esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The agricultural land suitability assessment is an interesting approach to land use planning and to achieve food security goals, and where the new technologies may contribute significantly [1]. The digital transition's contributions to food security and agricultural planning come from the collection of information to support policy and decision makers [2] and the processing of these data to predict plausible scenarios [3]. Cropland mapping is a case where machine learning, for instance, may produce important insights for sustainable land management [4]. This is particularly important in African countries [5], for example, where the challenges related to food security are serious and deserve special attention to find adjusted solutions [6]. The application of new technologies in the African context is not an easy task [7].  
Agricultural land management and planning are interrelated with other land uses, such as urbanization [8]. With respect to agricultural land planning, the current challenges are diverse and range from the food supply to the ecosystem services [9]. In these frameworks, specifically for policy design, it is important to be aware of the diversity of stakeholders with different objectives and skills [10]. Additionally, the contexts associated with climate change bring new concerns [11].  
Machine learning is a part of artificial intelligence applied to consider data and approaches to learn similarly to humans. The machine learning methods use remote sensing information [12], for example, to predict farming yields [13] and promote an adjusted agricultural planning [14] in the framework of the digital transition [15]. The outputs of machine learning are important and bring added value to the following domains: food security [16] and nutrition [17]; planetary health [18]; water management [19]; irrigated areas identification [20]; land susceptibilities mapping [21]; farm area mapping [22]; soil fertility assessment [23]; soil salinization analysis [24]; smart honey chains [25]; agricultural resources management [26]; agricultural production planning [27]; and climate change impacts assessment [28] and agricultural modelling [29].  
Based on the contexts described above, and taking into account the gaps in the literature about the use of bibliometric analysis to assess dimensions related to the consideration of machine learning approaches for food security prediction and agricultural planning, the main objective of this research is to show insights from the scientific literature about the interconnections between machine learning and food security and their interrelationships with agricultural planning in the Era 4.0. Indeed, a search in the Scopus database for the topics \"machine learning\", \"food security\" and \"bibliometric\" (within the \"Article title, Abstract and Keywords\") identified only one study, related to ecological restoration [30], thus highlighting the novelty of the research presented here and its relevance to the literature and to the agricultural and food sectors.
## 3 Image Feature Extraction and Feature Matching Based on SIFT
### Stable Keypoint Orientation Information Assignment  
The stable extreme point extraction based on different scale spaces ensures the scale-invariant characteristics. Whereas the distribution of the direction information of the key points, that is, the gradient of the extreme point, ensures the angular invariance and rotation invariance of the key points to the image. Define \(L(x,y)\) as the original image space function, with the gradient magnitude of any key point being shown in formula (2), and the gradient direction in formula (3).  
\[m(x,y)=\sqrt{\left(L(x+1,y)-L(x-1,y)\right)^{2}+\left(L(x,y+1)-L(x,y-1) \right)^{2}}, \tag{2}\]  
\[\theta(x,y)=tan^{-1}(\frac{L(x,y+1)-L(x,y-1)}{L(x+1,y)-L(x-1,y)}). \tag{3}\]  
The direction of the key point is obtained through the gradient direction histogram. Firstly, the gradient direction of all pixels in the neighborhood of the key point is calculated with \(10^{\circ}\) being used as a unit direction interval for classification. Secondly, the number of key points that fall within each direction interval is accumulated and represented as a gradient direction histogram. Finally, the direction indicated by the maximum value of the longitudinal coordinate in the histogram is assigned to the key point as its main direction, and the direction with the number of key points equivalent to 80% of the peak value is utilized as the auxiliary direction of the key point [16; 17; 18]. The application of auxiliary directions can improve the robustness of the algorithm and help stabilize feature matching.
