 2 m & Diffuse & \(P_{0}=-45.02,n=15.45\) & — & 7.256 \\  & & OLoS & \(P_{0}=-33,n=0.9304\) & \(\alpha=-33.41,\beta=0.6963\) & 5.565/5.552 \\ \cline{2-6}  & 3 m & Diffuse & \(P_{0}=-40.50,n=7.67\) & — & 5.263 \\ \cline{1-1}  & & OLoS & \(P_{0}=-37.18,n=1.863\) & \(\alpha=-38.95,\beta=1.582\) & 6.201/6.096 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Path loss fitting parameters for measurement and simulation results.  
Figure 9: Signal to Noise Ratio (SNR) vs. distance to transmitter for a ZigBee device.
## 3 Kinetic Analysis Methodology
### Coats-Redfern Integral Approximation Method  
Integral approximation methods replace the integral in Equation (6) with an integral approximation function, \(Q(x)\), where \(x\) is equal to \(E_{a}\)/\(RT\). The integral can therefore be expressed by the following expression (complete derivations can be obtained in previously published works [30]):  
\[g(\alpha)=\frac{AR}{E_{a}\beta}T^{2}\textit{exp}\left(\frac{-E_{a}}{RT} \right)Q(x) \tag{7}\]  
Many approximations for \(Q(x)\) exist and the Coats-Redfern approach is a commonly-used example:  
\[Q(x)=\frac{(x-2)}{x} \tag{8}\]  
\(Q(x)\) changes slowly with values of \(x\) and is close to unity [47]. Plots of \(\ln\left[g(\alpha)/T^{2}\right]\) versus \(1/T\) (Arrhenius plots) will result in a straight line for which the slope and intercept are \(E_{a}\) and \(A\)[50]:  
\[ln\left[\frac{g(\alpha)}{T^{2}}\right]=-\left(\frac{E_{a}}{R}\right)\left( \frac{1}{T}\right)+ln\left[\frac{AR}{E_{a}\beta}\right]\left(1-\frac{2 RT_{av}} {E_{a}}\right) \tag{9}\]  
where \(T_{av}\) is the average temperature over the course of the reaction [51].  
In order to find the kinetic parameters, Arrhenius plots for each \(g(\alpha)\) mechanism can be produced. One pair of \(E_{a}\) and \(A\) for each mechanism can be obtained by linear data-fitting and the mechanism is chosen from the data fit with the best linear correlation coefficient (R\({}^{2}\)) [47].  
Coats-Redfern cannot be applied to simultaneous multi-step reactions, although it can adequately represent a multi-step process with a single rate-limiting step [47]. For reactions with consecutive steps, the reaction can be split into multiple steps and model-fitting can be carried out for each step [47].
# Hybrid Clustering Algorithm Based on Improved Density Peak Clustering
## 2 Related Works  
The primary objective of clustering is to group a set of data objects into distinct clusters, achieving high intra-cluster similarity and low inter-cluster similarity. Data clustering methods have found widespread application in fields such as machine learning, pattern recognition, and video image analysis. Over the past few decades, numerous clustering algorithms have been developed, addressing this challenge from various perspectives successfully. Density peak clustering (DPC) is a relatively recent addition to the family of clustering methods. Its core idea revolves around placing cluster centers in regions of higher density compared to their neighbors while maintaining a distance from lower-density points. This concept allows us to intuitively determine the number of clusters and automatically identify and exclude outliers, irrespective of their shapes. This capability enables DPC to form non-spherical clusters. Additionally, DPC operates without the need for an iterative process and swiftly identifies the final clusters, exhibiting numerous advantages.  
However, a significant limitation of the DPC method lies in its sensitivity to the manual selection of cluster centers and their parameters. To address this limitation, various improvements have been proposed. These enhancements encompass methods for more efficient density value measurements [10], strategies for automatic cluster number determinations [11], effective label assignment mechanisms [12], and extensions of DPC to diverse application domains, including mixed data, high-spectral-band selection, and large-scale data analysis. This paper's focus is on enhancing the automated determination of cluster centers within the DPC method, with contributions such as the approach proposed by [PERSON] et al. (2016) [13]. These improvements make the DPC method more versatile and robust.
