esa-sceva/satcom-chunk-collection huggingface dataset:
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 4 Evaluation
### Simulation Scenario Description  
We considered the Veins and OMNeT++ framework to evaluate the implemented mechanics. Veins is an open source framework that implements the standard IEEE 802.11p protocol stack for inter-vehicle communication and an obstacle model for signal attenuation. For traffic and vehicle mobility simulation, we employed the Simulation of Urban MObility (SUMO), which is an open source traffic simulator, to model and manipulate objects in the road scenario. This allows us to reproduce the desired vehicle movements with random cruise speed and V2I interactions according to empirical data. We conducted 33 simulation runs with different randomly generated seeds, and the results present the values with a confidence interval of 95%.  
We conducted simulations in a Manhattan grid scenario to evaluate the impacts of vehicular mobility in the CP mechanisms. The scenario is a 1 km\({}^{2}\) fragment of Manhattan, USA, with several blocks and two-way streets so that the vehicles can move in opposite directions. The vehicle densities varied from 100 to 200 vehicles/km\({}^{2}\). Vehicles in the simulation have the exact dimensions, mean speed, and standard deviation speed. In addition, we applied a random mobility model to produce unique routes for each vehicle based on replication and density using a random mobility model. As a result, vehicles  
Figure 4: VILE perception phase.  
in the simulation share the same characteristics, such as exact size, mean, and standard deviation speed. All the main simulation parameters are summarized in Table 1. In our simulations, we chose the sensors' parameters according to others approaches that evaluate the CP in CAVs scenario [22; 32]. Thus, the FoV of 360\({}^{\circ}\) was chosen as well as the range of 150 m to generate CP messages with more detections in the scenario.  
In terms of collective perception evaluation, the number of messages, channel busy ratio, and packet delivery ratio are some metrics that address the network costs of the CP service but are not enough to assess the quality level of the perception for the CAVs applications because they fail to capture redundancy aspects of data content related to sensors' perception [32]. For example, the number of duplicate detected objects is based on how often the sensor has caught the same object, such as people or CAVs. Therefore, a higher redundancy value means worse channel utilization. We also evaluate the number of detected objects for each vehicle on the road. We measure the average number of unique, shared perceived objects for each CAV over its simulation time to calculate the absolute number of objects detected. On the other hand, the number of CPM messages generated and received measures the network costs for each approach based on the total number of packets generated and sent through the shared channel. The CPM-generated values closer to zero mean that the techniques use fewer messages in the shared communication channel to transmit the objects detected.
## 1 Introduction  
In welded joints, cracks typically initiate either at imperfections or local stress concentrations. Thus, the fatigue strength and structural integrity is directly linked to the magnitude of distribution of stress concentrations along the seam of welds. In order to improve weld quality by in-production comparisons, it is important to be able to detect and characterize local stress concentrations. In recent years, significant advances have been achieved in the field of automated weld geometry measurements techniques to replace traditional manual weld inspections. In the past, the results of weld inspections were directly linked to the qualification of the performing person [1], which makes unbiased comparisons between different inspections basically impossible. In addition, manual weld inspections are very time consuming and are often performed only on single sections and not on the complete welded joint.  
Recent advances in automated measurement techniques allow virtually unrestricted numbers of inspections by optical 3D-measurements of weld profiles. Such measurementsare typically based on the digitized surfaces of welds; however, further steps are required to allow comparisons of weld qualities of different welding processes and manufacturers. This includes (i) the development of tools to determine weld geometries from digitalized weld surfaces quickly and reliably, (ii) methods to transfer weld geometries into indicators that describe the local stress concentration or notch effect of welded joints, (iii) the establishment of the relation between these quantities and actual fatigue strength, and (iv) the verification and testing of the developed tools [2]. One important step in this context is a deeper understanding of statistical distributions of weld geometry parameters and stress concentrations along weld seams to use these methods in a reliable and fast manner. Thus, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an _Artificial Neural Network_ (ANN) is created from 945 finite element simulations to determine _Stress Concentration Factors_ (SCFs) at arc- and laser-welded butt joints with sheet thickness ranging from 1 mm to 100 mm.  
Following this introduction, this study is organized as follows. In Section 2, the state of the art on characterization of weld geometries and stress concentrations is introduced. Section 3 describes the finite element modelling and the sampling strategy to determine SCF at butt joint weld seams. The generation of the deep neural network--including the architecture and the training--is described in Section 4. A statistical characterization of weld geometries and stress concentrations along butt joint weld seams made by different welding processes is given in Section 5. Finally, the main findings of this study are discussed in Section 6.
## 3 Results and Discussions
### Case Study: Metro Manila, Philippines
By conducting a parametric analysis and presenting the resulting data in Table 5, this study contributes to our understanding of the seismic hazards this region faces and emphasizes the significance of proactive risk management and disaster preparedness measures. A reference PGA map of Metro Manila at an expected magnitude 7.5 M earthquake is shown in Figure 11.  
