esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact and Accurate Scene Text Detector
## 2 Related Work
### Efficient Models  
In this paper, we consider three efficiency factors: inference time, FLOPS, and the number of parameters. Many previous studies using VGGnet or Resnet considered only part of these parameter, for example, only inference time was measured in [10; 14]. Such studies are efficient in terms of the inference time, however they are not suitable for mobile environments that may have severely limited resources (e.g., computational power, memory). Moreover, such mobile environments may not provide a Graphical Processing Unit (GPU), which makes arithmetic operations with large quantities much faster. There have been several studies that proposed efficient structures. In [23] a dense convolutional network (DenseNet) was proposed by modifying the bottleneck structure of Resnet. As shown in [24], DenseNet has about 600 M FLOPS, 103 milliseconds (ms) per data, and achieved a 39.9% top-1 error on the ImageNet data (224 \(\times\) 224 images). Further, [17] proposed MobileNet, which employs the separable convolution originally proposed in [25]. In [26], a channel shuffle operation was applied to separable convolution for better efficiency. In [18] MobileNetV2 was introduced, which is an improved version of MobileNet by employing an inverted residual block (IRB). The MobileNetV2 has about 3.4 M parameters, 600 M FLOPS, 110 ms per data, and achieved a 28.3% top-1 error on the ImageNetdata. Previous studies for scene text detection have mainly adopted VGGnet or Resnet as a backbone, but it is necessary to consider other models such as MobileNet2.  
The previous studies on scene text detection have a simple decoder such as standard convolutional layers. For example, in [10], the decoder simply consists of standard convolutional layers and pooling layers. In [12; 14], \(1\times 1\) convolutions and \(3\times 3\) convolutions form decoders. The decoder has a huge impact on the performance in terms of the effectiveness and the efficiency. Therefore, it is necessary to investigate better designs for decoders.
###### Abstract  
Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis. However, a large number of the fault-related components that exist in other sub-bands are ignored in the commonly used envelope demodulation methods. Based on a weighted-empirical mode decomposition (EMD) de-noising technique and time-frequency (TF) impulse envelope analysis, a multi-scale demodulation method is proposed for fault diagnosis. In the proposed method, EMD is first employed to divide the signal into some IMFs (intrinsic mode functions). Then, a new weighted-EMD de-noising technique is presented, and different weights are assigned to IMFs for construction according to their fault-related degrees; thus, the fault-unrelated components are suppressed to improve the signal-to-noise ratio (SNR). After that, continuous wavelet transformation (CWT) is adopted to obtain the time-frequency representation (TFR) of the de-noised signal. Subsequently, the fault-related components in the entire frequency range scale are calculated together, referring to the TF impulse envelope signal. Finally, a fault diagnosis result can be obtained after the fast Fourier transformation of the TF impulse envelope signal. The proposed method and three commonly used methods are applied to the fault diagnosis of a planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault. The results show that the proposed method can effectively detect gear faults and yields better performance than other methods.  
