esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Description of the Experimental Setups
### Experimental Setup for the Wave Field Plate  
The experimental setup for the _wave field plate_ measurements is shown in Figure 8. The wave field was recorded by a 3D scanning laser Doppler vibrometer PSV-400-3D from Polytec GmbH (Waldbronn, Germany) with an additional PCI 6110 National Instruments measurement card to increase the sample rate to 2.56 MHz. In a first step, the full wave field is captured, as described in Ref. [11] and indicated by the purple region in Figure 3b. Next, a B-scan is recorded vertically to assess the influence of the stringer on the acoustic wavefield (marked in red in the same figure). All measurement parameters of the 3D scanning laser Doppler vibrometer (SLDV) are listed in Table 5. The temperature was measured before every 3D-LDV measurement with a GTH 1200 (Greisinger electronic) with an accuracy of 0.1 \({}^{\circ}\)C.  
The coordinates of the piezoelectric actuator and the reference damage locations are listed in Table 6. This table also includes information on the omega stringer placement and the measurement area. The properties of the excitation signals are listed in Table 7. The arbitrary signals were generated by an (Agilent, Santa Clara, CA 95051, United States) 33500 B and amplified by a (Falco, 2225 TH Katwijk  
Figure 8: Scanning laser Doppler vibrometer (SLDV) setup for the _wave field plate_ with stringer. The electromagnet was removed for the wave field measurements.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties** & **Value** \\ \hline Time samples (per signal) & 8192 \\ Sampling rate & 2.56 MHz \\ Number of spatial sampling points & 45,287 \\ Measurement range & \(\pm\)200 mm/s \\ Pixel spacing & \(\approx\)1.2 mm \\ Averaged measurements per pixel & 50 \\ Temperature & 21.1 \(\pm\) 0.14\({}^{\circ}\)C \\ Filter & bandpass (15–250 kHz) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measurement parameters (SLDV system).  
aan Zee, The Netherlands) Systems WMA-300 5 MHz high voltage amplifier. The reference damage was attached to the structure in the same way as in the SHM measurements.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 2 Literature Review
In summary, most of the previous models have been applied to hard rock TBM operations, and they have several shortcomings. For this reason, a new 1D CNN model is proposed in this research. Unlike prior applications, the proposed model is created for soft ground mechanized tunnelling and big data. The input parameters were obtained from a micro-tunnelling project rather than large-scale TBM sizes. The proposed model provides a more reliable and fast computation compared to previous models. The main purpose of this research is to investigate pipe jacking EPB TBM cutter tool life prediction using the suited 1D CNN regression through high-dimensional machine parameters and to overcome the gaps in the previous model. During the shield-type tunnel construction of a water tunnel in Japan, a huge number of real-time operational data were recorded. In this research, actual tool wear (wear amount in mm) was recorded from inspection points 205, 344, 493, and 623, and machine parameters were collected from a total of 723 rings in the shield tunnel. Moreover, the main geology of the tunnel is sandy silt and sandy gravel. Additionally, the novel 1D CNN regressor model was originally made for soft ground mechanized tunnelling. The model was compared to different traditional machine learning models and deep neural networks (extratree regression, light gradient boosting, gradient boosting, random forest, AdaBoost, k-NN regression, multilayer perceptron neural network, and LSTM) to determine the model's high reliability. The main contribution of this research is that the algorithm works with soft ground mechanized tunnelling operational parameters. It provides a low computational time, which is 3 min 22 s, and it can work with high-dimensional datasets. Compared to limited site investigation information, the algorithm can work with real-time recorded machine parameters. Figure 1 shows the proposed 1D CNN disc cutter wear prediction using the information of the inspection points of the tunnel and the EPB TBM operational data. The inspection points' information was used to obtain the cutter wear index for the output data, and the operational data were used as the input features.
## 5 Research Challenges and Future Directions
### Energy Harvesting  
The total energy that is consumed by a network will ultimately decide how much it will cost its operators to set up the network. Consequently, it is essential to manage the EE of a network at the optimum level to fulfill a user's QoS and QoE requirements. The use of renewable energy sources for both MBSs and SBSs has the potential to lower the total power consumption of the HetNet, and it is anticipated that 6G will make more use of green technologies [146]. SBSs and MBSs should be able to generate their own power from the surrounding environment. Investigating the possibility of BSs working together to build a network with increased EE is yet another area of research.  
