esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 6 Conclusions  
In this paper, we proposed a genetic-algorithm-based RSU deployment scheme, called GARSUD, which is capable of automatically determining the optimal RSU location to enhance warning message delivery in any given roadmap layout, ranging from regular to complex city map layouts. Simulation results, based on realistic traces and map layouts, demonstrate that GARSUD is able to reduce the warning notification time in different map layouts, including complex scenarios, under different vehicular densities, as well as when a different number of RSUs needs to be deployed. Additionally, GARSUD improves vehicular communication capabilities, especially in complex layouts since it increases the probabilities that warning messages can be received by emergency services.  
To the best of our knowledge, existing approaches are able to perform well under single vehicle density, as well as single street lane mobility models. Our proposal has been proven to pinpoint suitable solutions for a wide range of densities, vehicle traffic distributions and map layouts.  
In the future, we plan to deploy a real testbed in order to confirm the performance of our GARSUD approach in a real environment. In addition, we consider that our proposal could be extrapolated by other applications in intelligent transportation systems, especially those that require the use of infrastructure elements.  
This work was partially supported by the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientenda a los Retos de la Sociedad, Proyectos I+D+1 2014, Spain, under Grant TEC2014-52690-R, by the Government of Aragon and the European Social Fund (T91 Research Group), and by the Ministerio de Educacion, Cultura y Deporte within the framework of Programa Estatal de Promocion del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016.  
All authors contributed extensively to the work presented in this paper, i.e., to the genetic algorithm-based approach, to the research concept, to the experiment design and to analyzing the experimental results. In particular, [PERSON], [PERSON] and [PERSON] proposed the main idea, [PERSON] and [PERSON] designed the system and performed the experiments. [PERSON], [PERSON] and [PERSON] discussed the results and implications, driving the required improvements to the research aspects, as well as contributing to the manuscript at all stages. All authors participated in revising the manuscript.  
The authors declare no conflict of interest.
# Mobile Robotics in Agricultural Operations:
## 5 Conclusions  
This research sets out the theoretical foundation for understanding the planning of mobile robots, particularly within the field of agriculture. A \"narrative\" review approach was adopted to first provide the basic terminology over the technical aspects of mobile robots used in autonomous operations, and then the fundamental planning aspects of mobile robots in agricultural environments were identified. The main gap in the current mission-planning-related literature refers to the limited number of studies following a system engineering approach. More specifically, the majority of the studies test the performance of existing area decomposition and vehicle routing algorithm aspects, mainly in a simulation environment, and neglect the structure and specifications of the intended operations and tasks that have to be performed by the available mobile robot. Consequently, isolated planning approaches have been developed. Furthermore, in the few experimental studies, the trials are limited to few robot functionalities and do not integrally consider the deployment of a spectrum of field operations.  
This paper put up two RQs. First, RQ#1 concerns the basic and concise terminology over the technical aspects of mobile robots in autonomous operations and was answered with a critical analysis of the selected works. Our research group's expertise and experience on mobile robots in agriculture along with research evidence reveal that most of the technical terms are used interchangeably, thus not enabling effective communication among stakeholders in the field. Second, to tackle RQ#2, we synthesized evidence from extant studies, and we proposed seven attributes of planning, namely: high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion-task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.  
This research aimed at supporting the planning of mobile robots in agriculture through identifying and classifying a set of technical terms and basic planning attributes. We envisage that the proposed clarification will inform analytical tools used for the effective design of autonomous operations in agriculture [67], hence further enabling the digital transformations in the extended agri-food supply networks [68; 69].  
Conceptualization, D.B. and C.G.S.; Methodology, N.T. and V.M.; Investigation, V.M. and D.K.; Writing-Original Draft Preparation, V.M.; N.T.; D.K.; S.P.; D.B. and C.G.S.; Writing-Review and Editing, N.T.; D.B. and S.P.; L.B., Visualization, N.T.; V.M. and D.K.; Supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research has been supported by the Project: \"Human-Robot Synergetic Logistics for High Value Crops\" (project acronym: SYNERGIE), funded by the General Secretariat for Research and Technology (GSRT) under reference no. 2386.  
The authors declare no conflict of interest.