w +
Footnote †: journal: Applied Sciences  
_Applied Sciences_  
_Article_
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 1 Introduction
DenseNet, EfficientNetV2, and RegNet multi-classifiers achieved F1 scores of 0.984 when recognizing _Paspulum dilatatum_ Poir. and _Coperus rotundus_ L. However, the multiple stages involved in the detection produce a slower inference speed. Conversely, single-stage detectors do not provide for a region proposal step, and both object localization and classification are done in a single pipeline, exploiting a faster inference speed [22]. YOLO (You Only Look Once) is a single stage-deep learning algorithm that uses a convolution neural network for object detection. The object detection resulting from YOLO is formulated as a single regression problem by placing the bounding box coordinates into image pixels and then assigning class probabilities. Among the various DCNN object detection algorithms, YOLO achieves the detection in a single forward propagation, making it particularly suitable for real-time application. YOLO architectures achieved top performances in two official object detection datasets: Pascal VOC (visual object classes) [23] and Microsoft COCO (common objects in context) [24]. YOLO showed high precision and higher inference speed and represented state of the art in object detection algorithms. YOLO object detectors have shown potential for accurately detecting weeds in images and video data [25; 26; 27], however, it's worth noting that the YOLO performance for weed detection can be affected by lighting, background noise, and occlusion [28]. Therefore, optimizing the algorithm's parameters based on the specific use case and dataset is important to achieve the best results. Nevertheless, to date, only two research have been published on YOLO object detectors in turfgrasses: [PERSON] [29] assessed the feasibility of yolo detectors for detecting _T. officinal_ in Dermudagrass turf using YOLOv5. The model achieved 97% precision, 91% recall, and 41.2 frames per second to detect _T. officinal_ with Deepstrem on NVIDIA Jetson Nano 4 GB. [PERSON] et al. [28] assessed different object detectors, including YOLOv3, for _R. scabra_ detection in baihiagrass turfs managed with different drought stress. In this exploration it was found that neural networks such as AlexNet, GoogLeNet, and VGGNet demonstrated the highest performance levels. However, the application of YOLO for weed detection in turfgrass remains relatively uncharted territory, with only a handful of trials conducted thus far. Given the considerable challenge associated with achieving satisfactory weed detection in turfgrass using trained digital images, the focus of this study was to assess a range of YOLO model scales, specifically YOLOv5, YOLOv6, YOLOv7, and YOLOv8, for their efficacy in this task. The study aimed not only to evaluate these models' capacity for weed detection in turfgrass but also to compare their performance to identify the most effective approach.
# Multidimensional Study on Users' Evaluation of the KRAKEN Personal Data Sharing Platform
## 3 Results
### Compensation for Sharing Data and Data Valorisation  
We asked participants to the health pilot \"What type of compensation would you be looking for in exchange for your data products?\" Five participants replied that a monetary compensation would be appreciated for sharing their data products, since this would be the easiest way of managing a compensation for this type of products. Five participants expressed a preference for having a non-monetary form of compensation, mentioning more knowledge on their health condition, free access to services (e.g., counselling, premium contents on relevant health topics), gift cards or other credits. One participant thought that deciding for a type of compensation is a complex issue, since the platform should also compensate intermediary entities that may ensure the quality and standardization of the data products offered through the platform. One participant said they had no clear position on the type of compensation that should be provided. Participants were asked \"Do you feel comfortable in defining a price for your data product?\" In response, 11 participants expressed difficulties in defining a price for a data product, while just 1 participant said that they would be fine with defining a price for research purposes,by referring to other available datasets of health data that can be bought online. Two participants mentioned the need for having a general regulation helping to define prices for health data in a more standardized way. We also asked participants \"Would you need any support from for example an available tool in the platform to define or check if your price is sensible/correct?\" To this question, 12 participants expressed appreciation for having a tool or reference system in the platform supporting in defining a fair and sensible price for their data products. One participant proposed to develop a bidding system for data products, to promote quality of the data shared and incentives to share better data products. In general, there was large agreement among participants for the need of providing such a tool to help define prices and to valorize data products over time.  
We asked participants to the education pilot \"Would you seek compensation in exchange for securely sharing your data products?\" All three participants said they were not interested in sharing their education data for a monetary compensation. One participant had concerns with companies such as LinkedIn selling data entered by users in that platform. Another participant expressed interest for receiving a different form of compensation, for example free access to educational licenses of software.
# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 4 Evaluation and Results
### RSU DENM Messages Creation  
This section presents the DENM message generated by the RSU station. In Figure 9(a),d, the pedestrians were outside the crosswalk and the RSU-ITS-G5 module was not triggered. In Figure 9(c), the pedestrian detection module was activated but it did not identify a pedestrian and the RSU-ITS-G5 module was also not triggered. In Figure 9(b), the pedestrians were detected and the RSU-ITS-G5 module was triggered, creating and sending DENM messages.  
Table 1 exhibits the DENM message data used in the Facilities Layer and broadcasted to the OBU station.  