# A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies
## 9 Simulation Tools

Table 2: Simulation tools and testbeds for UAV systems performance analysis. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Name** & **Type** & **Mobility Model** & **Operating System** & **Programming Language** & **More Description** \\ \hline OPNET [131] & Simulator & \begin{tabular}{c} RW, Group mobility, \\ RWP, RD \\ \end{tabular}  & \ \begin{tabular}{c} Windows, Red Hat and \\ CentOS \\ \end{tabular}  & C, C++ & \begin{tabular}{c} Provides a powerful GUI and animation that involves \\ significant costs. \\ \end{tabular}  \\ \hline ROS-NetSim [132] & Simulator & N/A & Linux & C++, Python & An ROS package that acts as an interface between robotic & and network simulators. \\ \hline MATLAB [133] & Simulator & SRCM, PSMM & Windows, Linux, and & C, C++ & Provides different example applications involving both & fixed- \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Name** & **Type** & **Mobility Model** & **Operating System** & **Programming Language** & **More Description** \\ \hline BonnMotion [140] & Mobility & RW, RWP, CM, MG, & Linux, OSX & Java, Windows & Java software that creates and analyzes mobile ad hoc network characteristics. \\  & generator & RFCM and more & & Linux, OSM & & A robotics simulation platform for testing algorithms and building AI/ML platforms for UAV applications. It can connect to a robot control framework (ROS). \\  & & & & & & \\ \hline AriSim [142] & Simulator & N/A & Windows, Linux & C++, Cp, Python, Java & An open-source platform for AI research experimentation, with computer vision, deep learning, and reinforcement learning algorithms for UAVs \\  & & & & & & Integrates multi-robot simulators with network simulators for realistic communications simulation of networked multi-robot systems. It has been applied to interface the NS-2, NS-3, and ARGoS Player/STAGE simulators. \\ \hline Mininet-Wifi ([[https://mininet-wifi.github.io/accessed](https://mininet-wifi.github.io/accessed)]([https://mininet-wifi.github.io/accessed](https://mininet-wifi.github.io/accessed)) on 21 July 2022) & Emulator & \begin{tabular}{c} RW, RWP, \\ Randomized Direction, \\ Reference Point, \\ TimeVariantCommunity \\ \end{tabular} & \begin{tabular}{c} Any Ubuntu Distribution \\ from 14.04 \\ \end{tabular} & C++, Python & \
\begin{tabular}{c} An extension of the Mininet SDN network \\ emulator that adds or modifies classes and scripts. \\ \end{tabular} \\ \hline SUMO [144] & Simulator & N/A & Windows, Linux or MacOs & C++, Python &
\begin{tabular}{c} While it cannot be used directly in FANETs as it is tailored for 2D vehicles, it can be integrated with OMNet++ and NS3. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: Cont.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
###### Abstract
The strength of the interfacial film increases with the increase in interfacial pressure; hence, the emulsion becomes more stable. Therefore, the interfacial pressure can be used to indicate the strength of the interfacial film. [12]. Interfacial viscosity and interfacial pressure can be used as two important parameters to measure the structural strength of the interfacial film, and they play an important role in characterizing the rheological properties of the interfacial film. The strength of the interfacial film increases with the decrease in interfacial viscosity, such that the emulsion becomes more stable [13]. The stability of emulsions Is usually improved in the presence of surfactants, polymers, inorganic salts, or their combination [14]. In addition to the composition and properties of the oil phase and water phase, the stability is also greatly affected by the mixing conditions [15; 16; 17; 18]. The current research is mainly focused on the static stability of the emulsion after preparation. Thermodynamically speaking, the emulsion is an unstable system, and the two phases are eventually separated in a stationary state. Generally speaking, the stability of the emulsion decreases with the increase in moisture content [19; 20]. The preparation temperature can rapidly reduce the stability of most crude oil emulsions [20; 21; 22]. However, in some cases, the temperature may increase the formation rate of the interfacial film and enhance the stability of the emulsion [23; 24]. Some researchers have pointed out that stronger shear strength can improve the stability of the emulsion [19; 23; 25]. However, other studies have found that emulsion stability initially increases with the increase in shear strength, but decreases when the shear strength is higher than a certain critical value [26; 27]. At present, people usually regard emulsification as one of the oil displacement mechanisms of surfactant flooding or poly/surface binary flooding. Few people have systematically studied the effect of emulsion properties on heavy oil recovery from the perspective of emulsification itself. In this paper, the stability and droplet size distribution of W/O emulsions formed under different stirring speeds, temperatures, water cuts, mineralization degrees, and pH values were studied, on the basis of which double-tube core flooding experiments were carried out to study the shut-off capacity and oil displacement efficiency of W/O emulsions under different permeability contrasts and different water cuts of emulsions. Then, a numerical simulation of heavy oil emulsion flooding was carried out.
