esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Ways to Control the Image Capture Process (Process Management)  
The algorithms explained in the previous section that deal with image transformation conduct a kind of result management, but not process management, on images captured from a scene. Thus, in addition to using image matching algorithms that aim to identify similarities between images captured arbitrarily from different viewpoints, a preprocess should be required to control the viewpoint of the images. With this strategy, changes in viewpoints of pictures are minimized, and the current image matching algorithms can perform more accurately. The key to capturing two pictures from a single viewpoint is to hold a camera in a single position and orientation.  
One way to provide this condition is to use a fixed point camera approach [19]. In this approach, for each scene, a camera must be installed with a fixed viewpoint. Therefore, the resulting pictures are from the same point of view. However, this approach is not practical, especially for chaotic locations such as construction sites, which are exposed to the movements of workers, vehicles, and materials that can accidentally block or relocate cameras. Moreover, this method is very costly because a camera is needed for each scene (Figure 5a).  
The second way is to embed a benchmark (point of reference) for each scene on the jobsite and use the total station approach (i.e., installing the camera on a tripod) when taking pictures. In this way, crews can retrieve the position and orientation of the camera in different trials. However, the feasibility of implementing such an idea in a location that is under construction and exposed to different disturbances, such as the movement of workers and equipment, dust, floor washing liquids,  
Figure 4: An example of two scenes in one room during renovation. (**a**) West wall captured (before renovation); (**b**) North wall captured (after renovation).  
or demolishing and replacing floor covers, which could remove any marks and nails, makes this option unreliable (Figure 5b).  
Another way is to use a system that can navigate crews to locate the camera in a reference location and viewpoint without using a physical reference point or installing a fixed-point camera for each scene. To locate a camera on a single location and viewpoint, the position and orientation parameter values of the camera need to be retrieved remotely. However, the question is, \"Is there any way to repeat and retrieve the camera's position and orientation parameter values remotely on messy, dusty, and temporary locations like construction sites for the purpose of decreasing image transformation?\"
## 3 Results
### Titanium Manufactured Connectors  
The connectors were manufactured with titanium \"Ti-6 Al-4V-ELI\", which is used to develop medical devices in the health sector. The connectors' main function in this material lies in accurately connecting the Baxter(r) and PISA(r) commercial devices. The threading of the connectors was manufactured with support for both internal and external threads, adapting them to establish a mixture of equipment from both brands. The connectors were manufactured based on the proposed detailed designs. Engineering drawings and sketches were developed for the manufacturing of BP and PB connectors to generate them in titanium alloy, followed by a four-step process in approximately six hours of manufacturing.  
1. 5/8 \"TIAL6V4\" Titanium aluminum alloy bar is trimmed to a certain length for each connector in two pieces per connector (total four pieces in 1.5 h);
2. Cannula drill for internal threads on the pieces for the linking points with the commercial devices (1.5 h process);
3. Cannula drill for external threads on the pieces to join it in a single one with a forged thread technique (1.5 h process);
4. Forged impact to obtain full BP and PB connectors (1.5 h process). Figure 8 shows the BP connector manufactured with Ti-6 Al-4V: Baxter(r) infusion and drain bags to PISA(r) transfer line connection. It is used for the linking between elements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Drain Fluid Process**} \\ \hline \multirow{3}{*}{**Parameters**} & \multicolumn{2}{c}{**Flow (mm/s)**} & \multicolumn{2}{c}{**Pressure (MPa)**} & \multicolumn{2}{c}{**Vorticity (s\({}^{-1}\))**} \\ \cline{2-7}  & \begin{tabular}{c} **Inlet** \\ **Peritoneal** \\ **Cavity** \\ \end{tabular} & \begin{tabular}{c} **Outlet** \\ **Drainage** \\ **Bag** \\ \end{tabular} & \begin{tabular}{c} **Inlet** \\ **Peritoneal** \\ **Cavity** \\ \end{tabular} & \begin{tabular}{c} **Outlet** \\ **Drainage** \\ **Bag** \\ \end{tabular} & \begin{tabular}{c} **Outlet** \\ **Peritoneal** \\ **Cavity** \\ \end{tabular} & \begin{tabular}{c} **Outlet** \\ **Drainage** \\ **Bag** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} BP \\ connector \\ \end{tabular} & \(1.301\times 10^{-4}\) & \(1.420\times 10^{-3}\) & \(0.00078\) & \(0.00075\) & \(1.06\) & \(0.42\) \\ \hline
\begin{tabular}{c} PB \\ connector \\ \end{tabular} & \(1.709\times 10^{-4}\) & \(1.429\times 10^{-5}\) & \(0.00075\) & \(0.00067\) & \(1.06\) & \(0.40\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Flow, pressure, and vorticity behavior of the fluid in the BP and PB connectors in the drainage process.  
