esa-sceva/satcom-chunk-collection huggingface dataset:
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 7 Conclusions  
As our society embraces the IoT and cloud technologies in a wide range of application areas including industry, agriculture and education, considerations for security and privacy must be more than an afterthought. In this sense, the focus needs to shift from securing network perimeters to safeguarding data spread across devices, networks and the cloud as a whole system. To this end, an Azure Sphere based integrated solution for IoT, which utilizes the highly secure, low-cost MCU MT3620 and Microsoft Azure cloud services to meet the requirements of robust end-to-end security and unified device management user scenarios, is proposed in this article. The overall system has been completely designed from hardware prototype to software implementation, and from the device to the cloud. With the proposed solution, users are capable of integrating the highest levels of security into their products and services to address the rising risks and threats. Furthermore, device provision, configuration and management are implemented by Azure cloud services. In order to test and verify the proposed system, an experimental testbed is established on the basis of MT3260 development kit, CC2530 ZigBee wireless network, Azure cloud services, Power BI and native windows-form applications. The experimental results are demonstrated from the device layer, the cloud layer and the application layer respectively, indicating that the proposed integrated solution is feasible and effective.  
The source code and guidelines of this project for MT3620, CC2530 and windows-form based native applications are hosted on GitHub repository: [[https://github.com/shijiong/A-Security-Solution-Using-Azure-Sphere-for-IoT](https://github.com/shijiong/A-Security-Solution-Using-Azure-Sphere-for-IoT)]([https://github.com/shijiong/A-Security-Solution-Using-Azure-Sphere-for-IoT](https://github.com/shijiong/A-Security-Solution-Using-Azure-Sphere-for-IoT)).  
Conceptualization, [PERSON]; Methodology, L.J.; hardware, J.S.; software, [PERSON] and L.J.; validation, J.S.; investigation, L.J.; resources, J.S.; data curation, L.J.; writing--original draft preparation, J.S. and J.L.; writing--review and editing, J.L.; project administration, [PERSON].  
This research was supported by the Zhejiang Provincial Public Welfare Program under Grant (No. 2017C33152 and No. 2017C31040), Scientific Research Fund of Zhejiang Provincial Education Department under Grant (No. Y201636753), Research Development Fund of Zhejiang Wanli University, Ningbo Technology Project for People's Welfare (No. 2017C50028).  
The authors would like to thank all the anonymous reviewers for their insightful comments and suggestions.
## 3 V-HOE Recording Process  
The simplest V-HOE is a VHG which acts as a non-focusing element; therefore, it basically redirects the light [21]. Usually, a hologram can be recorded by interference between two beams, namely reference and object beam, respectively. When the two interfering beams are two collimated light beams, a VHG can be recorded. A scheme of the experimental setup typically used to record VHGs is shown in Figure 1a; the angle \(\alpha\) between two incident beams is related to the final grating period of the recorded VHG. The interference fringe pattern leads to a photoinduced modulation of the refractive index in a photosensitive thick film.  
Figure 1: (**a**) Set-up for volume holographic grating recording; (**b**) Set-up for volume phase holographic lenses recording; (**c**) Schematic representation of the recording of multiplexed holographic lenses: the reference beam is fixed while the object beam incident angle is changed during the process. The multiplexed holographic lenses recorded behaves like a passive solar tracker: the light coming from the sun in different positions, that is, at different times of the day, is always focused on the same point, where a photovoltaic cell is positioned.  
When in the object beam path a focusing optics is placed during the recording process, the interference between a reference (collimated) beam and an object (converging) beam is induced. Thus, an interferometric pattern that replaces the response of the focusing optical systems used as object is generated, obtaining a V-HOE that acts as a focusing element. This V-HOE shows the same effect as spherical or cylindrical lenses [4; 24]. A typical recording setup is reported in Figure 1b.  