# Comparison of CAD/CAM and Conventional Denture Base Resins: A Systematic Review
###### Abstract  
At present, complete dentures (CDs) remain the only treatment available for the majority of dedentulous patients. CDs are primarily fabricated using a conventional method using polymethylmethacrylate (PMMA) resin. The steps involved in PMMA polymerisation directly affect the quality of the resin prosthetic base and any error reduces retention and occlusal accuracy of CDs. Furthermore, when using the conventional technique, the residual monomer alters the resin mechanical properties and may cause mucosal reactions. Recently, computer aided design and computer aided manufacture (CAD/CAM) techniques were increasingly used to fabricate CDs by machining resin discs that have been manufactured under high pressure and temperature. This systematic review compares CAD/CAM and conventional CDs according to their mechanical, physical and chemical characteristics, as well as the clinical impact of any differences between them. A review was conducted according to the preferred reporting items for systematic reviews and meta-analyses checklist on 392 publications from both PubMed and backward research. Fifteen studies have been included. Results showed that CAD/CAM resins had globally better physical and mechanical properties than conventional resins. The use of machined resin could improve the clinical performance, maintenance and longevity of CDs. Further studies in clinical use would be required to complement these results.  
denture base; complete denture; acrylic resin; CAD-CAM; milling resin +
Footnote †: journal: Applied Sciences
## 5 Results, and Discussion
### Comparison Analysis with Other Detectors  
To highlight the effectiveness of the proposed YOLOv10-Swin Transformer backbone, a comparison was made against Faster R-CNN, SSD, and R-FCN, each employing alternative backbones such as ResNet-101, ResNet-152, and MobileNetv3. All other features and hyperparameters (for example, batch size and iteration count) were maintained in their original configurations. Any custom adjustments required for the experiments were implemented within MMDetection [39] to accommodate specialized settings for each detector.  
As shown in Table 6, each detector-architecture combination was evaluated in terms of different metrics. Among the Faster R-CNN models, ResNet-152 yielded the highest AP50 (87.67) and AP50:95 (64.85) within its category, though it remained below the proposed method's metrics. SSD500 with ResNet-152 also demonstrated strong accuracy (AP50 = 87.88, IOU = 83.71), yet it still could not reach the proposed method's leading AP50 of 89.72 or AP50:95 of 66.71. R-FCN similarly achieved moderate performance, with ResNet-152 emerging as the best backbone in that series, but again trailing the proposed method. Notably, the proposed method excelled across \(\mathit{AP}_{\mathit{S}}\), \(\mathit{AP}_{\mathit{M}}\), and \(\mathit{AP}_{\mathit{L}}\) at 62.84, 86.39, and 92.53 respectively--signifying the robust detection of small, medium, and large objects. Although many configurations surpassed or approached 30 FPS, the proposed method also sustained a commendable 35 FPS, matching or exceeding the speed of most other entries. Consequently, it not only delivered the highest detection metrics but did so with competitive inference speed, making it the most effective overall approach in this comparison.  
Figure 11: Sample images detected with lower accuracy.