# Metric for Estimating Congruity between Quantum Images +
## 1 Introduction
Very much like the more mature classical PSNR image quality measure, the proposed QIFM metric was formulated as a pixel difference image quality tool but with the necessary attributes required for its execution on the quantum computing framework built into it. These two simple instances differentiate the QIFM from both the digital PSNR measure and [PERSON]'s probability-based image similarity score [23]. The only resources required to execute the QIFM metric are quantum computing resources (i.e., logic gates) and some useful data retrieved from the classical versions of the reference and test image pair. In this study, we concentrate on this new metric and propose accretions tailored towards ensuring moderate computational requirements are used for its execution. In addition, we present a more detailed validation of the effectiveness of this new quantum image quality metric by deploying it as the metric to estimate congruity between images in an expanded database (in terms of both the size of images and the scope of applications) to include images of varying size, complexity, and applications [13]. Throughout our appraisal of the QIFM's efficiency we will compare its results alongside those obtained using the classical PSNR (\(P\)) measure and [PERSON]'s probability-based image similarity score (\(S\)); hence, allowing us to utilise an \(FPS\) (fidelity, PSNR, and Score) analysis as the basis for validation. As originally conceived in [1] and validated in this study, the proposed QIFM metric has the potential to replace the PSNR as an image quality measure on the quantum computing framework. In Section 2, we enunciate the veridical properties of the strip representation used to encode \(2^{m}\)-ending FRQI quantum images and adopt its use to encode our quantum register containing the reference and test images whose congruity is sought. To complete the background review required for our study, in latter parts of Section 2, we review [PERSON] et al. 's probability-based image similarity score and offer some motivation as to why it is inadequate to estimate congruity between two (or more) quantum images. Section 3 is devoted to discussions on the recrudesced QIFM image quality metric, while Section 4 focusses on the enhanced validation of this new image fidelity metric.
## 5 Attenuation of Adherent-Invasive _Escherichia coli_ Pathogenicity in Crohn's Disease  
[PERSON] and coworkers have presented data that suggest that c-di-GMP signaling in the adherent invasive _Escherichia coli_ (AIEC) pathotype is associated with the development of [PERSON]'s disease (CD) [69]. CD is a long-term inflammatory disorder that mainly targets the gastrointestinal tract, leading to inflammation that can occur anywhere from the mouth to the anus [69]. CD is classified as an inflammatory bowel disease (IBD), along with ulcerative colitis [70].  
[PERSON] et al. reported that the EAL domain phosphodiesterase yhjH and yahA play a crucial role in regulating the adhesion and invasion of the AIEC strain LF82 by modulating c-di-GMP levels [71]. Their study found that flagella absence in LF82 leads to a loss of type 1 pili, which are essential for intestinal epithelial cell invasion. Mutation of fliA, a key regulator of flagella and pili synthesis, reduces yhjH expression, resulting in elevated c-di-GMP levels, which suppresses type 1 pili production and impairs bacterial adhesion and invasion. Furthermore, they found that overexpression of yhjH or yahA, which degrade c-di-GMP, partially restored type 1 pili synthesis, adhesion, and invasion in the LF82-\(\Delta\)fliA mutant, demonstrating that lowering c-di-GMP levels counteracts these defects. These findings suggest that reducing c-di-GMP levels in adherent invasive _E. coli_ through yhjH and yahA PDE inhibition could attenuate AIEC pathogenicity, providing potential therapeutic insights for [PERSON]'s disease treatment.