Figure 8: BP connector manufactured in titanium with branded equipment.  
Similarly, Figure 9 presents the PB connector's manufacture that links the infusion bags and PISA(r) drainage to the Baxter(r) transfer line, obtaining a mix between equipment from both brands.  
The manufactured connectors do not present complications to the thread assembly with the two brands' equipment; it was also found that their handling is appropriate and can be used for the DPCA procedure following the corresponding process and operating instructions.
## 3 Statistical Methodology  
### Design of Experiments  
The proposed multi-fidelity method requires several sets of working points for screening, training, and verification purposes. Different designs of experiments are considered, depending on the required characteristics of each set of points. In this way, a fractional factorial design obtains the screening points, and a Box-Behnken design is used to provide the training points. For the sake of computational effort, the same set of points employed for screening will also be used to verify the accuracy of the surrogate models.  
\begin{table}
\begin{tabular}{l l l} \hline
**Stage and Included Steps** & **High-Fidelity Model 1** & **Multi-Fidelity Framework** \\ \hline
**1. Conceptual modeling** & & \\
1.1 Definition of \(N\) model parameters and \(l\) & & \\ number of levels & & \\
1.2 Definition of target and & & \\ intermediate variables & & \\ \hline
**2. Identification of significant parameters** & & \\
2.1 Determination of screening subset & & \\
2.2 Running the models at screening points & & \\
2.3 Identification of \(N^{\prime}\) significant & & \\ parameters (\(x_{j}\)) & & \\
2.4 Removal of \(N-N^{\prime}\) & & \\ non-significant parameters & & \\ \hline
**3. Surrogate modeling (for space design exploration)** & & \\
3.1 Selection of surrogate candidates & & \\
3.2 Determination of training subset & & \\
3.3 Running the models at training points & & \\
3.4 Construction of surrogate models & & \\
3.5 Determination of verification subset & & \\
3.6 Prediction of target variable at & & \\ verification subset & & \\
3.7 Calculation of error metrics and selection & & \\ of best surrogate variable and model & & \\
3.8 Usage of surrogate for space & & \\ design exploration & & \\ \hline \end{tabular}  
* For the sake of simplicity, HF superscript is omitted from the notation in this column, as it affects all variables and functions.  
\end{table}
Table 1: List of method steps and actions to be conducted depending on the fidelity level.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 4 Experimental Setup  
To test the proposed approach of automatically designing DRs for the VRP, the GP method described in the previous section is applied on two VRP problem variants: CVRP and VRPTW. Furthermore, both variants are investigated under static and dynamic case. The static case denotes the case in which all customers are known beforehand, meaning that all the information about the problem is known at the start of solving. On the other hand, in the dynamic case, not all customers are known at the start, but rather they become available as time progresses. In this case, we investigate the influence of different level of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline population size & 500 \\ selection & tournament of size 3 \\ mutation rate & 0.5 per individual \\ number of runs & 30 \\ termination condition & 25,000 evaluations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Genetic programming parameters.  
dynamism on the performance of the method, 5%, 25%, and 50%, which denote the number of customers that are not known up front and become available at a later point in time.  