The use of collaborative task execution strategies allows for a reduction in the total energy that mobile applications in a HetNet consume. In this approach, exploring the tradeoff between the application's performance and energy consumption is vital while simultaneously implementing a power allocation and saving mode on mobile devices [147]. Nevertheless, in order to further improve energy efficiency and prevent inter-cell interference, a coordinated BS system is necessary. This plan calls for BSs to share transmission cycles with each other. In order to enhance the functionality of ultra-dense HetNets in terms of energy efficiency, considerable research must be conducted on the use of energy harvesting from renewable energy sources. During periods of low traffic, a significant number of BSs can be underutilized since they cater to relatively few users while still using a considerable amount of power. As a result, it is essential to figure out research efficient BS switching-off techniques to decrease the energy used during periods of low traffic [148]. In energy efficiency, a system that runs in THz and mmWave frequencies is superior to networks that operate at sub-6 GHz regarding future 6G networks and beyond. The theory of consumption factors supports this idea by providing a measurement for the power-to-information exchange in each communication system.
## 3 Method
### Registration Phase  
The Licensee (L), Content Administrator (CA), and Proxy (P) should register with the Blockchain Center (BCC) and obtain a relative public/private key pair. The Licensee (L) and Proxy (P) also get a digital certificate of identity from the Blockchain Center via a secure channel. The system role X can represent the Licensee (L), Content Administrator (CA), and Proxy (P). Figure 4 shows the flowchart of the registration phase.  
* Step 1: Role X generates an identity \(ID_{X}\), and sends it to the Blockchain Center.
* Step 2: The Blockchain center generates an ECDSA private key \(d_{X}\) based on the role X, calculates: \[Q_{X}=d_{X}G.\] (1) If the identity of the registered role is verified, the smart contract Xins will be triggered, and the content is presented as follows (Scheme 2):  
\[\begin{array}{l}\text{function insert x smart contract xins (}\\ \text{string x\_id, string x\_detail) {}}\\ \text{count ++;}\\ \text{x[count].id = id;}\\ \end{array}\] (2) x[count].detail = detail;  
\[\begin{array}{l}\text{x[count].id= detail;}\\ \text{string x\_keypairs;}\\ \end{array} \tag{3}\]  
Then the blockchain center will transmit \(ID_{X},(d_{X},Q_{X}),PK_{X},SK_{X},Cert_{X}\) to role X.
* Step 3: The role X stores \((d_{X},Q_{X},PK_{X},SK_{X},Cert_{X})\).
## 2 Related Work
### Invertible Neural Network  
The flow model entails the mapping of a simple a priori distribution, typically Gaussian, to the true distribution of the data through a series of invertible transformations. This mapping is invertible, allowing for both the generation of samples from the data (forward flow) and the reconstruction of the data from the samples (backward flow). The earliest flow model, NICE, was introduced by [PERSON] et al. [24]. It comprises multiple additive coupling layers, significantly enhancing the capacity to learn nonlinear deterministic transformations of data through the scaling transformation layer. Subsequently, [PERSON] et al. [25] extended NICE by introducing a convolutional neural network (CNN), resulting in RealNVP. They improved the additive coupling layer to an affine coupling layer, making it suitable for image processing tasks. [PERSON] et al. [26] further advanced the field by introducing invertible \(1\times 1\) convolution and proposing Glow, a model capable of synthesizing images with enhanced perceptual fidelity.  
Invertible neural networks serve as a practical realization of the flow model, garnering significant interest from researchers in the imaging domain. Within the realm of super-resolution, [PERSON] et al. [27] presented SRFlow, a normalized flow-based super-resolution method that effectively learns the conditional distribution of high-resolution outputs given low-resolution inputs, resulting in high-quality images. [PERSON] et al. [28] introduced conditional invertible neural network (cINN), showcasing an architecture capable of efficiently pre-processing conditional inputs into valuable features and generating diverse samples with sharper images. In the context of medical image reconstruction, [PERSON] et al. [29] introduced the conditional flow model cINN, enhancing reconstruction quality by substituting the standard Gaussian distribution with a radial Gaussian distribution. Addressing the rendering of visually appealing sRGB images, [PERSON] et al. [30] devised an invertible image signal processing (InvISP) pipeline, demonstrating improved quality in both rendered sRGB images and reconstructed RAW data. For image rescaling, [PERSON] et al. [31] developed an invertible rescaling network (IRN) capable of producing visually superior low-resolution images through the inverse bijection process, while also reconstructing high-resolution images with quality closely resembling the original image. Invertible neural networks have found application in conjunction with image steganography. [PERSON] et al. [32] introduced an invertible steganographic network (ISN) designed to conceal multiple colour images within one colour image, exhibiting enhanced steganographic capacity while maintaining good invisibility. Meanwhile, [PERSON] et al. [33] proposed the HiNet steganography framework, incorporating a low-frequency wavelet loss to enhance security while preserving high image quality. Building upon HiNet, [PERSON] et al. [34] introduced a focus map mapping module to guide the location of image steganography effectively, thereby improving overall image hiding capacity.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 7 Conclusions
### Future and Additional Research  
Additional research of course should be extended to probe the effectiveness extent against collective and coherent eavesdropping attacks for this protocol using asymptotic formulas or numerical analytic approaches [66]. We have limited our analysis to individual attacks, assuming [PERSON] only has access to public communication and the end of quantum edge of [PERSON]\({}_{1}\) before the assembling of the key. However, for collective attacks, where [PERSON] brings each quantum signal and hears all public communication between [PERSON] and [PERSON] [18], more decisive probes are needed.  