The header DENMV1 shows the event system timestamp (2,961,833,238,866), when the pedestrian was detected on the crosswalk with the crosswalk GPS coordinates (401,994,420-84,407,560). The OBU should consider the event relevant since it is at less than the relevanceDistance parameter (lessThan50 m). All traffic directions are affected through the relevanceTrafficDirection parameter (allTrafficDirections). The validation duration of the message is defined by the validityDuration parameter (oneSecondAfterDetection) and the type of the emitting device is defined by the stationType parameter (roadSideUnit). The informationQuality parameter defines the level of reliability of the information received (Highest) and the cause of the alert is defined by the eventType parameter (humanPresenceOnTheRoad) and the sub-cause (unavailable).  
At the Network layer the GeoNetworking header contains information of the message timestamp and the crosswalk GPS coordinates, latitude and longitude. The BTB-B header has the code 2002 because the packet is transported in a non-interactive way, with only the destination port included, since the communication is unidirectional.
# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 3 Method
### PointNet++ Architecture  
Innovations in 3D object recognition network architecture have continued to improve the accuracy on point cloud synthesis datasets. Despite this, PointNet++ performs equally well as the latest architectures after controlling factors independent of the network architecture via comprehensive comparison experiments [30]. Moreover, PointNet++ achieved the lowest error rate of classification results on ModelNet40-C among the existing network architectures as described in [11].  
As the first object recognition network that directly uses point clouds as input, PointNet attracted widespread attention and improvement. PointNet++ was proposed to improve its poor ability of information integration on the local regions of point clouds. PointNet++ acquires the local features of different regions with sampling and grouping layers. Then, the network integrates the local features of different levels into local-global feature through several step-by-step downsampling operations, named set abstraction.  
PointNet++ can be regarded as an encoder-decoder structure, as shown in Figure 3. Encoder is a downsampling process, which achieves multi-level downsampling by several set abstractions to obtain the local features of different regions.  
The set abstraction consists of three layers: Sampling layer, Grouping layer, and PointNet layer. The Sampling layer downsamples the point set with the farthest point sampling (FPS), reducing the input point set to a smaller size. The FPS algorithm makes the sampled points as far away from each other as possible, which has the benefit of making the downsampling results more uniform and allowing the network to cover as many points in space as possible. The sampling process can be understood as selecting N1 key points among N points. The Grouping layer takes each of these key points as the center and finds its neighbors of fixed size (size of K) to form a local neighborhood. The purpose of the grouping layer is to generate N1 local neighborhoods and output their local features. The PointNet layer uses a multi-layer perceptron and max-pooling to extract local features and can be considered a local feature learner. The last output of set abstraction can be considered a global feature. This also means that the network obtains the local-global features of different scales and levels at last.  
The decoder of the classification task transmits the local-global feature obtained by the encoder into several fully connected layers, and finally outputs the recognition result with SoftMax.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Other Considerations
#### 5.3.1 Training and Competency Requirements  
The least predictable component of any system is the human operator [88]. One way in which this can be more standardised is through formal training and competency requirements that form part of the overall operation. There may be different sources of training and competency requirements. Some requirements are stipulated by government regulators and form part of an application for a license or certification to carry out a certain activity. However, companies often also have internal training and competency requirements that form part of their safety assurance. When considering a BVLOS operation within a forest environment, there are some unique considerations for training. Firstly, the operating environment presents a number of difficulties that have already been outlined in this paper. Pilots and support personnel need to be familiar with these peculiarities and how to manage them. Secondly, there are some unique hazards within this environment that require thorough training on emergency procedures. One salient example is the risk of creating a forest fire due to an accident. Lithium polymer batteries and petroleum (thetwo most common energy sources) are inflammable and highly flammable, respectively. The organisation should have considered how it would manage post-crash activities when writing their operating manual; however, staff require training on these emergency procedures to ensure that these can be carried out in a timely and coordinated manner. Thirdly, the highly automated nature of the systems means that the role of a pilot is more one of monitoring systems rather than conventional \"hand on stick\" flying. This means that they need a thorough understanding of the systems that are being used, their failure modes, and when intervention may be necessary or desirable in the interests of safety. Because there are significant differences between platforms and ground control systems, this training needs to be bespoke for the particular operator. Lastly, an assessment of competence cannot be restricted only to flying ability; competency needs to be assessed more holistically. The term operational competency tends to be used for more novel applications, such as BVLOS in forestry, whereby pilots and support crew need to demonstrate competency in the overall operation, including pre-flight procedures, in-flight procedures, post-flight procedures, emergency procedures, and so on. This often means working with training providers to create bespoke competency assessments that provide a sufficient level of safety assurance for the human element of the operation.