## 3 Proposed Solution
### Forwarding Strategy  
In this paper, we introduce a new forwarding approach that combines the NDN's default forwarding strategy and the location-based forwarding strategy. As we have already mentioned, the data retrieval in NDN is based on content names without any location information of the device, resulting in the inability to handle producer mobility. That is why we propose integrating the location-based forwarding strategy, which can coexist with the NDN's default forwarding strategy to maintain uninterrupted content delivery to consumers. When the MP initiates a handoff, it sends a RemovePath (RP) packet to notify the nearest Anchor about the predicted coordinates of its next location where it will be present after completing the handoff. Consumers remain unaware of the MP's location change and send Inputs using NDN's default forwarding strategy. Once an intermediate router receives the Input, it checks its FIB to determine the Face with the longest prefix match. If a matched FIB entry does not exist, the Input will be forwarded until it reaches the nearest Anchor. Subsequently, the Anchor checks its FIB to determine the strategy to forward this Input. If the path has been created between the MP and this Anchor before the reception of the Input, then the Anchor and all intermediate routers along the path use the NDN's default forwarding strategy. Otherwise, the Anchor updates the X, Y, and Z coordinates in the Input with the producer's predicted location coordinates and forward it using the location-based forwarding strategy.  
Leveraging the location information of fixed routers and Access points, each router in the location-based forwarding strategy determines the next hop for the Input based on the Cartesian coordinates of its neighbors, where it calculates the Euclidean distance between its neighbors' coordinates and the destination coordinates included in the Inputand it forwards the packet to the nearest router to the destination coordinates, ensuring the shortest path to the MP.  
To implement the location-based forwarding strategy without modifying the Input structure, we use the \"GeoTag\" header field within the Input, introduced in the second version of the NDN Link Adaptation Protocol [33]; this predefined 3-byte field is specifically designated for including some location information in Input. We assume that routers and Access points involved in the network are stationary and possess prior knowledge of each other's Cartesian locations.
## 5 Results and Discussion
### Simulation Results
#### 5.2.1 Results under Different Controller Configurations
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**No Controllers**} & \multicolumn{2}{c}{**PSSs**} & \multicolumn{2}{c}{**BESS Controllers**} & \multicolumn{2}{c}{**PSSs and BESS Controllers**} \\ \cline{2-9}  & \(P(\textbf{a}>-\textbf{0.1})\) & \(P(\textbf{\xi}<\textbf{0.1})\) & \(P(\textbf{a}>-\textbf{0.1})\) & \(P(\textbf{\xi}<\textbf{0.1})\) & \(P(\textbf{a}>-\textbf{0.1})\) & \(P(\textbf{\xi}<\textbf{0.1})\) & \(P(\textbf{a}>-\textbf{0.1})\) & \(P(\textbf{\xi}<\textbf{0.1})\) \\  & \{**\%**\} & \{**\%**\} & \{**\%**\} & \{**\%**\} & \{**\%**\} & \{**\%**\} & \{**\%**\} & \{**\%**\} & \{**\%**\} \\ \hline
1 & 100 & 100 & 0 & 97.9797 & 0 & 0 & 0 & 0 \\
2 & 96.5000 & 100 & 94.3965 & 100 & 0 & 0 & 0 & 0 \\
3 & 92.0600 & 100 & 97.5650 & 100 & 0 & 0 & 0 & 0 \\
4 & 94.4200 & 100 & 0 & 0 & 0 & 100 & 0 & 0 \\
5 & 0 & 100 & 0 & 0 & 0 & 100 & 0 & 2.7340 \\
6 & 0 & 100 & 0 & 0 & 0 & 100 & 0 & 2.2390 \. & - & - & - & - & \" & \" & \" & \" \\
15 & 0 & 100 & 0 & 0 & 0 & 100 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of stability indices under different controller conditions using the optimization-based tuning method (OBTM). Figure 13: Objective-function convergence.