To test the approach for CVRP the problem instances proposed by [PERSON] et al. are used [52], whereas for the VRPTW variant the instances proposed by [PERSON] are used [42]. Both problem sets are divided into a training set, which is used by GP to evaluate the rules during the evolution process, and a test set, which is used after training to evaluate the quality of the evolved rules on unseen instances and compare the results. The division of the problem sets is described in more detailed in the subsequent sections. The approaches are evaluated across several criteria that are commonly considered in VRP problems, which include the number of vehicles, total travelled distance, and total tardiness. These criteria are combined in different ways using a weighted linear combination, as will be outlined in more detail at the corresponding parts in the next two sections. Apart from these two criteria, another criterion called the distance deviation (more details about which are given in Section 6.3) is also used to demonstrate the ability of the approach to generate DRs for an arbitrarily defined user criterion. The results obtained by the proposed method are compared with the best known results for the problem instances known in the literature, as well as with simple manually designed DRs which present the most similar approach from the literature to the automatically developed DRs.
## 2 Materials and Methods' Theoretical Basis and Diagnostic Model
### PSO-Optimized Support Vector Machine  
An SVM is a machine learning classification algorithm for problems with multiple categories that achieves the effect of linear classifier by creating an optimal hyperplane [25].  
In the literature [26], an SVM is applied to the fault diagnosis of reciprocating compressors for the first time. It is proved that the classification method has high reliability in the state detection of reciprocating compressors, while kernel function parameters \(g\) and penalty parameters \(c\) have great influence on the classification performance of SVMs. PSO is a classic optimization algorithm inspired by the foraging behavior of bird flocks in bionics. This algorithm simulates the movement of particles and information exchange in the search space to find the optimal solution. It is widely used in fields such as function optimization, machine learning, and neural network training, and has become a powerful optimization tool [27]. To obtain a better classification effect, PSO is used to find the optimal kernel function parameters \(g\) and penalty parameters \(c\) of SVMs. The principle of PSO is to optimize all objects that could be considered as the search space of particles. Since each particle has a fitness value, the ideal value \(pbest\) and the global best value \(gbest\) of particle swarm could be determined through their individual fitness function. For a given \(D\) dimensional space, the particle swarm is composed of \(m\) particles, the position of the \(x_{i}\) particle is expressed as \(x_{i}=(x_{i1},x_{i2},\ldots,x_{iD}),i=1,2,\ldots,m\), and the speed of the \(x_{i}\) particle can be expressed as \(v_{i}=(v_{i1},v_{i2},\ldots,v_{iD})\). The optimal solution searched after \(t\) iterations is \(pbest_{i}=(p_{11},p_{22},\ldots,p_{iD})\), and the global optimal solution is \(gbest_{i}=(g_{i1},g_{i2},\ldots,g_{iD})\). The velocity and position of the particle are updated as follows:  
\[v_{id}(t+1)=v_{id}(t)+c_{1}r_{1}(p_{id}-x_{id}(t))+c_{2}r_{2}(g_{id}-x_{id}(t)) \tag{18}\]  
\[\begin{array}{l}x_{id}(t+1)=x_{id}(t)+v_{id}(t+1),\\ i=1,2,\ldots,m,\\ d=1,2,\ldots,D\end{array} \tag{19}\]  
where \(c_{1}\) and \(c_{2}\) are the acceleration constants; \(r_{1}\) and \(r_{2}\) are random numbers within \([0,1]\); \(t\) is the current evolution algebra; \(T_{max}\) is the maximum number of evolutions; and \(v_{max}\) is the maximum speed limit of the particle. The K-CV method [28] is employed to cross-validate the SVM classifier, obtaining the classification accuracy. This accuracy is then utilized as the fitness metric in the particle swarm optimization algorithm to identify the global optimal solution for the parameters.
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Shape Features
#### 2.1.2 Feature Classification  
The classification techniques are machine learning algorithms that are used to categorize input sample data into different classes or groups of belonging or membership [3; 56; 11; 5]. These classifiers may employ supervised learning, unsupervised learning, and reinforcement learning methods during their training [39]. Supervised learning occurs when a person is a trainer of the model and may use pre-formed datasets to conduct the training [39; 53]. Unsupervised learning occurs when there is no training data available and the algorithm must train itself and improve its classification efficiency by iteratively adjusting itself [5; 53; 5]. Reinforcement learning occurs when the algorithm makes classification rulings based on the feedback applied by the environment to it [12; 39]. In the case of vision-based plant disease-monitoring systems, the most cited classification algorithms include support vector machines (SVM), artificial neural networks, k-nearest neighbors' machines, and fuzzy machines. The following subsections discuss these classification techniques.