This study's map of estimated peak ground acceleration (PGA) for Metro Manila is consistent with the results of a previous study by [PERSON] and [PERSON] (2022) [53]. In addition to assessing the amplification of seismic waves based on soil properties, their study produced a seismic hazard map for Metro Manila with a 10% probability of exceedance (POE). In this study, the PGA values for Metro Manila and other important geotechnical parameters were used to calculate the factor of safety (FS) against liquefaction. The probability of liquefaction (PL) was then calculated, and the resulting liquefaction severity index (LSI) was used to assess the liquefaction hazard of various zones within Metro Manila. As depicted in Figure 11, a liquefaction hazard assessment was conducted for an anticipated magnitude of 7.50 on the Marikina West Valley Fault, and a map was produced using the methodology developed by [PERSON] et al. (2004) [1]. This map provides  
Figure 11: PGA map of Metro Manila at expected magnitude 7.5 M earthquake. vital information regarding the likelihood of liquefaction and associated risks in various areas of Metro Manila. It is evident that the susceptibility of a layer to liquefaction is determined by its N-value, and layers with N-values greater than 30 are not considered susceptible to liquefaction, shown in Figure 12. In addition, the first 20 m of the investigated area in these cities consists of clayey soils that are non-liquefiable. The liquefaction severity index varies between various Metro Manila cities. There is a mixture of low, high, and very high values in Caloocan, Las Pinas, and Quezon City. Some cities on the Central Plateau have non-liquefiable areas due to the presence of sand in the shallow layers. However, the risk of liquefaction is greater in Makati, Mandaluyong, and San Juan near the west. In contrast, Marikina, Muntinlupa, Paranaque, Pasig, Pateros, Taguig, and Valenzuela have low, high, and extremely high liquefaction severity index values. The cities on both the Central Plateau and the Plain have a range of low, high, and extremely high liquefaction severity index values. Malabon, Manila, Navotas, and Pasay are, however, the most common locations with extremely high liquefaction severity index values. Consequently, coastal cities have an exceptionally high liquefaction severity index, making them more susceptible to liquefaction during earthquakes. The resulting liquefaction potential map concurs with local findings, which determined that the coastal and plateau cities of Metro Manila have the highest liquefaction potential. Figure 12: LSI Map of Metro Manila, Philippines.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Experimental Setup  
This section presents the main features of the base experiment for our evaluation. Table 1 summarizes the base parameters of the experimental setup, which will be explained in the following paragraphs.  
Communication is modeled inside the nodes by four top layers and one omnipresent layer defined inside the scenario. In combination, all of the layers resemble the OSI model from bottom to top: Physical layer (PHY), Link layer (MAC), Network layer, and Transport layer.  
With the revolution in accessibility to space started by initiatives such as the CubeSat standard, major global organizations such as \"Thales Alenia Space\" are developing projects in the emerging IoT paradigm. \"Project ONION\" [25] is one of the most representative projects that includes a definition of a spacecraft platform for different resources depending on the mass characteristics of the satellite.  
ONION defines three types of platforms: micro (maximum of 100 kg), heavy (between 100 kg and 1000 kg), and conventional (1000 kg and more). In addition, it describes three types of radio transceivers: small, medium, and heavy, all with a radio range of 1500 km. This work modeled all the satellites as micro platforms with medium transceivers. Thus, the PHY layer of each node is configured to simulate a radio transceiver with a data rate of 750 kbps and a maximum radio range of 1500 km.  
As this work focuses on evaluating the performance of the routing strategies in ad hoc IoT networks, basic MAC and transport layers are used in the experimental setup. The MAC layer explicitly provides packet encapsulation, decapsulation, and optional out-of-band acknowledgment. The Transport layer uses datagrams and a simple connectionless approach without handshakes, acknowledgments, or retransmissions (e.g., as in UDP).  
The Network layer provides a unique identifier to each node. Additionally, a \"Routing\" module is implemented in this layer. Thus, the rules followed by a particular routing protocol are implemented in this module for each node. As previously mentioned, in this contribution, we implemented the OLSR routing protocol in this module to evaluate the proactive routing approach. Similarly, the AODV routing protocol was implementedto evaluate the reactive routing approach. Once implemented, the protocols could be interchanged in the experimental testbed whenever needed.
# FocusedDropout for Convolutional Neural Network
## 5 Experimental Section
### Evaluation of FocusedDropout
#### 5.2.2 Tiny ImageNet  
Tiny ImageNet dataset is a subset of the ImageNet [44] dataset with 200 classes. Each class has 500 training images, 50 validation images, and 50 test images. All images are of 64 \(\times\) 64 resolution. The test-set label is not publicly available, so we use the validation set as a test set for all the experiments on Tiny ImageNet, following the common practice. We apply different dropout methods to ResNet-110 and compare the results with FocusedDropout. As shown in Table 3, FocusedDropout achieves a significant 2.16% improvement over the base model and has a larger gain compared to other dropout methods.  