Conventional concentration modules need the aid of complex mechanical systems (active solar tracking) to improve their efficiency. These aiding systems increase the complexity of the PV cell power system design, the panel volume and development costs. The possibility to reduce or even eliminate, the moving system using multiplexed holographic lenses as passive solar trackers, makes the system more competitive in the perspective of new generation solar panel development. As a result, there are fewer problems related both to the wear of moving parts and possible vibrations due to movements [16]. Multiplexed V-HOEs can be achieved by changing the angle between the two incident wavefronts during the writing process, as schematically shown in Figure 1c. In this way, a V-HOE that addresses the solar radiation incident with different angles, on a single photovoltaic cell, can be realized.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 2 Related Work  
### HSV Color Space and Color Quantization  
HSV is an alternative representation of the RGB color model and aims to be more closely aligned with how human vision perceives color attributes. The hue component describes the color type, and saturation is the depth or purity of color. The colors of each hue are arranged in radial slices around a central axis of neutral colors, and the neutral colors range from black at the bottom to white at the top. An HSV color space is given with hue \(\text{HV}\in[0^{\circ},360^{\circ}]\), saturation \(\text{SV}\in[0,1]\), and value \(\text{V}\in[0,1]\). The color conversion from RGB to HSV is given by [42]:\[R,G,B\in[0,1] \tag{1}\]  
\[X_{max}=V=\max(R,G,B) \tag{2}\]  
\[X_{min}=\min(R,G,B) \tag{3}\]  
\[C=X_{max}-X_{min} \tag{4}\]  
\[H=\left\{\begin{array}{ll}0&if\ C=0\\ 60^{\circ}\cdot(0+\frac{G-G}{C})&if\ V=R\\ 60^{\circ}\cdot(2+\frac{B-R}{C})&if\ V=G\\ 60^{\circ}\cdot(4+\frac{R-G}{C})&if\ V=B\end{array}\right. \tag{5}\]  
\[S_{V}=\left\{\begin{array}{ll}0&if\ V=0\\ \frac{C}{V}&otherwise\end{array}\right. \tag{6}\]  
For example, [\(R=1\), \(G=0\), \(B=0\)] can be represented by [\(H=0^{\circ}\), \(S=1\), \(V=1\)] in HSV color. When \(S=0\) and \(V=1\), the color is white; and when V = 0, the color is black. The \(H\), \(S\), and \(V\) channels are divided into different bins, and then a color histogram is used for color quantization.
## 5 Bit Error Rate of the Information Signal in the Power Packet  
The power packet includes the information signal which is temporally attached to the power payload as header and footer. Here, we assume that the original pulses are rectangular waves, because signal pulses are also generated by turning on and off in circuits like payload. In addition, the raised cosine (RC) pulse is also considered as one of the band-limitation pulse shapes.  
Figure 9 is a schematic diagram of the model investigated here. We transmit the positive and negative pulses randomly through the power line channel, using the branched and branch-less channels (Figure 6). The noise is assumed to be white Gaussian noise and is added at the start of the power line channel. A matched filter is set at the receiver. The input impedance of the receiver is set to match that of the VVF cable. At the above settings bit error rates (BERs) are calculated by numerical simulation.  
Figures 10 and 11 show the BER as functions of bit energy at transmitter over noise power \(E_{\mathrm{b}}/N_{0}\) for the rectangular and RC pulses, respectively. The theoretical BER is obtained by \(\frac{1}{2}\mathrm{erfc}(\sqrt{E_{\mathrm{b}}/N_{0}})\). Because of the power loss in the power line channel, the rectangular and RC pulses perform worse than in the theoretical calculations. The rectangular pulse has a lower BER than the RC pulse. Although there  
Figure 8: Arrival power ratio for rectangular pulse and sine wave.  
Figure 9: Schematic diagram of the model investigated here.  
is no bandwidth limitation for the rectangular pulse, in practice it is necessary to apply band-limited pulse shaping, similar to that of the RC pulse. In both cases, it is clear that the branch-less channel performs better than the branched channel. These results are important when designing the signal part of the packet with respect to the number of bits in the header and footer. For example, the required bit energy can be estimated at the transmitter to obtain the desired BER.
## 5 Outlook
### Preprocessing and Data Fusion  
In earlier studies, the preprocessing stage was important for the appropriate use of Landsat data and as a result, a large proportion of time and resources were dedicated to this step. Miss-registration between images was an early and major source of error in Landsat-based methods, and much effort was dedicated to the geometric correction of images. Additionally, radiative models required information about the atmospheric condition during the time of image collection and relative methods were not accurate enough. Today, however, the availability of the Landsat Collection 2 data has almost eliminated the need for extensive preprocessing, thus contributing to an increased application of Landsat data.  
Similarly, clouds and other anomalies common in time series are no longer considered major problem, given developments in cloud masking and composite image methods that ensure cloud-free time series or annual images, such as pixel-based image compositing. For example, [PERSON] et al. (2014) developed the best-available-pixel (BAP) method for creating image composites over large-areas. This method does not rely on a scene-based analysis, but rather assigns an overall score to each pixel within an image stack based on the distance to clouds, cloud shadow, opacity, sensor, and day of the year ([PERSON] et al., 2015). Then a pixel with the highest overall score is chosen for the composite image for a specific period or a limited phenological window. This method can provide annual, multiyear, and proxy-value composite images.  
