esa-sceva/satcom-chunk-collection huggingface dataset:
# Retinal Vessels Segmentation Techniques and Algorithms: A Survey
## 4 Retinal Vessels Segmentation Techniques
### Multi-Scale Techniques
As a first step, the noise-corrupted retinal image (grey layer) was denoised against the additive Gaussian noise by applying a flux based anisotropic diffusion technique; a multi-scale response of multi-level resolution of retinal image was computed. Then, as a second step, a vessel model was established in order to analyze the eigenvalues and the eigenvectors of Hessian matrix for each scale. The final result of multi-level analysis represents the pixel-wise maximum of the results obtained over all scales. The proposed algorithm reported area under ROC curve of 0.94514 on STARE dataset. [PERSON] et al. [69] presented an algorithm to segment the blood vessel in retinal images based on multilevel line detection and connection of line primitives. Multilevel line detection is used for extracting the retinal vessels at multiple values of Gaussian smoothing parameters. Then, the line primitives that extracted at different scales were merged into one single vessel extraction result. The proposed algorithm was validated using DRIVE dataset where it proved the capability to detect most of the major part of vessel skeleton with false positives. A new approach based on a multi-scale method for segmentation of retinal vessel was proposed by [PERSON] et al. [67] where it used weighted Medialness function along with the eigenvalues of the Hessian matrix. The proposed approach consists of two phases. In the first phase, the medial axis of retinal vessels was extracted using a two-dimensional Medialness function in multiple scales and sum of smoothed eigenvalues of the image. The second phase is for vessel reconstruction where centerline of vessels was extracted and radius of vessels was estimated simultaneously to obtain the final segmented results. The proposed approach was validated using DRIVE and STARE datasets where it showed high performance in terms of accuracy and area under the ROC curve where it has achieved accuracy of 0.9659 with area under ROC of 0.9580 via DRIVE dataset and accuracy of 0.9756 with area under ROC curve of 0.9678.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
## 2 Background and Related Work
### NDN Forwarding Strategies  
#### 2.2.1 Problem Description  
Today, mesh networks, both stationary and mobile, mostly rely on reactive broadcast-based mechanisms to find packet forwarding paths. According to these mechanisms, every packet is transmitted to every neighboring node, facilitating the discovery of a path to the destination without the need to maintain routing tables and update them regularly. The benefits of reactive routing can be visible in mobile ad hoc and low-power wireless networks, where proactive routing protocols cause a lot of periodic routing announcements. However, the utilization of broadcasts dramatically increases network load.  
The broadcast-based mechanism is well-suited for NDN architecture, as it does not depend on prior knowledge of the requested data location: (i) a consumer issues an Interest, (ii) the network finds a fitting producer to serve this Interest, (iii) and then the data are sent back in reverse direction to the originating node. With this idea in mind, efficient broadcast-based solutions for NDN systems should satisfy the following principles: (i) no global knowledge is available to any of the participating nodes, and each node makes an independent decision when and where to forward an incoming packet, (ii) no forwarded packets are altered in any way and no additional packets are generated, (iii) minimize the overheads of the broadcast by utilizing any prior possible knowledge gained through handling previous packets, and (iv) adaptation to changing environments caused by either increasing networks loads or mobility.
## 3 Analysis of Dynamic Performance and Energy Consumption
### Analysis of Cyclic Impact Energy
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Specimen** \\ **No.** \\ \end{tabular} & \
\begin{tabular}{c} **T/\({}^{c}\)C** \\ \end{tabular} & **Impact No.** & **Peak Stress/MPa** &
\begin{tabular}{c} **Impact** \\ **Velocity/m s\({}^{-1}\)** \\ \end{tabular} & **W\({}_{\textbf{I}}\)/J** & **W\({}_{\textbf{R}}\)/J** & **W\({}_{\textbf{T}}\)/J** & **\(\zeta\)/\%** \\ \hline \multirow{4}{*}{7-1} & \multirow{2}{*}{25} & 1 & 56.86 & 7.81 & 307 & 132 & 36 & 139 & 45.3 \\  & & 2 & 57.53 & 8.23 & 357 & 246 & 40 & 71 & 19.9 \\  & & 3 & 41.63 & 8.05 & 342 & 136 & 26 & 180 & 52.6 \\ \hline \multirow{4}{*}{8-5} & \multirow{2}{*}{200} & 1 & 35.45 & 7.90 & 318 & 215 & 16 & 87 & 27.4 \\  & & 2 & 38.98 & 7.84 & 318 & 257 & 19 & 42 & 13.2 \\  & & 3 & 37.21 & 7.58 & 322 & 149 & 16 & 157 & 48.7 \\ \hline \multirow{4}{*}{6-3} & \multirow{4}{*}{400} & 1 & 34.24 & 7.78 & 332 & 164 & 23 & 145 & 43.7 \\  & & 2 & 29.29 & 7.99 & 333 & 248 & 12 & 73 & 21.9 \\  & & 3 & 20.17 & 7.87 & 313 & 235 & 5 & 73 & 23.3 \\ \hline \multirow{4}{*}{17-2} & \multirow{2}{*}{600} & 1 & 29.76 & 8.23 & 375 & 217 & 15 & 143 & 38.2 \\  & & 2 & 27.90 & 7.90 & 353 & 287 & 8 & 58 & 16.4 \\ \hline \multirow{4}{*}{23-2} & \multirow{2}{*}{800} & 1 & 14.92 & 8.47 & 489 & 423 & 16 & 50 & 10.2 \\  & & 2 & 10.89 & 8.08 & 351 & 218 & 0.4 & 132.6 & 37.8 \\ \hline \multirow{4}{*}{4-4} & \multirow{2}{*}{1000} & 1 & 8.66 & 7.84 & 346 & 313 & 0.6 & 32.4 & 9.4 \\  & & 2 & 6.56 & 8.05 & 356 & 320 & 0.3 & 35.7 & 10.02 \\ \hline \end{tabular}