Together, extensions for the current approach in the six-states protocol direction [40; 47] should be tried with more general and complex processing to that established in (12), instead with the full form for two-qubit rotations: \(\mathcal{U}_{k}=e^{i\omega_{k}\overline{\mu}_{k}\cdot\vec{\sigma}}\), with \(\vec{\sigma}=(\mathbf{X},\mathbf{Y},\mathbf{Z})\), thus introducing additional parameters for the basis selection. In that trend, deeper elements regarding the classical authentication \(\omega_{k}\) and the mathematical relation stated by the parameter \(K\) could be oriented to well-stated methods in classical cryptography.  
An optimality analysis should be performed to reach adequate prescriptions fixing affordable values for the QBER and the eavesdropper rate of success in terms of the configurable selection of the parameters on the region settled by the Figures 7 and 9.  
**Author Contributions:** Conceptualization, F.D.; methodology, F.D.; software, C.C.-I. and F.D.; validation, C.C.-I. and F.D.; formal analysis, F.D.; investigation, C.C.-I.; resources, C.C.-I. and F.D.; data curation, C.C.-I.; writing--original draft preparation, C.C.-I. and F.D.; writing--review and editing, C.C.-I. and F.D.; visualization, C.C.-I. and F.D.; supervision, F.D.; project administration, C.C.-I. and F.D.; funding acquisition, C.C.-I. and F.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Authors acknowledge the economic support to publish this article to the School of Engineering and Science from Tecnologico de Monterrey. [PERSON] and [PERSON] acknowledge the support of CONACYT.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Key Insights into Machine Learning and Deep Learning Models for Magnitude Estimation  
The analysis of the 28 selected articles has identified diverse approaches for earthquake magnitude estimation using ML and DL techniques. Most studies utilize deep neural networks, supervised learning models, and hybrid architectures that combine different techniques to improve the accuracy and robustness of predictions.  
Table 2 presents the advantages, disadvantages, and applications of some representative models used in earthquake magnitude estimation. These models were selected based on their relevance in the literature, covering both traditional ML techniques and advanced DL architectures. The analysis of the models reveals several key trends in current research:  
* Dominance of DL models: Architectures such as Bi-LSTM [7], Transformers [46], CNN-based networks [32], and hybrid approaches like MEANet [32] and MCFrame [52] have demonstrated high capability in capturing temporal and spatial features of seismic signals. These models improve early magnitude prediction by leveraging complex feature extraction mechanisms.
* Optimization strategies: Some studies employ ensemble learning models, such as EEWPEnsembleStack [49], which combine multiple predictors to reduce variance and enhance generalization across diverse seismic datasets.
* Computational considerations: While DL models offer superior accuracy, their computational demands can pose challenges for real-time applications, particularly in resource-constrained environments. In contrast, simpler models like SVM-M [40] perform well on small datasets but may struggle to maintain accuracy and scalability for complex, large-scale earthquake events.
* Balance between speed and accuracy: In early warning systems, rapid and accurate predictions are crucial. Models like MagNet [44] and real-time CNN-based approacheshave been designed to operate efficiently, making them suitable for emergency response scenarios.
* Challenges and future directions: Despite advances in magnitude estimation, accurate prediction of large earthquakes (\(M\geq 6\)) remains challenging due to limited training data in this range. Additionally, generalizing models to different geological regions is still an open issue that requires further investigation.
## 4 Results and Discussion
### Ice Thickness Analysis along the Blade Spanwise Direction  
Using four measurement points at point 1 and point 2 and the maximum thickness in Figure 10, the thickness when not iced is subtracted from the measured thickness of the windward side and the leeward side under different icing conditions, and the change of icing thickness at 0.2R, 0.4R, 0.6R, 0.8R, and 0.95R along the blade spanwise direction can be obtained. The trend of icing thickness along the blade spanwise direction is shown in Figure 11.  
Figure 11: _Cont._  
Figure 10: Location of measurement points along the blade spanwise direction.  
