esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This work presents a system that creates its dataset to obtain the speed of a vehicle from image sequences. This dataset created served as a base for experiments with different statistical and machine learning methods. We divide the speed estimation process into two stages. The first stage consisted of extracting the essential features from the videos of vehicular traffic. This stage used a video camera a radar to take de videos. We processed 29 videos (previously obtained) to obtain the 532 valid samples. This process combines the use of YOLO to detect multiple vehicles and the Kalman filter to predict the vehicle's trajectory. The second stage uses valid samples as an input of different statistical and machine learning methods to estimate the vehicles' speed. The system shows that it is relatively simple to obtain many parameters from a video by implementing existing computer vision algorithms and trained machine learning models. One of the biggest challenges for implementing this system has been sampling since it is necessary to find an ideal place with enough vehicular flow to obtain as much data as possible. Another essential point to mention is the processing time of each sample. As presented above, it can take up to 33% more time than the original video in the worst case. Plus, the time required to clean and validate the samples adds more time to each video's processing. The results obtained from the generated dataset with different statistical and machine learning methods show the accuracy of each method. The linear regression method had the best-performing accuracy, obtaining an accuracy of 0.956 km/h on the mid lane and 1.694 km/h on the third lane. However, the other statistical methods implemented showed similar performance. On the other hand, the machine learning methods used in this work had acceptable performance; however, they could not improve the performance achieved by the statistical methods. We will explore the use of deep learning and transfer learning to the developed dataset to improve the results presented in this work. We will focus on solving limitations, such as complete system automation without human intervention since human interactions were necessary for the samples' generation and validation. In addition, we will include in the dataset of night videos to test our proposal's behavior and videos with a higher cars frequency. The information generated with the system, such as frequency and type of vehicles, could also be expanded to allow experts to improve traffic flow and reduce accidents or environmental impacts.
## 3 Analysis of Strain Transfer Influential Factors  
The strain transfer characteristics are decided by coefficient \(\omega\) and bonding length \(L\) according to Equations (23) and (24). The coefficient \(\omega\) contains geometrical and mechanical parameters of the measurement structure, and it is the synthesis of \(\delta_{1}\), \(\delta_{2}\), and \(\delta_{3}\) according to (21). Coefficient \(\delta_{1}\) includes the parameters of the silica core and the coating which are regarded as constants for a certain FBG sensor. Coefficient \(\delta_{2}\) indicates the interactions between the FBG sensor and adhesive layer. The coefficient \(\delta_{2}\) can be influenced by geometrical parameters and the elasticity modulus of the adhesive layer, which has been investigated in previous papers [22; 28]. Coefficient \(\delta_{3}\) mainly describes the interactions between the adhesive layer and the CFRP laminates. The configuration of CFRP laminates influences the coefficient \(\delta_{3}\) by changing strain transfer characteristics. According to the analysis in Section 2, the configuration is determined by stacking direction, stacking number, and stacking sequence. Therefore, the effect of the three factors on the strain transfer characteristics is further analyzed. In this paper, the fabric layers are placed in three kinds of directions: \(0^{\circ}\), \(\pm 45^{\circ}\)and \(90^{\circ}\). The \(\pm 45^{\circ}\) layers come in pairs to keep the symmetry of CFRP laminates. Table 1 shows the geometrical and mechanical parameters (at 20 \({}^{\circ}\)C) of the measurement structure.
## 3 Methodology
### Implementation and Development of the Presented Methodology
Tools such as spaCy and the BERT Model allow for extracting useful information from large volumes of unstructured data. In the context of customer relationship management (CRM), NLP helps manage customer relationships by personalizing the response according to their experiences and reported problems. * Real-Time Monitoring and Control. These tools enable the real-time monitoring of asset performance and condition, which is crucial for immediate response to failures. Apache Kafka and Prometheus are used to collect and analyze sensor data, supporting the Reliability, Availability, Maintainability, and Safety (RAMS) phase by ensuring assets maintain an optimal level of operation. * Performance and Quality Analysis. Systems such as JProfiler and Apache JMeter are used to assess the performance of the warranty management model and monitor quality at each phase of the asset life cycle. These enablers are essential to track KPIs and ensure the quality of assets in service. * Life-Cycle Management. In the Life-Cycle Cost Analysis phase, asset life-cycle management systems, such as IBM Maximo and Infor EAM, are used to plan and manage the costs associated with component maintenance and replacement. These tools provide a comprehensive view of cumulative costs, which is critical to optimizing asset profitability and financial sustainability. * Collaborative Learning. Google Colab and Microsoft Azure Notebooks are platforms that facilitate collaborative learning, allowing teams from different disciplines to work together on prediction and optimization models. This collaboration is essential in the context of continuous improvement, where it is necessary to integrate new approaches to warranty management. * Optimization Models. Optimization models, implemented using tools such as Gurobi Optimization and IBM CPLEX, allow for maximizing efficiency and minimizing costs in the warranty model. These models are especially useful in the Warranty Policy Risk-Cost-Benefit Analysis phase, where warranty policies are evaluated based on return on investment and operating cost. * Continuous Improvement. Continuous analysis tools such as Salesforce Einstein Analytics facilitate constant feedback on warranty policies, ensuring that they adapt to changes in the market and customer expectations. These tools are integrated into the e-Warranty phase, where a constant update of warranty policies is sought to maximize customer satisfaction and loyalty. To evaluate the impact of these technologies, the following key performance indicators (KPIs) are defined, as presented in Table 4.
# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
###### Abstract  
Hand gestures and poses allow us to perform non-verbal communication. Sign language is becoming more important with the increase in the number of deaf and hard-of-hearing communities. However, learning to understand sign language is very difficult and also time consuming. Researchers are still trying to find a better way to understand sign language using the help of technology. The accuracy of most hand-sign detection methods still needs to be improved for real-life usage. In this study, Mediapipe is used for hand feature extraction. Mediapipe can extract 21 hand landmarks from a hand image. Hand-pose detection using hand landmarks is chosen since it reduces the interference from the image background and uses fewer parameters compared to traditional hand-sign classification using pixel-based features and CNN. The Recursive Feature Elimination (RFE) method, using a novel distance from the hand landmark to the palm centroid, is proposed for feature selection to improve the accuracy of digit hand-sign detection. We used three different datasets in this research to train models with a different number of features, including the original 21 features, 15 features, and 10 features. A fourth dataset was used to evaluate the performance of these trained models. The fourth dataset is not used to train any model. The result of this study shows that removing the non-essential hand landmarks can improve the accuracy of the models in detecting digit hand signs. Models trained using fewer features have higher accuracy than models trained using the original 21 features. The model trained with 10 features also shows better accuracy than other models trained using 21 features and 15 features.  
Keywords:hand-sign detection; Mediapipe; feature selection; recursive feature elimination +
Footnote †: journal:
## 8 Security Analysis  
In this section, we discuss the security features offered by our solution.  
The security of our solution is based on three components:  
1. Message splitting: Each message to be sent is split in \(k\) parts. An adversary cannot reconstruct \(M\) without knowing all the parts.
2. Covert channel: Each part is sent through a cover channel exploiting the length field of the MQTT packet. This makes it harder to distinguish between a publisher transmitting actual data and dummy data.
3. PRNG: The use of a shared seed for a PRNG ensures synchronized and unpredictable selection of brokers and topics for communication. Moreover, secure and unpredictable parameters for the execution of the protocol are extracted through the PRNG. Our threat model includes three possible threats:  
* Eavesdropping and traffic analysis: An adversary intercepts communication to gain information about the transmitted message.
* Man-in-the-M = middle attacks [54]: An adversary intercepts, modifies, or injects messages between the publisher and subscribers.
* Broker compromise [55]: An adversary gains control over one or more brokers.  
Figure 3: Length distribution.  
In the following, we show how our proposal addresses this threat model.  
Eavesdropping and traffic analysis:  
Message splitting makes traffic analysis less effective. Indeed, the original message \(M\) cannot be reconstructed when the adversary can retrieve up to \(k-1\) parts.  
In addition, the attacker cannot identify meaningful information within intercepted packets due to the covert channel. The use of the length field to encode parts of the message means that the actual payload appears as random or dummy data. This reduces the likelihood of detection by intermediaries or adversaries monitoring the traffic.  
Finally, the adoption of PRNG for broker and topic selection mitigates the risk of traffic analysis. Indeed, the publisher and subscribers share the seed of the PRNG and are able to independently determine the same brokers and topics for communication. On the other hand, without such a seed, the brokers and topics are unpredictable for an adversary.  
Man-in-the middle attacks:  
The adoption of a hlcovert channel prevents data interception and tampering. Indeed, any modification to the length field or payload would render the part unusable, as it would fail the brute force verification performed by the subscriber.  
The only way for the adversary to compute a valid length field should be to recover the parameters \(n\) and \(a\). However, an adversary cannot do so, since there are no feasible means to guess or compute these two parameters.  
Broker compromise:  
The brokers adopted to transmit parts are randomly chosen through the PRNG. Then, it is difficult for the adversary to detect the right brokers to compromise. In addition, message splitting requires the adversary to break at least \(k\) brokers to recover the original message, \(M\).