# Heuristics and Learning Models for Dubins MinMax Traveling Salesman Problem
###### Abstract
One popular approach to solving the DTSP, as discussed in several studies [35; 36; 37; 38; 33], involves sampling the interval \([0,2\pi]\) into \(k\) candidate headings at each target and posing the resulting problem as a generalized traveling salesman problem (GTSP) formulation. In GTSP (also known as the one-in-a-set TSP), given \(n\) nodes partitioned into \(k\) mutually exclusive sets, the goal is to compute the shortest path that contains exactly one node from each of the \(k\) sets. GTSP is an NP-hard problem since it reduces to the TSP when each cluster contains only one node. DTSP can be reduced to a one-in-a-TSP by forming \(n\) mutually exclusive sets consisting of \(k\) vehicle configurations corresponding to \(k\) heading angles at each of the \(n\) targets. Increasing the angle discretizations at each target for the  
Figure 1: Examples of Dubins Paths. one-in-a-set TSP variant results in solutions that get close to the DTSP optimum. This approach provides a natural way to find a good feasible solution to the DTSP problem, as noted in previous studies [36]. Figure 2 illustrates the discretization of heading angles at a target for the DTSP. In this work, we explore the MinMax variant of the mTSP problem for a Dubins-like vehicle by formulating it as a one-in-a-set TSP, called the MinMax Dubins generalized multiple traveling salesman problem (MD-GmTSP) and present three different approaches to solve it. The MinMax variant is an important problem to study as it directly relates the objective to the mission completion time for visiting all the targets by a team of vehicles. This variant is relevant for real-world applications as it accounts for the worst-case scenario in situations where \(m\) homogeneous vehicles are assigned travel paths by accounting for limitations on load/delivery capacities or refueling constraints. Contributions of This Article
* We formulate a mixed-integer linear program for the MD-GmTSP (Section 3). * We develop heuristics to generate feasible solutions for the MD-GmTSP in a relatively short amount of time, followed by using a combination of variable neighborhood search (VNS) metaheuristic [39] for improving solution quality (Section 5). * We explore learning-based methods to solve the MD-mTSP. An architecture consisting of a shared graph neural network and distributed policy networks is used to define a learning policy for MD-GmTSP. Reinforcement learning is used to learn the allocation of agents to vertices to generate feasible solutions for the MD-GmTSP, thus eliminating the need for accurate ground truth (Section 6). * Finally, we implement all the algorithms on a set of modified instances from the TSPLIB library [40] using CPLEX and present simulation results to corroborate the performance of the proposed approaches (Section 7).
## 1 Introduction  
Deep learning techniques [1; 2; 3], including deep convolutional neural networks (CNNs), are being employed widely in the field of image processing to conduct image classification [4; 5; 6], object detection [7; 8], and image segmentation [9; 10; 11; 12] tasks. Recently, many studies [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] have investigated the applications of deep learning techniques in medical imaging, which now serve as an expansion to this field.  
Image diagnosis using computed tomography (CT) and magnetic resonance imaging (MRI) is currently becoming indispensable in the medical field. Although a large number of CT and MRI images are being generated from daily medical examinations, these images are referred to as a follow-up for only a few specific patients. There are many existing models [4; 5; 6; 7; 13] for the classification of medical images; however, these models are not usually updated since they are created only when needed. Thus, it is not possible to improve such models because they lack procedures and feasibility to retrain the additional medical images. Additionally, creating models requires a large number of images that usually are registered images in a training dataset because training images for CNN are processed using supervised learning algorithms. Herein, we focus on additional learning and automatic learning for CT images because a current CT scanner has the ability to generate a large number of images per examination. Although there is an existing report [13] on the classification of CT images including contrast enhancement data, there are no reports on the classification of medical images based on the evaluation of the additional learning and automatic image learning system. This study aims to evaluate the overall accuracy of the additional learning and the automatic classification systems for CT images.