From Figure 11, it can be seen that the icing thickness on the windward and leeward sides of the blade shows a nonlinear trend along the spanwise direction. From Figure 11a-c, it can be seen that when the windward side is iced, the trailing edge thickness increment changes the most along the spanwise direction, and the thickness of the trailing edge measurement point is always greater than that of the leading edge measurement point. With the increase in icing mass, the change rule of measuring points 1 and 2 along the spanwise direction is changed, and the thickness change of the measuring points at the maximum thickness shows a trend of decreasing first and then increasing. This is due to the change of the wing angle of attack along the spanwise direction, and the windward wing curvature changes more and the location of ice accumulation changes significantly. From Figure 11d-f, it can be seen that when icing occurs on the leeward side, the thickness of the leading edge changes most significantly along the spanwise direction. With the increase in icing mass, the overall change trend of the three measurement points along the spanwise direction remains stable. The change in the angle of attack of the airfoil affects the location of the thickness change of the measurement points along the spanwise direction. The thickness changes on the windward side near 0.4R and 0.8R are obvious. On the leeward side, the thickness at 0.4R increases significantly with the increase in icing mass, and then decreases.
# A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture
## 4 Testbed
### Design  
The following requirements had to be met in the design phase of the testbed:  
* Initially oriented towards FIWARE but adaptable for use with other platforms thus enabling comparative studies.
* Oriented towards measurement of performance and data storage capacity but extendible to include other properties.
* Establishment of data generation parameters: number of IoT nodes, data frequency generation and payload.
* Establishment of test parameters in FILAB: number and characteristics of virtual machines (VMs) deployed, number and characteristics of Generic Enablers instantiated and parameters of the Cygnus-NGSI injector.
* Test settings and autostart against FIWARE instances.  
The testbed designed (Figure 4) takes the most important elements of the case study shown in Figure 3 into account, including (1) a context generating application, (2) instances of reference implementations of the Context Broker (Orion) and Big Data Analysis (Cosmos), (3) a client application (Hive) that acts as the FMS application and (4) the Cygnus-NOSI injector.  
The context generator application follows the client/server architecture and runs on 13 machines located in one of the laboratories of the research group (Electronics Engineering and Systems Division, DSIE) of the Universidad Politecnica de Cartagena. This application simulates the generation of context information from an IoT node network by implementing RESTful (NGSI) clients. One design criterion is that each IoT node simulates a producer of context that can refer to an intermediate node (e.g., IoT _Broker_) or a final NGSI node (see Figure 2) that can integrate multiple sensors. In these cases, requests to the _Context Broker_ are transmitted directly to update the context corresponding to entities that can be modelled on future IoT systems, posing the challenge of scalability discussed in the previous section.  
The Http source of the Cygnus-NSGI injector is responsible for receiving events from the _Context Broker_, transforming them into Flume events [55] and injecting them into the channel. The Cygnus sink _(Orion HDFS Sink)_ extracts the Flume events from the channel and uses the API WebHDFS/HttpFS [53] to achieve persistence of the context information contained in the body of the Flume events in the _Hadoop Distributed File System_[53] set up in Cosmos, creating a historical view of such data. Also, the application of FMS implements a Hive client [53] for the execution of _MapReduce_ tasks associated with new generation mining and analysis techniques (reasoning models) that process historical context information stored in Cosmos. The resulting perceptions are extracted through _HiveQL_ language.
# A Mobile Gait Training System Providing an Active Interaction
## 2 Proposed System
### Sensing Part  
The sensing part is responsible for measuring the trainee's feet coordinates when the trainee interacts with the training content provided by the system. It shown in Figure 2 provides a measurement of 2D pointcloud data and a measurement of foot coordinates. Generally, trainees are accompanied by gait aids (walkers, cane, etc.) because they have difficulty in gait. Therefore, the sensing part of the system distinguishes the trainee's feet from the assistive device and measures the feet's coordinates. The sensing part measures the trainee's feet coordinates based on 2D point cloud data and uses a 2D-LIDAR. as a sensor for data collection. Figure 3 shows the procedure for measuring the feet coordinates of a trainee using the 2D-LIDAR.  
First, in step (a), 2D point cloud data are collected in real time through a sensor. In step (b), 2D point cloud data collected through the clustering process is classified into one object unit. In step (c), the gait aid devices and the trainee's feet are distinguished. All objects classified in the clustering process are used as foot position candidates, and the candidates  
Figure 3: A procedure for foot coordinate measurement of sensing part: (**a**) 2D point cloud collection; (**b**) derivation of candidates through clustering; (**c**) distinction between gait aid devices and foot; (**d**) measurement of foot coordinates.  
Figure 2: Proposed system’s block diagram.  
are compared to find the actual trainee's foot position. Since gait aids generally have sizes and thicknesses smaller than those of the trainee's foot, the trainee's foot classification proceeds based on the size of the objects. By comparing the sizes of the candidates, the two largest candidates are selected as the feet of the trainer. Finally, in step (d), among the selected two candidates, the candidate on the right is recognized as the right foot, and the candidate on the left is recognized as the left foot.  
Through the above procedure, the sensing part measures the coordinates of the trainee's feet in real time and transmits them to the moving part and the augmenting part, allowing the robot to automatically move and the trainee to interact with the content.