## 4 Discussion of the Findings
### Empirical Validation
This is further evident in the usability questionnaire of the qualitative study, where 59% of students indicated the validity of RestRho, with a further 12.8% of students indicating that validity would increase with additional documentation and training. A further 15% of students had a neutral result for validity, such as \"I have no opinion\". Overall, both the quantitative and qualitative studies have demonstrated that RestRho supported students to deliver results faster with less time writing code. Concerns around the ease of learning have been made evident, which can be resolved with more documentation and tutorial sessions. Nevertheless, the validity and utility themes of the qualitative analysis verified that RestRho successfully achieves its proposed objectives.
## 3 Data Sources  
This article uses Landsat 0 LI and WorldView-2 remote-sensing data, and its basic parameters are shown in Table 1. Landsat8 carries two sensors: OLI and TIRS. The OLI sensor includes nine bands of a spatial resolution of 30 m, including a panchromatic band of a spatial resolution of 15 m [46]. OLI includes all the bands of Landsat7 ETM+ and has a narrower spectral width, which improves the recognition quality of clay minerals and iron oxides and can better distinguish vegetation and non-vegetation features in the panchromatic image. In order to avoid atmospheric absorption features, OLI Band 5 excludes the water vapor absorption feature of 0.825 um. In addition, a blue band (band 1: 0.433-0.453 um) and a cirrus band (band 9: 1.360-1.390 um) are added, which can be applied to coastal observation and cloud detection, respectively. Furthermore, all the OLI and TIRS spectral bands are stored as geolocated 16-bit digital numbers in the same Level 1 terrain corrected (L1T) file, which facilitates the differentiating of materials more efficiently than ETM+ imagery stored as 8-bit numbers [47; 48]. Although multispectral sensors such as TM, ETM+, and OLI render insufficient spectral resolution for discriminating specific minerals, achieving effective processing, or running data analysis, they yield useful image products for regional exploration and discovery when they are combined with a good understanding of the associated landforms [46]. In this study, a cloud-free L1T Landsat-8  
Figure 2: Field photographs of typical ore structures of (**a**) chromitite with massive ore; (**b**) banded chromite; (**c**) densely disseminated chromitite; (**d–f**) well-developed nodular chromitite.  
image of the study area (path/row 137/40) was downloaded from the US Geological Survey (USGS) Earth Resources Observation and Science Center ([[http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov)]([http://earthexplorer.usgs.gov](http://earthexplorer.usgs.gov))).  
The WV-2 satellite was launched on 6 October 2009 and operates on a sun-synchronous orbit at a height of 770 km [49]. It can provide panchromatic imagery with a spatial resolution of 0.5 m and multi-spectral high-resolution satellite imagery with a spatial resolution of 1.8 m [50; 51]. The satellite-borne multispectral sensor not only has four standard spectral bands of the industry (red, green, blue, and near-infrared 1), but also includes four additional bands (coastal, yellow, red edge, and near-infrared 2) [52; 53; 54]. The data used in this article were imaged on 8 February 2015.  
The remote sensing images are clear, with a strong sense of hierarchy for different land cover types, clear textures, and good tone contrast. The projection coordinate system is the UTM Mercator projection, and the ellipsoid is WGS-84.
# IFE-Net: An Integrated Feature Extraction Network for Single-Image Dehazing
## 4 Experiments
### Qualitative Results on Real-World Images  
Figure 6 shows a comparison of the results between IFE-Net and other methods using real scenes. As shown in Figure 6, DCP and GCA suffered from visual artifacts on the real hazy images. AOD-Net, FFA-Net, and GCA produced unrealistic colors in one or several images, such as the results of AOD-Net and FFA-Net in the fourth row as well as the results of AOD-Net and GCA in the fifth row. Dehaze-Net and FFA-Net retained a thin layer of haze, as shown in the second row. However, IFE-Net achieved excellent results in both thin and thick haze areas while maintaining colors consistent with real scenes. A similar result can be observed in the outdoor images shown in Figure 7. We enlarged the upper left corner of Figure 7a-g,h-n to show the enlarged results. The results of AOD-Net, FFA-Net, GCA, and GUNet exhibited color distortions and many non-natural characteristics. Additional white haze appeared in FFA-Net, resulting in incomplete dehazing. The result of DWGAN contained too much white. GCA and DWGAN showed unclear outlines of buildings in hazy areas above the sky. In contrast, IFE-Net successfully removed almost all of the haze while preserving the essential properties of the images, with obvious advantages in preserving edges, texture, contrast, brightness, and other image characteristics, as shown in Figure 7.  
In addition, we also removed haze from the hazy image of a large area of the sky and compared it with several other advanced methods. Most dehazing algorithms have poor dehazing effects on images containing large areas of sky, resulting in color distortion and uneven color blocks in the restored haze-free images. We show the results of several methods in Figure 8. Figure 8a shows the input hazy image, and Figure 8b-d show the dehazing results of GCA, GUN-Net, and IFE-Net, respectively. From Figure 8b, it can be observed that the results obtained show a thorough removal of haze on the ground, but uneven color blocks appear on the ground and also in the sky area. In Figure 8c, there are no issues with image color distortion, but the dehazing effect in the ground and sky areas is not significant. In Figure 8d, the haze in the sky is suppressed without significant color distortion blocks. Simultaneously, the dehazing effect in the ground area is significant, and the results obtained are good in terms of dehazing and details.  