\end{table} 
Table 3: Calculation of cyclic impact energy of sandstone after different temperatures. caused cracks in the sandstone, and the internal damage is serious. The sandstone was destroyed by impact loads, and the absorbed energy was reduced. That is, the dissipated energy was reduced.
## 3 Proposed Methods
### Optimization Algorithm Steps  
The incremental multi-objective whale optimization algorithm (IMOWOA) first employs the Sobol sequence to initialize the population, thereby enhancing diversity. A reward factor is then integrated to refine the search direction, followed by the incorporation of the grey wolf optimization (GWO) algorithm to perturb the current optimal solution, thereby augmenting global search capabilities.  
Step 1: Initialize algorithm parameters, including population size, data dimensions, number of iterations, alpha, beta, etc.  
Step 2: The regression function was employed to formulate a relational model delineating the association between the objective variable and the process parameters. Subsequently, set objective function by Equation (5).  
Step 3: Set the constraints according to Equation (6).  
Step 4: Initialize three populations using Sobol in Equation (7).  
Step 5: Select individuals exhibiting high adaptive capabilities among the whale population as reference points. Assess the value of the acceleration coefficient A\({}_{\text{i}}\). If the \(\mid\text{A}_{\text{i}}\mid<1\), incorporate a random perturbation (p) and update the optimal positions of the different groups following the Equation (11). Conversely, the optimal positions of the various groups will be updated following Equation (12) provided.  
Step 6: The optimal position of the overall population is updated under Equation (13), and the validity of this optimal position is assessed against the established constraints utilizing Equation (6).  
Step 7: Following a single iteration, the reward factor is incorporated, and the objective function f\({}_{\text{i}}\) is modified following Equation (9) to determine the current optimal position.  
Step 8: the optimal position and its corresponding fitness value are updated based on the prevailing state of the whale population.  
Step 9: The convergence criteria are evaluated using three termination conditions: (1) the relative change rate of the best fitness value F remains below 0.1% over five consecutive iterations, (2) the total iteration count reaches 800, (3) the relative fluctuations of both HV and Spacing metrics are maintained below 0.1% across five consecutive iterations. To ensure computational efficiency without compromising solution quality, the algorithm employs low-frequency evaluation and dynamic thresholds derived from sensitivity analysis. The optimization process terminates immediately upon satisfying any criterion; otherwise, it returns to Step 4 for continued iteration.  
The flowchart of the IMOWOA is illustrated in Figure 2.
# Influence of Welding Parameters on the Weld Pool Dimensions and Shape in a TIG Configuration
## 5 Conclusions  
In the literature models, the arc ignition is not considered and the numerical procedure to initiate the arc is not often described. The description of the interaction with the workpiece during the first instants is therefore subject to discussion. The results of the mathematical models need therefore to be considered carefully during the first instants and it should be better for model validation to consider longer times. For these reasons and due to the lack of experimental values, our experiments were realized from 1 s to 20 s. In a vertical TIG configuration, effects of different welding parameters were analysed through the weld pool dimensions, shape and through energy involved to melt the material. Six interaction times were considered. For each interaction time, three methods were applied to determine the weld pool dimensions.  
Our analysis pointed out that the welding current is the more influential parameter, as width and depth rise significantly when it increases. Increasing the arc length modifies both the shape and dimensions: width and depth of the weld pool are higher. Use of helium as a shielding gas instead of Arcal.37 leads to a deeper weld pool but the shape remains the same. These effects are explained by the predominance of penetrating movements, which become higher when energetic plasma such as helium is used. Finally, modification of the tip angle affects the weld pool dimensions, especially in depth, which is greater when the tip angle decreases. Nevertheless, this parameter appears less significant than other parameters studied.  