## 2 RISs and Their Integration with NTNs
### Application of RISs to NTN Communications  
Incorporating RISs into NTNs and upcoming 6G technologies presents distinct advantages. RIS's ability to manipulate EM waves without conventional transceivers aligns well with the objectives of NTNs, particularly in reducing payload weight and power consumption, crucial factors in satellite operations [69]. For 6G, RISs can be pivotal in realizing ultra-reliable, low-latency communication and enhanced connectivity. Its energy-efficient nature and reduced hardware requirements offer an economical and sustainable solution for the expansive infrastructure envisioned in 6G. The unique properties of RISs, including improved signal propagation and coverage, address the challenges in NTNs and 6G, such as large coverage areas and signal attenuation, making it an innovative and viable technology for 6G networks.
# Shear Mechanical Properties of Rock Joints Under Non-Uniform Load Based on DEM
## 2 Modeling of Non-Uniform Load Mechanics  
Establishing a mechanics model that accurately reflects non-uniform load conditions is the foundation of this study. Assuming that the joint is smooth, we constructed an overburden load mechanics model using the strip division method [20]. According to this, three typical forms of overburden load distribution are observed on slopes and in tunnels (Figure 2). The first and second forms of load distribution are commonly encountered in tunneling conditions, where the load on the joint comes from the dead weight of the overlying strata (P = mgh), with its magnitude determined by the height of the overlying strata. Since the angle of the joint remains constant, both distribution forms (a) and (b) exhibit proportional increases or decreases in height along with shear direction while maintaining an isometric pattern. The difference \(L_{d}\) between adjacent loads depends on the angle \(\alpha\). The third type of load distribution, often found in slope conditions, features more overlying strata above the joint center that symmetrically decrease along both sides with a constant difference; thus, this results in greater overlying loads at the joint center tapering off evenly on both sides.  
Figure 2: Three main forms of the distribution of overburden loads on joints (where \(N=10\); h is the height of the rock above the joint; P is the load; \(\alpha\) is the angle between the joint plane and the rock surface; \(L_{d}\) is the length difference determined according to the strip division method; and \(L_{n}\) is the length of the rock).  
In this study, these three distribution forms are designated as far-field concentration (FFC), near-field concentration (NFC), and focus on central (FOC) based on their respective patterns along the shear direction indicating varying degrees of load concentration. To accurately represent how different modes of load distribution affect mechanical properties at joints, we divide each form into equal strips and use identical differences in applied loads for NFC and FFC distributions, respectively [21]. The magnitude distributions within NFC and FFC strips relative to the shear direction can be expressed as follows:  
\[\left\{\begin{array}{l}G_{T}=N\cdot G_{1}+\frac{N(N-1)G_{d}}{2}\\ G_{n}=G_{1}+(n-1)G_{d}\end{array}\right. \tag{1}\]  
where \(G_{T}\) is the total overlying load, \(N\) is the total number of strip loads, \(G_{n}\) is non-uniform load at different positions, \(n\) is the sequence number of each sub-load, \(G_{1}\) is the first strip load along the shear direction, and \(G_{d}\) is the load difference between each strip load.  
For FOC, the load decreases with the same difference along the node, and the distribution of the load with the shear direction can be expressed as follows:  
\[\left\{\begin{array}{l}G_{T}=2[\frac{N}{2}G_{1}+\frac{N(\frac{N}{2}-1)G_{d} }{2}]\\ G_{n}=G_{1}+(n-1)G_{d}\\ G_{n}=G_{\frac{N}{2}}-(n-1)G_{d}\\ \end{array}\right. \tag{2}\]
# A Review of the Performance of Infilled RC Structures in Recent Earthquakes
## 1 Introduction
The definition of the most common damage types herein presented is supported by several post-earthquake damage reconnaissance missions carried out by different researchers from the Laboratory for Earthquake and Structural Engineering (LESE) of the University of Porto. The list of countries visited post-earthquake include: Sichuan (China) in 2008, L'Aquila (Italy) in 2009, Lorca (Spain) in 2011, Emilia-Romagna (Italy) in 2012, Gorkha (Nepal) in 2015, Muisne (Ecuador) in 2016 and Chiapas (Mexico) in 2017 (see Figure 1). Furthermore, the lessons learned from all these earthquakes are presented and discussed. Finally, the types of damage found are compared with the standard construction practice in Southern European countries. Retrofitting strategies to eliminate or prevent these types of damages are also presented and discussed throughout the manuscript. In addition, the repair costs of damaged buildings after earthquakes and the impact of the infill walls on the global costs are presented. For this, the rehabilitation costs of the entire damaged building structure are compared with the costs of rehabilitating the damaged masonry infill walls. The last goal of this manuscript is to present a case study to assess the effect of applying simplified retrofitting strategies to prevent the soft-storey mechanism, which is one of the most common problems observed in all the past earthquake events. An eight-storey infilled RC structure was simulated in the software SeismoStruct [12]. Four retrofitting strategies are tested: RC jacketing, the addition of RC shear walls, the addition of steel braces with and without energy-dissipation devices.