Figure 8: Results of dehazing in sky areas with dense haze.  
Figure 7: This figure shows our ability in detail and color processing compared to other networks.
## 3 Crystal-Based X-ray Interferometer
The absorption and phase shift are unknown and cannot be quantitatively obtained from a single interference pattern. Therefore, a method for quantitatively detecting the phase shift is needed. Two candidate methods are (1) the Fourier transform method [21], in which narrow-spaced interference fringes (carrier fringes) are generated and the phase shift is calculated from the spatial movement of the fringes, and (2) the fringe scanning method [22], in which a phase shifter is placed in the reference beam path and the phase shift is calculated from multiple interference images acquired for different phase shifts. The  
Figure 2: Various types of crystal-based X-ray interferometers. spatial resolution of the former method is limited by the spacing of the carrier fringes, so the latter method is used in most cases. The \(\Delta p\) in the fringe scanning method with \(n\)-steps can be obtained from  
\[ \begin{array}{l}\Delta p=tan^{-1}\left(\frac{I_{re}}{I_{in}}\right)\\ I_{re}=\sum\limits_{m=0}^{n-1}{{I_{m}}^{\prime}\cos\frac{2\pi mn}{n}}\\ I_{im}=\sum\limits_{m=0}^{n-1}{{I_{m}}^{\prime}\sin\frac{2\pi mn}{n}}\end{array}  \tag{3}\]  
by using intensities \({{I_{m}}^{\prime}}\) obtained for the \(m\)-th phase step. In addition, \(\gamma^{\prime}\) can be obtained using \(I_{re}\) and \(I_{im}\):  
\[\gamma^{\prime}=\sqrt{I_{re}^{2}+I_{im}^{2}} \tag{4}\]  
The decrease in \(\gamma\) is related to the disturbance of the wavefront due to the scattering caused by the sample's microstructure and to the decrease in coherence due to the misalignment \(\Delta x\) of the object and reference beam paths on A. The scattering has been shown to correspond to small-angle scattering [23]. Hence, the resulting image is called the scattering or small-angle scattering image and is used as a third image in GXI and DEL. Both the scattering and the decrease in coherence cannot be separated in CXI, so \(\gamma\)-contrast imaging, i.e., coherence-contrast imaging, is used as qualitative imaging to compensate for the narrow dynamic range of CXI. The absorption \(((I_{1}-\Delta I_{1})+I_{2})\) can be obtained by adding \({{I_{m}}^{\prime}}\) because the phase terms (oscillation terms) cancel each other out. Therefore, three types of images (absorption-, phase-, and coherence-contrast) can be obtained simultaneously with the fringe scanning method. The obtained \(\Delta p\) is \(2\pi\) wrapped, so the \(2\pi\) must be recovered using a phase-unwrapping process. As shown above, CXI detects the \(\Delta p\) (not the spatial gradient of \(\Delta p\)) directly, so the sensitivity is about one order of magnitude higher than that of other PCXI methods [8,9].
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 1 Introduction
### Contributions  
The contributions of this work are as follows.  
* A simple yet effective coordinated control of VPPs for short-term frequency containment, which improves the power system dynamic response and maintains frequency stability.
* A comprehensive study of the impact of different communication networks, focusing in particular on the effect of latency, on the performance and stability of the proposed coordinated control.
* An in-depth discussion of the impact of stochastic sources such as SPVG and WG on the performance of the VPP with and without the inclusion of the proposed coordinated control.
## 3 Data Analysis
### Temperature  
Battery cell temperature is one of the parameters that primarily affects the operation of the battery. Low temperatures can cause lower capacity, pronounced increases in internal resistance, and problems in the balance of discharged cells. On the other hand, elevated temperatures can overstress batteries during discharges and shorten service life [17,18].  
Figure 14 shows the comparative evolution between the voltage dispersion (with the weighted correction applied) and the ambient temperature of use for battery 5. Both parameters are presented in a dimensionless way based on their respective average values in each pack. There is no clear correlation between them, and this can be seen in the rest of the batteries in the same way.  
It can be concluded that in this case, the temperature of use did not directly affect the operation of the batteries. This may be because temperature peaks are not extreme or continuous enough over time to cause negative effects. The average temperature was 22.5 \({}^{\circ}\)C with peaks from 3.5 \({}^{\circ}\)C to 39 \({}^{\circ}\)C. Hence, in this study, the temperature will not be taken into account to perform the modeling.