## 6 Discussion  
The non-food crop recognition model proposed in this study achieves high-precision classification and robust cross-region generalization in large-scale cropland scenarios by deeply fusing multi-source temporal remote-sensing data with crop phenology information. In the temporal-fusion component, a Transformer-based encoder leverages self-attention to capture global dependencies throughout the crop growth cycle, overcoming the CNN's inherent bias toward local features. For instance, the model discriminates the distinct temporal signatures of rapeseed during regreening and maturity, yielding a marked sensitivity to critical phenological stages (F1-score = 74.84%). Concurrently, the MTAF module dynamically weights and integrates Sentinel-1 (SAR) and Sentinel-2 (optical) data, addressing the frequent optical-data gaps in southern China's rainy season. Specifically, during June-August 2022 in Hunan Province, Sentinel-1 VV polarization compensated for cloud-masked optical pixels, boosting classification IoU by 12.6% in missing-data areas. Moreover, as illustrated in Figure 9, the PAM module not only enhances overall performance but, more importantly, transcends the limitations of fixed temporal windows by dynamically weighting key growth periods. Experimental results show that PAM increases recall by 9.3% during the flowering stage--when spectral distinctions are most pronounced--thereby reducing misclassification due to phenological overlap. In terms of cross-domain adaptability, the model attains an mIoU of 55.94% on the Canadian Cropland Dataset (CCD Set). Although this represents a 5.44% performance drop due to region-specific phenological differences, it still outperforms mainstream approaches (e.g., MTAF-TST at 53.12%), confirming that the phenology-window mechanism substantially promotes consistent feature representation across diverse agro-ecological zones.
# Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network
## 3 Surface Defect Detection Model
### Surface Defect Recognition Model of CNN  
Traditional machine vision and deep learning can be used to realise automatic detection of copper strip surface defects. If traditional machine vision is used to extract and classify the defect feature, then endowing the model with high detection accuracy and strong capability of generalisation and anti-interference is difficult. At present, the convolution neural network (CNN) has made outstanding application performance in many engineering fields, such as the defect detection of the steel strip surface, fault diagnosis, and pattern recognition, with the rapid development of artificial intelligence and deep learning theory [23; 24; 25; 26]. Therefore, this paper establishes the intelligent recognition model of copper strip surface defect based on the deep CNN, according to the defect images collected from the production site. The general structure of the model is shown in Figure 5. This structure mainly comprises an input layer of the image data, multiple convolutional layer, multiple pooling layer, fully connected neural network layer, and an output layer; the specific number of convolution and pooling layers should be determined in accordance with the specific problem. The feature information of surface defect is extracted after the multiple operations of convolution, pooling, and non-linear activation function mapping on the surface defect image of the copper strip. Finally, the probability of each type for a certain image is calculated through fully connected and output layers and realises defects classification.
## 3 System Modeling
### Coordinate Conversion
Since point \(O^{\prime}\) and point \(O\) are coincident, the translation vector from coordinate frame \(O^{\prime}\) to coordinate frame \(O\) is a zero vector. \(\boldsymbol{R}_{O^{\prime}}^{O}\) is calculated according to the angle \(\varphi_{\rm offset}\), while \(\varphi_{\rm offset}\) shows the deflection angle of the coordinate frame \(O^{\prime}\) relative to the coordinate frame \(O\) in the \(Z\)-axis direction. Since the direction of the deflection is along the axis \(Z^{\prime}\), there is no rotation component in other directions, \(\boldsymbol{R}_{O^{\prime}}^{O}\) can be calculated as:  
\[\boldsymbol{R}_{O^{\prime}}^{O}=\left[ \begin{array}{ccc}c(\varphi_{\rm offset })&-s(\varphi_{\rm offset})&0\\ s(\varphi_{\rm offset})&c(\varphi_{\rm offset})&0\\ 0&0&1\end{array} \right] \tag{9}\]  
The remaining problem is to calculate the angle \(\varphi_{\rm offset}\). According to the above description, it can be known that for each 3D scan of a rotating 2D lidar, the value of angle \(\varphi_{\rm offset}\) is not constant. If we can accurately calculate the value of \(\varphi_{\rm offset}\) for each 3D scan, then we can eliminate the uncertain attitude error of the 3D point cloud according to Equations (8) and (9). As a result of which, we can get the 3D point cloud relative to coordinate frame \(O\) accurately--both in shape and attitude. The calculation method of the angle \(\varphi_{\rm offset}\) for each 3D scan will be described in more detail in the next section.
## 5 Scenario Descriptions
### Scenario \(\alpha\): Essential and Unessential Communication with the Internet  
In general, we can distinguish two types of devices based on how tied to the Internet they are: (a) devices that can and (b) devices that cannot be used without an Internet connection [16]. Digging deeper into devices that rely on Internet connectivity, we can see that they communicate with both mandatory (e.g., updates) and not mandatory services (e.g., crush reports).  