# Determination of the Waterway Parameters as a Component of Safety Management System
## 5 Discussion  
The width of the safe manoeuvring area is set at a certain level confidence, which depends on the types of manoeuvres performed, their intensities, and the adopted levels of risk [16]. In practice, the decisive element is the lane width reserve, depending on the  
1. accepted confidence level (acceptable risk);
2. traffic intensity;
3. water parameters;
4. parameters of the manoeuvre being performed.  
The simulation and analytical research were carried out for a dredged fairway up to 12.5 m in the section from 5.0 km to 66.0 km; this made it possible to determine a safe fairway width for ships with the maximum parameters [20]:  
1. bulk carriers and container ships (L = 220 m, B = 32.3 m, and T = 11.00 m);
2. passenger cruisers (L = 260 m, B = 33.0 m, and T = 9.00 m).  
Finally, the following values of the safe fairway widths were adopted:  
1. for the straight sections, d = 110 m;
2. for the transitional sections, d = 130 m (min. 250 m before or after the turn);
3. for the turns, d = 150 m.  
The presented research was supposed to answer if it would be possible to allow the entrance of bigger bulk carriers at the port of Szczecin in those designed fairway parameters. This need was dictated by the plans to call on large bulk carriers at the port of Szczecin and Police, and taking into account the current modernisation of the fairway, it is reasonable to check what the maximum ship is that can actually enter the port. Moreover, at the present stage, it is possible to redesign the fairway so that it will allow the entry of the tested vessel. The final decision will be made by the economic calculations, which unfortunately goes beyond the scope of this publication.  
Among all the tests carried out, the most favourable were those using PNS, regardless of the time of day. In Table 6, there is a comparison of the tested bulk carrier (L = 260 m, B = 36.0 m, and T = 11.00 m) fairway widths (good visibility with the PNS system) with designing the fairway widths. In the straight sections, where the assumed width of the fairway is 110 m, differences can be seen to the disadvantages of the designed widths (value given in red colour). In the sections on Zalew Szczecinski, the difference is 10 m, and in the sections limited by the proximity of the shore, the difference is 30 m. However, the sections with bends, due to the fact that they are designed to a width of 150 m, all meet the criteria of a safe fairway width for the tested bulk carrier. It should be noted that the results obtained do not clearly mean that a ship with such parameters cannot enter the port. The calculated widths assume independent passing through the Swinoujscie-Szczecin fairway without towing assistance (except for mooring operations). Assuming the deepening of the fairway to 12.5 m, which is currently taking place, and with the use of towing assistance, entering the port of Szczecin is very feasible. This increases the costs of operating the ship, and, as already mentioned, the final decision will be the result of many factors, primarily economic ones.
## 1 Introduction
Through this, our study not only contributes to the theoretical knowledge in the field but also offers practical solutions for improving V2V communications in challenging and disconnected areas. The paper begins with a review of related work in Section 2. Section 3 lays the groundwork for vehicular communications, followed by Section 4, which details a reputation-based system model. Section 5 presents the novel signature scheme, leading into Section 6, which introduces the simulation experiments showcasing the approach's effectiveness. Section 7 delves into the simulation discussion, exploring various aspects of the results related to the operational effectiveness of the Pre-Signature scheme. The paper culminates in Section 8, summarizing the key findings and suggesting avenues for future research.