## 6 Results  
### Simulation Results  
For evaluating the ring-shaped signaling algorithm for a conventional communication system, Matlab simulations with varying \(I_{m}\) values were performed with a fixed value of \(I_{p}\), since \(I_{p}\) was not effective for the intended receivers. The simulations were performed with conventional QPSK, 16 QAM, and 64 QAM systems under an AWGN channel. The simulations were performed without any additional error correction coding or performance-improving functions. As shown in Figure 9, the ring-shaped signaling algorithm properly reshaped the constellations of QPSK, 16 QAM, and 64 QAM. Moreover, since \(I_{m}\) multiplied the magnitude, the reshaped constellation had a more complex shape for QAM than PSK, and the complexity increased with a higher modulation order. However, as shown in Figure 10, interestingly, the effect of \(I_{m}\) variation toward the BER was similar no matter what modulation scheme was used. The following sections present the implementation of the ring-shaped signaling algorithm with SDR. Due to several configuration complexities, we only focused on implementation with the PSK system, and we considered the implementations with a QAM system for future work.  
Figure 8: Proposed header and data separated symbol transformation process.  
Figure 9: Constellation of (**a**) QPSK original, (**b**) 16 QAM original, (**c**) 64 QAM original, (**d**) reshaped QPSK, (**e**) reshaped 16 QAM, and (**f**) reshaped 64 QAM.
## 3 Results  
We start our discussion with a short introduction into the generation of single-attosecond pulses and attosecond pulse trains, which are obtained by the HHG process in noble-gas atoms. The latter can be envisioned in a three-step process [36; 37]: During each cycle of the laser pulse, the atom is ionized by the strong electric field of the laser pulse. The electron is subsequently further accelerated by the laser field. When the electric field reverses its sign during the cycle, a certain fraction of the electron wave packet is accelerated back toward the parent ion and recollides. Since it gained kinetic energy during the acceleration process, the recombined atom emits a photon with energy corresponding to an odd high-order multiple of the fundamental photon energy. A typical gas target HHG spectrum of a near-infrared few-cycle pulse can be divided into three regimes (Figure 2): The intensity of the spectral lines rapidly decays at the lowest odd harmonic orders due to decreasing conversion efficiency in this regime. At intermediate harmonic orders, there is a plateau regime with distinguishable lines of constant intensity located at the odd multiples of fundamental frequency. A dropping cutoff continuum is formed at the highest harmonic orders within reach where only recolliding electrons from a single half-cycle of the IR pulse with maximum electric field strength contribute. Cutoff energy depends on the ponderomotive potential of the laser-driven motion, and thus increases with increasing laser intensity [26].  
RABBITT and streaking experiments require filtering different regions of the HHG spectrum. In a RABBITT experiment, the excitation spectrum is restricted to the plateau region where separate EUV lines can form attosecond pulse trains [28]. For streaking, a broad part of the cutoff continuum needs to be spectrally filtered to generate single-attosecond pulses. To obtain sufficient spectral width in the cutoff regime for single-attosecond pulses, the fundamental pulse duration is restricted to less than two optical cycles [1]. The goal here is to study the possibility of direct experimental comparison of streaking and RABBITT measurements of identical initial and final states. Hence, it is required that single-attosecond pulses and attosecond pulse trains with identical mean photon energy are available. Therefore, either HHG cutoff is brought into overlap with a spectral filter that then transmits a single pulse, or the cutoff is shifted to significantly higher energies, so that the plateau regime is transmitted through the filter, resulting in an attosecond pulse train. For the following analysis, we assume that we can independently adjust the EUV spectrum from the IR pulse. In an experiment, this is, in principle, possible if the IR pulse is separated from that part of the IR pulse driving the HHG process. The pulse duration and intensity of the part of the IR pulse responsible for HHG needs to be adjusted to tune the EUV spectrum. Furthermore, effects related to the spectral phase of the EUV spectrum are omitted.