## 5 Summary of the Interoperability Tests
### More on Self-Testing  
According to the buildingSMART recommendations for IFC certification, self-tests are suggested but not mandatory. However, it is worth considering that all the information included in the IFC schema was correctly read and interpreted by the software that originated the IFC file. On the other hand, many self-tests performed in the study failed because the mapping procedure gave different outputs in the case of repetitive self-tests.  
In order to shed light on this issue and collect all the entities of the IFC file and their attributes at once, the IFC File Analyzer Software by NIST [48] was adopted. The output of each run was a spreadsheet file organised in worksheets that collected each type of entity in the IFC file. Moreover, in a summary worksheet, the entities were grouped and indicated by colour (for example, the entities related to parametric profiles and extrusions were in green, the properties were in yellow, the representation type was in purple, and so on). In the following paragraph, the attention is placed on the results of the repetitive self-tests carried out using all the analysed software packages, looking at the different mapping structures resulting from the exchange tasks.  
Revit and Edificius were the software packages that, compared with the others, mapped the entities in different ways during the repetitive self-tests (completing four self-tests before stability was reached in the list of entities appearing in the IFC file). For both systems, the number of IFC entities increased, as well as the file size. In particular, in Revit, for both IFC2x3 and IFC4, _IfcBeam_ only appeared in the first output IFC file, while it was converted into a much more general _IfcBuildingElementProxy_ entity in the later exchanges with the associated _IfcMappedItem_ representation, which enabled a representation to be used as a representation item in one or more other representations (ISO/CD 10303-43:1992). The property information captured in _IfcPropertySet_ also changed and increased, as well as the _IfcAxis2 Placement3D_ entities related to a local coordinate system. The columns were originally classified as _IfcColumn_, and no changes occurred in the subsequent tests. In Edificius, the original _IfcColumn_ and _IfcBeam_ entities were all converted into _IfcBuildingElementProxy_ after the first self-test and were mapped in the same way in the repetitive tests, but the overall number of entities was higher than in other systems, as well as the file size. In ArchiCAD, the self-test worked well, as the mapping stayed invariant for both IFC 2x3 and IFC 4. In AllPlan, the mapping preserved the main information, but the file size increased for the additional data on _IfcCartesianPoint_ in IFC 2x3 alone (indicating a different decomposition of the domain), while no issue appeared with IFC 4. For all the CAE packages, no changes occurred during the repetitive self-tests.
## 3 Methods
### Evaluation Metrics  
The metrics used for evaluation of the regression models are the mean square error, mean absolute error, root mean square error, mean absolute percentage error, R-square, and correlation coefficient [35].  
* Mean square error (\(MSE\)) [35]: \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\] (13) where \(n\) is the number of data points in the dataset, \(y_{i}\) represents the actual target value of the \(i\)-th data point, and \(\hat{y}_{i}\) represents the predicted value of the \(i\)-th data point.
* Mean absolute error (\(MAE\)) [35]: \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\] (14) where \(|\cdot|\) denotes the absolute value.
* Root mean square error (\(RMSE\)) [35]: \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}\] (15)
* Mean absolute percentage error (\(MAPE\)) [35]: \[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{|y_{i}|}\times 100\] (16)
* R-square (\(R^{2}\)) [24]: \[R^{2}=1-\frac{SSE}{SST}\] (17) where \(SSE\) represents the sum of squared differences between the actual target values and the predicted values. \(SST\) represents the total sum of squares, which is the sum of squared differences between the actual target values and their mean.
* Correlation coefficient [36]: The correlation coefficient is given by the Pearson correlation coefficient: \[r=\frac{\sum_{i=1}^{n}(y_{i}-y_{mean})(\hat{y}_{i}-\hat{y}_{mean})}{\sqrt{ \sum_{i=1}^{n}(y_{i}-y_{mean})^{2}}\sqrt{\sum_{i=1}^{n}(\hat{y}_{i}-\hat{y}_{ mean})^{2}}}\] (18) where \(y_{mean}\) is the mean of the actual target values and \(\hat{y}_{mean}\) is the mean of the predicted values.