To get an intuitive grasp of how FocusedDropout makes the network focus more on target, we use class activation mapping (CAM) [45] to visualize the activation units of ResNet-110 trained by Tiny ImageNet, as shown in Figure 5, from which we observe that the network trained with FocusedDropout can more accurately identify the target's location and contour; additionally, the identification of the main target and the secondary target in the image is clearer. This proves that our approach enables the network to focus more on the target. It also shows that making the network more goal oriented helps improve the performance of the network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **ResNet-20** & **ResNet-56** & **ResNet-110** & **VggNet-19** & **DenseNet-100** & **WRN-28** \\ \hline Dropout & 69.81 \(\pm\) 0.12 & 73.81 \(\pm\) 0.27 & 74.69 \(\pm\) 0.33 & 73.01 \(\pm\) 0.21 & 77.45 \(\pm\) 0.12 & 81.21 \(\pm\) 0.11 \\ SpatialDropout & 69.71 \(\pm\) 0.05 & 74.38 \(\pm\) 0.17 & 74.76 \(\pm\) 0.12 & 72.84 \(\pm\) 0.21 & 77.97 \(\pm\) 0.16 & 81.40 \(\pm\) 0.30 \\ DropBlock & 70.03 \(\pm\) 0.11 & 73.92 \(\pm\) 0.10 & 74.92 \(\pm\) 0.07 & 73.01 \(\pm\) 0.04 & 77.33 \(\pm\) 0.09 & 81.15 \(\pm\) 0.09 \\ \hline FocusedDropout & **70.27 \(\pm\) 0.06** & **74.97 \(\pm\) 0.16** & **76.43 \(\pm\) 0.17** & **73.91 \(\pm\)0.19** & **78.35 \(\pm\) 0.16** & **81.90 \(\pm\) 0.11** \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 5: Class activation mapping (CAM) [45] for ResNet-110 model trained without any method, trained with DropBlock, and trained with FocusedDropout. The model trained with our method can accurately identify the target’s location and contour.
## 5 Experimental Results and Discussion and Analysis
### Ablation Study  
In order to further prove the effectiveness of the two modules (CPDA and MCD), we conducted ablation studies on the two modules on MS COCO2017 and SVRDD data sets using YOLOv8n as the baseline algorithm, and the results of the experiments are shown in Table 4. The Recall(R) and mAP50 of the original YOLOv8n model are 59.3% and 64.5%, respectively, whereas DAPONet, although the Precision decreases slightly after only the CPDA module is introduced, the Recall(R) improves from 59.3% to 62.1%, which is an increase of nearly 2.8%, and the mAP50 improves from 64.5 to 66.1, with an increased by nearly 1.6%, while Params, FLOPs, and Model Size are all reduced, especially FLOPs are reduced from 8.1 to 4.6, which is about 41%. This verifies the efficient multi-scale extraction capability of the CPDA module. When only the MCD module is introduced into DAPONet, the Recall(R) and mAP50 of the model are also improved, in which the Recall(R) increases from 59.3% to 60.8%, which is nearly 1.5%, and the mAP50 increases from 64.5 to 65.2, which is nearly 0.7%, and the reduction in the Params, FLOPs, and Model Size are also achieved. Thus, the efficient downsampling effect of the MCD module is proved. When the two are combined, the model performance reaches the best, which not only exceeds the benchmark model YOLOv8n in precision from 70.7% to 71.6%, but also improves the recall from 59.3% to 66.6%, which is an increase of about 7.3%, and the mAP50 and mAP50-95 are increased from 64.5, 37.8, 70.1%, and 42.8%, which are significant increases, respectively. increase. And Params, FLOPs, and Model Size are all reduced significantly, in which FLOPs are reduced from 8.1 to 1.7, which is about 79%. It can be inferred that both CPDA and MCD play an indispensable role in DAPONet. The combination of the two makes DAPONet able to effectively capture the multi-scale features of the detected targets, and at the same time, realize the efficient processing of multi-scale features and the organic fusion of global and local information, which in turn significantly improves the detection rate while guaranteeing the accuracy of the model.