This scenario therefore focuses on this particular aspect, where a _Smart Home Device_ might communicate with multiple servers on the Internet, some of which contribute to its main functionality and some of which do not; the latter communication would therefore be dispensable.  
While in reality both categories might comprise multiple servers, the scenario abstracts from this by replacing them with two distinct servers, each of them representing one of the two groups. The first server, representing the servers to which functionality-related messages are sent, is called _Essential Server_. In contrast, the _Unessential Server_ represents the group of servers that only handle communication which is not contributing to the user experience. Notice that a device might communicate with the same server for multiple purposes, possibly including both functional and non-functional communication. The _Smart Home Device_ is in this scenario the sole appliance inside the user's local network in order to put the focus on the distribution of outgoing connections. This scenario is depicted in Figure 7. Notice that the information flow between the _Smart Home Device_ and the _Unessential Server_ is unidirectional. This reflects the assumption that the communication is merely used to collect information (e.g., crash reports, logs, usage statistics) from the device.
## 5 Discussion
### Prediction of Potential Slope Failure  
A dynamic simulation of possible landslide failures is required to analyze possible disaster risks. Therefore, the numerical simulation method (LA) was used to predict the failure process of the Xiaomojiu landslide. Its principle is described in Section 3.2. By combining the extent of landslide deformation and geomorphological features, we (Figure 5) simulated the kinematic and accumulation characteristics of the landslide. According to the drilling data [31], the thickness of the Xiaomojiu landslide was 40-80 m, with an average of 60 m. The volume was estimated to be 51,600,000 m\({}^{3}\). Thus, the average thickness of the sliding body was set to 60 m. The landslide comprises metamorphic rock and gravel soil, so we set the lateral pressure ratio and internal friction angle to 35\({}^{\circ}\), which is somewhat larger than the friction angle of the soil material [32]. The cohesion was calculated as \(\text{hc}=\text{C}/\text{pg}\approx 0.8\), and we set a small value of 0.05 as the lateral pressure ratio (k).  
Figure 15 shows the dynamic failure process of the Xiaomojiu landslide. The whole motion lasted about 80 s. At 0-20 s, the rock mass begins to slide down the sliding face as gravity's potential energy is converted into kinetic energy. Approximately 40 s after initiation, the sliding body passes through the river, with the mountain on the opposite bank blocking the leading edge of the rock. At 40-60 s, the sliding body gradually builds up on the riverbed. At 80 s, most of the rock stops moving and is deposited at the foot of the slope, blocking the river and forming a barrier dam. The simulation results indicated that the Xiaomojiu landslide could collapse and form a barrier dam at the Jinsha River with an area of about 450,000 m\({}^{2}\). The accumulation distribution shows that the influence area exceeds 2 km from the riverbank, and the maximum thickness of the accumulation was about 150 m.  
The numerical simulation results showed the landslide's future trends, and the initial displacement changes reflect the unstable damage in the soft zone of the landslide. We compared the numerical simulation results with the InSAR spatial deformation field. Figure 16 shows the average deformation field of the landslide and the velocity within 5 s of the initial  
Figure 15: Failure simulation of the unstable rock mass using LA model.  
simulated damage. Our results showed that the large deformation region indicated by InSAR is consistent with the initial instability damage region in the numerical simulation results, proving that simulation results are reliable and provide scientific guidance for pre-disaster prediction and early warnings of landslides.
## 3 Results and Discussion
### Comparison of Different Types of Proponents  
The results of the sphericity, apparent density, bulk density, and crush resistance tests of various types of proponents are presented in Table 1. The sphericity of poly(St-\(\omega\)-MMA)/FA was close to 1, which is better than the proponents based on walnut shells (0.65) [34], quartz sand (0.68) [4], and ceramic (0.8) [35]. Higher sphericity leads to higher proppant-pack porosity, which could increase the fracture conductivity of proppants. According to the literature, there are several ways to decrease the proppant density. The first method is to select a material with a lower density, such as walnut shells and organic polymeric materials [34]. Even though such materials would penetrate deeper into the fracture,  
Figure 6: Apparent density and bulk density of poly(St-\(\omega\)-MMA)/FA microspheres.  