The melted area shapes and their related energy, and not only the depth-to-width ratio, appear essential to study the influence of welding parameters. All these experimental data and observations are obtained in a vertical TIG position without movement. Even if this configuration can be considered'simple' compared to the real welding process, these experimental results are essential and can be used for comparisons and model validation in simple 2D configuration before considering a transient 3D configuration.  
The authors would like to thanks here ESAB Company and [PERSON] from West University of Sweden for their contribution on this work. [PERSON] conducted the experiments, analyzed the data and proposed the plan of the paper. [PERSON] and [PERSON] analyzed the data, write the paper and proofread it. The authors declare no conflict of interest.  
Figure 7: (**a**) Effect of the tip angle on the evolution of the weld pool shapes; (**b**) Effect of the tip angle on the depth-to-width ratio.
# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
## 3 MAT Solutions for PVI Shopping
### Navigation and Product Identification
#### 3.2.1 Tag Based Systems  
Tag systems use wireless communication technology to transfer data from the tag attached to an object to a tag reader for automatic identification and tracking. Developers are using several types  
Figure 2: MAT solutions for the parts of the shopping process for PVI.  
of tags, but we will concentrate on RFID and NFC tags [63]. RFID uniquely identifies items by using radio waves. Each RFID system has three components: a tag, a reader, and an antenna. The reader sends a signal to the tag via the antenna, and the tag responds to this signal by sending its unique identification information [64]. There are two types of RFID tags: active and passive. Active tags broadcast a signal up to a range of 100 m. Passive tags use electromagnetic energy from the reader, but they do not have their own power source. They can broadcast a signal up to a range of 25 m. Passive tags are typically smaller and cheaper than active tags [65].  
For shopping, RFID tags are attached to products and PVI hold tag readers to identify them. After identifying the items, PVI get details about them, such as name, price and special offers [66; 67; 68; 55]. These systems use a server or a local database to store product details. Other solutions use RFID tags for navigation inside shopping malls. These solutions use tag reader attached to white cans to identify RFID tags. They use a map to guide them through the store and suggest the shortest route for PVI to reach their products. They give audio messages that provide verbal directions [69; 70].  
NFC technology is a version of RFID that operates at a maximum range of about 10 cm. The NFC system consists of three main components: a tag, a reader, and a database. The tag is a place to store information, while the reader is an NFC mobile device that reads the content of the tag. The database stores additional information to the NFC tags. NFC technology is used for active or passive communication. In active mode, the NFC tag and NFC tag reader produce a radio field during communication. In passive mode, only the NFC tag reader generates a radio field and begins the communication session, so NFC tags do not process any information sent from other sources, and cannot connect to other passive components. The main differences are the communication distance and the use of mobile devices by NFC systems or RFID Readers by the RFID systems [33; 71].  
To use them for shopping, NFC tags are attached to products and identified by smartphones like RFID. After identifying the items, PVI get details about them [33; 72]. These systems use a database to store product details so PVI can scan them and get information such as name, price, and special offers [37]. Some other solutions used NFC tags for navigation inside shopping malls. Like RFID, NFC tags use a map to guide PVI through the store and suggest the shortest route for PVI to reach their products. These tags also give audio messages that provide verbal directions [33; 73].
## 7 Conclusions  
This study investigates the stability of tunnel surrounding rock during excavation through a fractured slate zone using the Longshu Tunnel of the Central Yunnan Water Diversion Project as a case study. A combination of full-face and step-by-step excavation simulations, NSGA-II-based optimization, and field validation is employed to derive  
Figure 22: Displacement diagram of tunnel deformation monitoring station 4.  
excavation strategies suitable for weak and complex geological environments. The key findings are summarized as follows:  
(1) The numerical simulations revealed that step-by-step excavation leads to significantly reduced deformation at critical locations of the tunnel cross-section--including the crown, invert, and shoulders--compared to full-face excavation. Furthermore, the stress concentrations on both sides of the tunnel were consistently lower, improving the mechanical stability of the surrounding rock.  
(2) An automated optimization framework was developed by integrating the NSGA-II algorithm with the FLAC3D platform using Python scripting. This framework enabled efficient parameter tuning based on multi-stage deformation objectives. The optimal lower step height was identified as 3.0 m, offering a balance between deformation control and construction efficiency.  
(3) Field monitoring data confirmed the accuracy of the simulation results, with measured displacements at the tunnel crown and invert ranging from 32 mm to 35 mm and shoulder displacements ranging from 25 mm to 29 mm. Stabilization of the surrounding rock was observed approximately 25 m behind the tunnel face, validating the model's predictive capability.  