## 3 Results and Discussion
### Flow Velocity Evolution and Distribution of the Investigated Droplets
#### 3.2.2 Velocity Distributions of the Investigated Binary Droplets
Supposing that the averaged droplet diameter (\(D_{\text{aver}}\)) at the stable evaporation stage is:  
\[D_{\text{aver}}=(D_{0}+D_{1})/2 \tag{1}\]  
where \(D_{0}\) is the initial droplet diameter, and \(D_{1}\) is the final droplet diameter of the stable evaporation stage, the averaged perimeter of the radial flow (\(L\)) approximately equals to half of the droplet perimeter (C) plus the averaged droplet diameter. \[L=C/2+D_{\text{aver}}=(\pi/2+1)\;D_{\text{aver}} \tag{2}\]  
Figure 8: Velocity profiles of the five investigated binary droplets along the droplet horizontal radial direction at four moments (125, 250, 375, and 500 ms) of the stable evaporation stage. The horizontal coordinate denotes the radial distance from the droplet center normalized by the droplet radius, while the vertical coordinate represents the flow velocity at the corresponding position. For each binary droplet, the approximate radial flow laps \(n\) at the measured duration time of the stable evaporation stage (\(t_{1}\)) can be calculated with Equation (3). \[n=V_{\text{aver}}t_{1}/L \tag{3}\]  
where \(V_{\text{aver}}\) is approximated as 3 mm/s. As shown in Table 2, at the whole stable evaporation stage, the radial flows can only run about one lap at the most, which implies that the convection-induced mass transfer is relatively weak, and consequently, the content gradient along the radial direction of the droplets is still controlled by mass diffusion.
## 3 Validation of the Algorithm  
Being the presented algorithm an application of the Static Theorem, a comparison between the results obtained using such an analysis and the Kinematic Theorem was carried out [43; 51]. While the Static Theorem aims to find a lower limit of the load multiplier, the Kinematic Theorem's objective is to find an upper limit of the load multiplier [51; 56; 58]. The comparison was based on the lines of thrust obtained with the two methods, as well as on the position of the plastic hinges.  
Four case studies were analyzed in order to validate the algorithm. In particular:  
1. Case study 1: All sixth arch with a concentrated force applied in the midspan of the arch.
2. Case study 2: All sixth arch with a concentrated force applied at a distance of 1.767 m from the left intrados springing point.
3. Case study 3: All sixth arch, without ballast, and the Category D4 line load was applied with the first wheel at a distance of 1.941 m from the left intrados springing point.
4. Case study 4: All sixth arch, with ballast, and the Category D4 line load was applied with the first wheel at a distance of 1.941 m from the left intrados springing point.  
The arches considered in the four case studies are all sixth arches characterized by length of the arch \(L=5\) m, depth \(s=0.55\) m, fill layer depth at the key of the arch \(c=0\) m, and depth of the ballast layer null (\(b=0\) m) for the first 3 case studies, while the ballast layer was considered to be \(b=0.8\) m for the 4 th case study. The arch thickness was set as \(B=3.5\) m. The unit weights \(\gamma_{AR}=20\) kN/m\({}^{3}\), \(\gamma_{BALL}=18\) kN/m\({}^{3}\), and \(\gamma_{FILL}=18\) kN/m\({}^{3}\) were considered for the masonry, ballast, and fill layer respectively. The train-track load was assumed \(q_{TT}=2\) kN/m\({}^{2}\).  
Figure 5: Load redistribution hypothesis.  
The arches were each divided in 40 blocks (\(n_{blocks}=40\)) and the hypotheses on the load distribution were as stated in Section 2.4.  
For validation, it was not necessary to conduct the capacity analysis (Section 2.2) since the depth of the arches was given; therefore, only the demand analysis was conducted.  
The lines of thrust obtained with the numerical algorithm are shown in Figure 6, where, for comparison purposes, the positions of the plastic hinges obtained with a software that operates using the Kinematic Theorem (software Ring [59]) are shown in red.  
The position of the plastic hinges coincides with good approximation for all the 4 validation case studies. In addition, even though Ring employs a more refined load redistribution rule (Boussinesq [57]), the results obtained with Ring and with the proposed numerical method are in good agreement, confirming that the accuracy of the implemented redistribution rule is sufficient at this stage.