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 2 Materials and Methods
### Sowing Operation and Crop Management  
Sowing rate of 43 kg ha\({}^{-1}\) of a winter ryegrass (_Lolium multiform_ Lam.) under no-till farming took place in mid-October after irrigation, weed control of a fixed rate of a glyphosate-based herbicide with 3.00 l ha\({}^{-1}\), and a basal dressing of 150 kg ha\({}^{-1}\) NPK 7-21-21 fertilizer. During tillering stage--Moment 1 (M1)--the field was sampled at one point per hectare, approximately. Considering the similar soil conditions of the total area under study and the most commonly used techniques of crop management, crop biomass was evaluated under three different grass weed controls: no herbicide application (Plot I), VRAof 0.5 to 0.75 l ha\({}^{-1}\) of post-emergence control of broad-leaved weeds using Florasulam (Plot II), and fixed rate application of 0.75 l ha\({}^{-1}\) of the same herbicide (Plot III). Figure 1 shows the delimitation areas of the three plots under study and sampling points.  
Herbicide rate was defined considering grass weed growth stage and total percentage of plants per sampling area based on a segmented image. A three-point mounted sprayer, Amazone UF, with a 12 m spray bar width and a New Holland T5 120 tractor, both ISOBUS compatible, applied the prescription map recorded in a shape file folder.  
Twenty-five days after the herbicide application--Moment 2 (M2)--condition of grass weeds and yield of fresh matter (FM) and dry matter (DM) of the fodder crop were determined. The collected samples, with one sample made up of three sub-samples, were weighed to determine the FM production per hectare, and subsamples in small paper bags were kept at 65 \({}^{\circ}\)C until they reached constant weight to determine forage moisture content and DM content, according to the [PERSON] method [21].
## 5 Numerical Experiments and Discussion
### Estimating \(\mathbf{H},\ \mathbf{p},\ \mathbf{q}\)  
In order to implement the proposed technique, WSNs and IOTs should have flexible network design such that they can employ different routing protocols alternatively during their operations. The advancements in network softwarization could facilitate the existence of such networks. In [48], the authors surveyed the softwarization of Unmanned Aerial Vehicle (UAV) routing, while [49] presented the existing literature on AI-enabled routing protocols for UAV networks. Other applications of softwarization include, but are not limited to, Routing algorithm optimization in software-defined network Wide Area Network (WAN) [50], integrating Multi-path TCP (MPTCP), and Segment Routing (SR) paradigms over SDN/NFV [51].  
Once such networks are established, \(\mathbf{H},\ \mathbf{p},\ \mathbf{q}\) can be easily obtained either by developing a simulation model or extracting empirical data from emulated networks. The simulation and emulation models should have the ability to estimate the survival times, latency, and energy for a pair of routing and attack mechanisms. Thus, for a given network under consideration, the data \(\mathbf{H},\ \mathbf{p},\ \mathbf{q}\) should be estimated via simulation model a priori by the WSN manager or the decision maker. A parametric estimation of \(\mathbf{H},\ \mathbf{p},\ \mathbf{q}\) can be a future research direction.
## 4 Results
### Internal Damage  
The internal damage in the CFRP laminate can be non-destructively evaluated with an ultrasonic scanning device (KSI V400E, Germany). The frequency of the ultrasonic wave was 40 MHz, and the pulse-echo mode was used for scanning. Ultrasound C-scan, which is sensitive to delamination, is based on the reflection of ultrasonic energy from the intermediate interface. When the ultrasonic wave encounters the damaged interface, the reflected energy in the form of pulse-echo amplitude is different from the undamaged condition [33]. The reflected ultrasonic signals are converted into image signals with different gray values.  
Figure 7a,b show the internal damage morphology under the action of components C and D, respectively. These figures show that the damage expands from the fastener to the delamination boundary after the lightning strikes. The damage within the delamination boundary in Figure 7 consists of two zones. One is the thermal decomposition damage and ablation damage, where the material is pyrolyzed and vaporized (marked with dark red, hereinafter referred to as the decomposition damage). The other is speculated to be delamination (marked with blue), where the damage of the interlayer structure is caused by the internal pressure generated by the rapid expansion of pyrolysis gas [34].  
Figure 6: Damage in the fastener hole of the specimens after lightning strikes: (**a**) component C; (**b**) component D. (**c**) Specim cutting diagram for microscopic evaluation.  
For component C, the damage area inside the specimen is small, the damage lengths in the 0\({}^{\circ}\) direction and 90\({}^{\circ}\) directions are approximately 30 mm, and the difference between the two directions is small (in Figure 7a, the damage length in the 0\({}^{\circ}\) direction is 30.4 mm, whereas the damage length in the 90\({}^{\circ}\) direction is 29.8 mm). This finding indicates that a small amount of resin in the specimen has undergone pyrolysis and gasification during the lightning strike.  
For component D, a large area of damage is observed in the specimen, and the damage lengths in the 0\({}^{\circ}\) and 90\({}^{\circ}\) directions are greater than 100 mm, which indicates that during the lightning strike, component D induces more resin pyrolysis and gasification than component C. Moreover, an internal explosion occurs under component D. The rhomboid-shaped damage area might be caused by the orthogonal stacking structure of the specimen.