(4) The optimized step excavation method significantly improved construction performance, enabling better control of ground deformation, reducing the need for excessive support and remedial work, and contributing to overall cost efficiency and construction safety.  
(5) It was also acknowledged that the current model is deterministic in nature. Considering the geological heterogeneity in fractured slate zones, future work could incorporate probabilistic analysis to evaluate the impact of geological uncertainty on excavation stability and optimization outcomes.  
S.N.: investigation, writing--review and editing, and data curation. J.W.: Supervision, Methodology, Funding acquisition. J.Z.: writing--review and editing and investigation. J.P.: writing--review and editing, supervision, and investigation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Research Program of Shanxi Province (No. 202303021221042) and the Research Program of Shanxi Province (No. 202403021221119).  
Not applicable.  
The original contributions presented in this study are included in this article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Calculations and Statistical Analyses  
The specific growth rate (SGR) for each fish tank was calculated as Ln (W2) \(-\) Ln (W1) \(\times\) 100/t, where W1 and W2 are the tank's average fish weights (g) in the beginning and at the end of the experiment, and t is the experimental period in days (42 d). Feed conversion ratio (FCR) was calculated as the weight of feed eaten (kg)/fish weight gain (kg). For analyzing the spinach biomass, dry weights were recorded at the start and the end of the experiment. Shoot and root lengths were recorded for each plant at the end of the experiment. The total individual plant weight (g) on each raft was calculated using the total end dry weight (root + shoot). The starting dry weight of spinach seedlings (0.003 \(\pm\) 0.0005, \(n\) = 3) was negligible, and therefore biomass change during the experiment was not calculated separately.  
Statistical analyses were run with IBM SPSS Statistics 26. Independent samples \(t\)-test was used to compare the means between treatments for fish and plant data analysis. The mean concentrations of macronutrients (g/kg) and micronutrients (mg/kg) in spinach shoots were also compared between the treatments by the independent samples \(t\)-test.  
Homogeneity of variance was checked by [PERSON]'s test. The variances of the means of all variables were equal. The observational unit was always the tank or tray (i.e., \(n=3\)).  
The daily means of ammonia (TAN), nitrite, nitrate, and pH were compared between treatments by repeated measures ANOVA (\(n=3\)). [PERSON]'s test of sphericity \(p\)-value was always \(<0.15\); thus, the Greenhouse-Geisser adjustment was applied. The selected anions (chloride, nitrite-N, nitrate-N, sulfate, and phosphate) were analyzed at the end of the experiment and compared between treatments by repeated measures ANOVA (\(n=3\)). A Huynh-Feldt adjustment was applied because [PERSON]'s test of sphericity \(p\)-value was always one. The values for nitrate were Ln transformed before the statistical analysis. The values for nitrite were zero on the start and end day of the experiment and were not included in the analysis.  
The MIB and GSM in spinach shoots and roots and lipid content in fish muscle between treatments were analyzed by independent \(t\)-test, while the start values were compared with the end values by one sample \(t\)-test. For assessing MIB and GSM in water samples and fish muscles repeated measures ANOVA was performed. A Huynh-Feldt adjustment was applied because [PERSON]'s test of sphericity \(p\)-value was always one.
## 2 Materials and Methods
### Recurrent Neural Network (RNN)
#### 2.2.1 Long-Short Term Memory (LSTM)  
Long-Short Term Memory (LSTM) determines the quantity of information needed to be preserved and forgotten with the gates mechanism. A basic LSTM unit consists of a cell state \(c_{t}\), a forget gate \(f_{t}\), an input gate \(i_{t}\), and an output gate \(o_{t}\). LSTM uses a cell state \(c_{t}\) as a memory that stores information from all computations. To prevent the storage of unnecessary information in a long sequence of inputs, a forget gate \(f_{t}\) discards a fragment of it. Using an input gate \(i_{t}\), LSTM can select inputs that maximize outputs. The purpose of an output \(o_{t}\) gate is to decide which part of the cell state \(c_{t}\) is relevant to the output. Finally, a hidden state \(h_{t}\) of a basic LSTM is an element-wise product of \(o_{t}\) and the hyperbolictangent of \(c_{t}\). For every time-step \(t\), the compact forms of the equations for the forward pass of an LSTM unit, based on the descriptions above, are described as follows:  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{3}\]  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}) \tag{4}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{5}\]  
\[c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{6}\]  
\[h_{t}=o_{t}\odot\tanh(c_{t}) \tag{7}\]  
where \(x_{t}\in\mathbb{R}^{d}\) is the input; \(W\in\mathbb{R}^{hxd}\), \(U\in\mathbb{R}^{hxd}\), and \(b\in\mathbb{R}^{h}\) are weight matrices and bias vector parameters respective to each function; \(\sigma\) and \(tanh\) are sigmoid and hyperbolic tangent functions, respectively.