Figure 7: Crushing rate at 52 MPa of poly(St-\(\omega\)-MMA)/FA microspheres.  
their low sphericity or strength limits their applicability to relatively low closure pressures. The second method to make lightweight proppants is to introduce a microporous structure into the proppant, such as that in resin-coated porous ceramic [36] and inorganic polymer proppants [37]. These advanced ceramic proppants have a higher strength/weight ratio than conventional ceramic proppants. However, too many holes will reduce the strength of the proppant. The third method of reducing the density of a proppant is coating [4; 38; 39; 34]. However, due to the small content of resin on the proppant surface, it has little contribution to reducing the density of the proppant. The last way to reduce the density of a proppant is by doping inorganic minerals into organic polymers. Suspension polymerization is an effective method for preparing organic-inorganic composite microspheres. Several typical ULW proppants, such as PMMA/AG [19], PMMA/FA [21], and PS/graphite [23] composite microspheres, were prepared by suspension polymerization and used as ULW proppants in our previous study. As with the poly(St-_co_-MMA)/FA ultra-lightweight proppant of this work, all of the aforementioned proppants had a high sphericity (>0.9), ultra-low density, and good compressive strength. The studied proppant has a performance comparable to that of traditional proppants, which allows the proppant to reach the width of the fracture, travel deeper, and improve the conductivity.
## 4 Experimental Process and Result Analysis
### Ablation Experiment  
To verify the effectiveness of the MHA mechanism and gating operation, ablation experiments are performed. The ablation experiment consists of four groups of experimental objects. The control variable method was adopted in the ablation experiment. Four groups of experimental subjects were tested separately under the premise that other parameters remained unchanged.  
The MLP model without the MHA mechanism and the gating operation has difficulty effectively processing different features in the data due to the lack of feature importance screening ability. Therefore, it can be seen from Table 5 that the RMSE values obtained by the MLP model with the MHA mechanism and the gating operation are smaller than those of the MLP model without the MHA mechanism and the gating operation on both datasets. This shows that the proposed model gives full play to the advantages of the MHA mechanism and the gating operation, and can better learn and extract features in the data and obtain more accurate estimation accuracy. In addition, from the experimental results of the MLP model with only the gating operation and the MLP model with only the MHA  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **LSTM [30]** & **CNN [32]** & **Ours** \\ \hline RMSE value on the first dataset & 0.393 & 0.288 & 0.239 \\ RMSE value on the second dataset & 0.039 & 0.033 & 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experimental results of the proposed method and two state-of-the-art methods (unit: m).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **RMSE Value on the First, Dataset** & **RMSE Value on the Second, Dataset** \\ \hline EKF & 1.324 & 0.118 \\ LR & 0.864 & 0.071 \\ SVM & 0.618 & 0.065 \\ DT & 0.527 & 0.060 \\ RF & 0.473 & 0.047 \\ Ours & 0.239 & 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experimental results of the proposed method and five common methods (unit: m).  
mechanism, it can be seen that the MHA mechanism improves the model performance more than the gating operation.
# Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
## 3 Method
### Feature Enhancement Module  
For convolution, the setting of the convolution kernel limits the size of the perceptual field, resulting in a network that often requires multiple layers of stacking for focusing on the entire feature map. The advantage of self-attention is that its attention is global, and it can obtain the global spatial information of the feature map by simple query and assignment. The feature enhancement module does feature fusion and self-attention calculation on the basis of the four features, \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\), obtained from the FPN network, to gain a global feature of the feature map itself. This global feature was added with \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\), respectively, and then fused. The final enhanced feature \(E\) had both the local features extracted by the CNN network and the global features obtained by the self-attention calculation.  
As shown in Figure 2, the four features \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\) were fused by the concat operation. Then, a 3 \(\times\) 3 convolution was appended for dimensional transformation to obtain the fused features \(F\). After that, the self-attention calculation was performed, which was calculated by using shifted window to obtain a self-attention feature \(S\). Then, \(P_{2}\), \(P_{3}\), \(P_{4}\), and \(P_{5}\) were summed with \(S\) by element, respectively, and \(E_{2}\), \(E_{3}\), \(E_{4}\), and \(E_{5}\) were correspondingly obtained. A concat operation was performed on \(E_{2}\), \(E_{3}\), \(E_{4}\), and \(E_{5}\) for feature fusion to obtain a final enhanced feature \(E\). The specific calculation formulae are given in Equations (7)-(10):  
\[F=conv3(concat(P_{2},P_{3},P_{4},P_{5})), \tag{7}\]  
\[S=SA(F), \tag{8}\]  
\[E_{j}=S\oplus P_{j}, \tag{9}\]  
\[E=concat(E_{2},E_{3},E_{4},E_{5}), \tag{10}\]  
where \(conv3\) denotes 3 \(\times\) 3 convolution; \(concat\) denotes feature association; \(SA\) denotes self-attention calculation; \(j\) = 2, 3, 4, and 5.  
Figure 2: Module of feature enhancement.