## 1 Introduction
[PERSON]'s team conducted a lot of research work on the numerical simulation of the TVC, with the research objects being ellipse hydrofoils, conventional propellers, and ducted propellers, and mainly explored the cavitation number, Reynolds number, nucleus size, nuclei spectrum distribution, bubble transformation, turbulence simulation method, etc. The team published a summary report on numerical research into cavitation inception in a complex flow field based on their research work and plentiful achievements [47]. A large number of prediction results indicate that the numerical prediction method for cavitation inception with the combination of the Lagrangian framework and the Eulerian framework based on bubble dynamics theory is capable of adapting to various water quality, being more practical and accurate. There are two sets of discrimination criteria in the numerical simulation research based on this method: the optics criterion and acoustics criterion. The optics criterion refers to determining cavitation inception based on the nucleus size; if a nucleus grows to a specific size, it is determined as cavitation inception. For example, the specific size is set at 1 mm, which can be observed with the naked eye, and if a nucleus's diameter grows to 1 mm, the nucleus is determined as cavitation inception [39]. However, for the case of different initial nucleus sizes, there is not a definite and unified standard in this criterion that can be used to determine cavitation inception. The acoustics criterion refers to determining cavitation inception using acoustic pressure pulse signals. However, the acoustics criterion is seldom used owing to its complicated calculation and inconsistency with the conventional experimental theory. In summary, the current cavitation inception numerical prediction method based on bubble dynamics has not established a set of unified and specific discrimination criteria applicable to propeller cavitation inception. This method is mainly used for verifying rather than predicting cavitation inception. Therefore, if we have the intention of discriminating cavitation inception accurately, it is essential to establish a set of reliable cavitation inception numerical discrimination methods with definite physical significance. In view of this, we proposed a new discrimination method for TVC inception applicable to propellers based on the Eulerian-Lagrangian framework in this research to compensate for the deficiencies in existing cavitation inception numerical prediction methods based on bubble dynamics. Using the commercial program ANSYS-FLUENT, the stress-blended eddy simulation model (SBES) was used to accurately predict the TVC inception of the PPTC propeller, and the prediction results were compared to experimental results and prediction results obtained using the minimum pressure coefficient method. Moreover, with the aim of offering a reference for the accurate numerical prediction of propeller TVCinception, we further investigated the effect mechanism of tip vortex flow characteristics on nuclei evolution.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Grasp Planning  
During the grasp planning phase, a set of grasps is created for each trajectory. This phase occurs after the trajectory planning phase since it is assumed that the tubes are rigid enough so their shape is not affected by the location of the grasping point. If the tubes were more flexible, then the trajectory planning should take into account the grasp's configuration.  
**Definition 14**.: _A **grasp** for a virtual tube is defined by the 3D point \(G_{point}=[G_{x},G_{y},G_{z}]\), known as the **grasping point**, which is the gripper's tool center point (TCP) position when it grasps the object. In addition, a grasp is associated with an orientation for the gripper, the fingertip positions, an approximation vector it follows before grasping the tube, and a trajectory it follows after grasping._
## 1 Introduction
Based on these findings, [PERSON] et al. [1] proposed a joint retrieval scheme for ocean surface wind and current vectors based on VAM using NRCS and DCA estimated from the TanDEM-X along-track interferometry (ATI) SAR data. Results indicated that the VAM had minimal bias compared to the direct retrieval method. However, research on integrating DCA information into VAM has primarily focused on the application of ENVISAT ASAR [6,14] and TanDEM-X ATI-SAR [1] Doppler data, with relatively limited exploration of Sentinel-1 Doppler information. Additionally, existing studies often lack stratified validation analysis for different wind speed ranges when evaluating the inversion accuracy of wind speed and direction using VAM. Furthermore, the impact of background wind field quality on the results of VAM has not been thoroughly investigated. This study utilizes 1803 scenes of Sentinel-1 vertical-vertical (VV) polarization level-2 Ocean (OCN) products from coastal areas to conduct a comparative analysis of different wind field retrieval methods. VV polarization was selected due to its superior signal-to-noise ratio (SNR), demonstrating effective noise suppression even under low-wind-speed conditions [15]. By quantitatively assessing the retrieval accuracy of each method across various wind speed ranges and examining the impact of background wind field accuracy on the results of VAM, this study provides valuable insights for the practical application of Sentinel-1 level-2 OCN products for ocean surface wind field retrieval. The remainder of this paper is organized as follows: Section 2 details the datasets used in the study. Section 3 outlines the wind field retrieval methods based on Sentinel-1 VV polarization level-2 OCN products, covering both direct inversion method and VAMs. Section 4 presents the experimental results, followed by a comprehensive discussion. Finally, Section 5 summarizes the conclusions of the study.