Given improvements in the standards of Landsat data, there has been a great focus on multisensor studies that combine different sources of information to improve overall results. For example, the fusion of high-frequency MODIS data with relatively higher spatial resolution Landsat data allows for the extraction of greater detail in highly dynamic situations ([PERSON] et al., 2015). Similarly, radiometrically consistent Landsat data can be fused with Sentinel-2 data, which has a higher spatial resolution ([PERSON] et al., 2015). A detailed review is available on recent applications of Landsat-8 and Sentinel-2 with both pixel-based and geographical object-based approaches. Application of conventional vegetation indexes for phenology-based LULC classification is illustrated and challenges to deal with Landsat-Sentinel's data integration and geo-big-data is highlighted ([PERSON] et al., 2015). The fusion of Landsat data with active sensors, such as SAR and LiDAR, can also provide new information to change detection. Unfortunately, there is no uninterrupted freely available SAR data archive that matches the historic extent of Landsat; however, PALSAR 1 and 2 data have been frequently used in multisource studies applying optical and radar data together. Likewise, a synergistic use of Sentinel-1 and Landsat-8 ([PERSON] et al., 2015) for change detection analysis represents great opportunities for future studies.
# Metallurgical Effects of Niobium and Molybdenum on Heat-Affected Zone Toughness in Low-Carbon Steel
###### Abstract  
Modern weldable high strength steel grades are typically based on low-carbon alloy concepts using microalloying for obtaining a good strength-toughness balance. Such steel grades having a yield strength in the range of 420 to 690 MPa are very commonly used in pipelines, heavy vehicles, shipbuilding and general structural applications. Thermomechanical processing during hot rolling combined with accelerated cooling is an established means of producing such steel grades. Considering the alloying concepts, the use of niobium and molybdenum, and in selected cases boron, is very efficient to achieve high strength and good toughness. However, all targeted applications of such high strength steels involve extensive welding. Thus, heat affected zone properties are of particular importance. The present paper investigates the effects of Nb, Mo and Ti on the heat affected zone properties. Variations of the Mn and Si contents are considered as well. Additionally, the influence of post-weld heat treatment in the coarse-grained heat-affected zone (HAZ) is considered. In this approach, HAZ subzones were generated using laboratory weld cycle simulations in combination with systematic variation of alloying elements to scrutinize and interpret their specific effects. The results indicate that Mo and Nb, when alloyed in the typical range, provide excellent HAZ toughness and guarantee sufficiently low ductile-to-brittle transition temperature. An alloy combination of Nb, Mo and Ti improves performance under hot deformation conditions and toughness after post-weld heat treatment.  
welding simulation; heat-affected zone; post-weld heat treatment; high-strength steel; microalloying; solubility; precipitation; solute drag; abnormal grain coarsening; hot ductility Article
###### Abstract  
The remote and non-contact monitoring of human respiration and heartbeat based on radars is a safe and convenient practice. However, how to accurately estimate the heart rate is still an open issue, because the heartbeat information in radar signals is affected by respiratory harmonics. In this paper, a maximum likelihood estimation was introduced to extract the heart rate from high-pass-filtered radar heartbeat waveforms where the low-frequency respiratory and heartbeat components were attenuated. The closed-form asymptotic estimation variance of the maximum likelihood estimator was derived to describe its performance in white Gaussian noise with a high signal-to-noise ratio (SNR). The proposed method was verified using two publicly available datasets and demonstrated superior performance compared to other methods. The estimation method and the asymptotic estimation variance here described are also applicable for signal period estimation in other settings with similar conditions.  
asymptotic estimation variance; Cramer-Rao bound; heartbeat rate; maximum likelihood estimation; remote monitoring
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### System Operation and Optimization
#### 4.2.2 Demand Response Management  
Demand response management is a critical component of modern smart grids, aimed at adjusting the demand for power instead of the supply. Digital twins facilitate a more responsive and flexible demand management system by accurately modeling the grid and consumer behavior. This allows utilities to incentivize reduced consumption during peak times and manage the distribution of resources more effectively. The integration of Digital Twins in demand response programs enhances the grid's ability to adapt to real-time changes and maintain an equilibrium between supply and demand, contributing to overall energy savings and system reliability.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 3 Related Work
### Threats, Attacks, and Countermeasures in FL
#### 3.2.2 Training and Prediction Phase  
The FL training phase is vulnerable to two main types of attacks: poisoning attacks, which aim to disrupt the model's availability, and privacy inference attacks, which seek to extract sensitive information about the dataset and compromise the confidentiality of the FL system.  
Poisoning attacks can target both the model and the data. Model poisoning typically degrades model performance, while data poisoning might be mitigated by the learning algorithm, such as through gradient shaping techniques [19]). The threat model involves adversaries manipulating local workers to alter model updates, with the extent of manipulation affecting the attack's success. Attacks can be either targeted, such as backdoor attacks, or non-targeted, like Byzantine attacks, including variants like the free-riding attack, where workers contribute minimally or provide irrelevant updates [20]. Defenses include robust aggregation, where the central server verifies the global model's performance to detect potential poisoning and compares updates across workers to identify anomalies [21].  
Privacy inference attacks exploit the FL framework in which gradients and parameters are exchanged between local workers and the central server, allowing for white-box attacks. The threat model considers vulnerabilities at both the worker and server levels, as well as in communication channels. Key attack types include membership inference, aiming to determine whether specific data were used in training [22]; class representative inference, which seeks to obtain prototypical samples of a target label [23]; property inference, targeting meta-characteristics of training data [24]; and data reconstruction, aiming to accurately reconstruct training samples and labels [25]. Defenses focus on processing shared gradient information, such as compressing gradients to reduce exposure [26], using homomorphic encryption to protect data during computation [27], and applying differential privacy by adding controlled noise to gradients [28].  
In the model prediction phase, the global model is accessible to both local workers and the central server, creating potential vulnerabilities. Malicious actors, whether a local worker or the server, can attempt to extract sensitive information from the global model, leading to two primary types of attacks: evasion attacks and privacy inference attacks. The threat model, attack strategies, and defense mechanisms in this phase are consistent with those of centralized ML, as the post-training environment mirrors this setting [29].
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 3 Proposed Method
### Computational Complexity Analysis  
We now analyze the computational complexity of CGMvFS. Given that the algorithm primarily operates on the variable \(W^{v}\), its efficiency critically depends on the scale and manipulation of this matrix. Through careful evaluation of the dominant procedures--notably the iterative updates and evaluations involving \(W^{v}\)--we determine the time complexity to be \(O(k_{\sigma}^{3})\). Consequently, the overall complexity of CGMvFS is \(O(\sum_{v=1}^{V}k_{\sigma}^{3})\). This confirms that the computational demands are fundamentally tied to the dimensionality of its core variable.
## 4 Case Study
### Impact of Multiple Slices on User Satisfaction
#### 4.5.1 Mobility  
In Figure 5, we observe the impact of varying the number of slices on user satisfaction with respect to mobility for different numbers of VUs. The results highlight a notable trend. As the number of slices increases, there is an evident improvement in mobility satisfaction. This improvement is attributed to the ability to cover larger areas of the ground efficiently. With multiple slices, the service infrastructure can span wider geographical regions, allowing VUs to traverse expansive areas without experiencing loss of connectivity to the server or higher-layer entities, such as UAVs.  
Figure 4: User satisfaction in different KPIs vs the number of slices.  
In particular, when only one slice is utilized, the coverage of the entire area is based on a single slice and the FL method. This limitation presents challenges in efficiently serving diverse VU mobility patterns. However, as the number of slices increases, the system gains the ability to take advantage of different layers of NTN, leading to a significant increase in user satisfaction. This is particularly advantageous as the number of VUs increases, demonstrating the adaptability of the proposed framework to scale and accommodate growing demands without compromising user satisfaction in terms of mobility.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
Experiments on the Beijing subway travel dataset were conducted, and the results demonstrated the effectiveness of our method. The contributions can be summarized as follows:  
* A continuous-time dynamic graph model is proposed for the passenger behavior prediction problem. Unlike previous sequential models, DyGPP treats fine-grained timestamped events as a dynamic graph and learns temporal patterns for both passengers and stations from their historical sampling sequences. * A temporal pattern encoding module is designed to simultaneously capture the individual temporal patterns for passengers and stations and correlate temporal behaviors between passengers and stations. * Experiments on real-world datasets demonstrated the effectiveness of our method. Compared to the baseline models, our approach achieved higher average precision and AUC values, demonstrating its superiority.
## Appendix A MVSS Beamforming Methodology
### Image Processing and Stacking  
The result obtained by the beamformer is the imaging result in the time domain. We estimated the depth of the reflection interface based on the background velocity and the wave travel time, which is a conversion from time domain to depth domain. For each shot, an image under a particular illumination angle is obtained. The final image is obtained by stacking the amplitudes of each shot, where \(N\) is the total number of shots, \(I_{d}\) is the final image with all shots stacked together, and \(c\) is the estimated background velocity:  
\[I_{d}=c\sum_{N=1}^{N_{max}}I_{t}(N) \tag{13}\]
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 6 Conclusions  
In this paper, three CNN-based CSS schemes are proposed to further improve the sensing performance of CSS. Firstly, the two-dimensional dataset is established based on the covariance matrix of the observed signal. Then, the LeNet, AlexNet and VGG-16-based CSS schemes are trained. Finally, the sensing performance of the proposed schemes is compared with that of the classical CSS schemes, including the AND, OR, majority voting rule. In addition, the average computation times of the proposed CNN-based CSS schemes are discussed. The simulation results show that the sensing performance of the proposed schemes is obviously higher than that of the classical schemes.  
Conceptualization, Y.T. and X.J.; methodology, Y.T.; validation, Y.T.; formal analysis, Y.T.; investigation, Y.T.; data curation, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, X.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the commercial purpose.  
The authors declare no conflict of interest.