esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### The Experiments for Measuring Dynamic Object  
In order to verify the effectiveness and the practicality of the proposed method for measuring the 3D shapes of dynamic object, several experiments are conducted. One of them is to measuring the swinging \"heart\" model. The deformed patterns of the swinging \"heart\" model, in different states, are captured at 78 fps. The corresponding reconstructed 3D shapes of the swinging \"heart\" model are reconstructed with the proposed method. Figure 13a-c show three states (state 1, state 2, and state 3) of the captured deformed patterns from R channel. The corresponding reconstructed 3D shapes at above three states are shown in Figure 13d-f. It can be seen that the 3D shapes of the measured swinging \"heart\" model at different states can be also well reconstructed with the proposed method. Therefore, the proposed method is effective for measuring the dynamic objects. If the higher frame rate camera and HDLP are used, the deformed patterns contained the 3D shape information of the measured dynamic object will be obtained at higher speed, the proposed method has potential application in superfast, dynamic 3D shape measurement. Although, with the additional Fourier transform, low-pass filtering, and inverse Fourier transform used in the proposed method, which may be for more time consuming, the proposed method can realize dynamic 3D shape information acquisition. If the graphic processing unit (GPU) acceleration calculation technique [37] is used in the proposed method, it will make the proposed method realize the real-time dynamic 3D shape reconstruction which is into our further research.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{h_{0}}\) & 3.00 & 5.00 & 9.00 & 14.00 & 25.00 & 35.00 \\ \(\mathbf{h_{avg}}\) & 2.945 & 5.060 & 8.965 & 13.950 & 24.943 & 34.926 \\
**MAE** & 0.065 & 0.070 & 0.074 & 0.060 & 0.078 & 0.082 \\
**RMS** & 0.034 & 0.041 & 0.037 & 0.033 & 0.042 & 0.045 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measuring results for different planes with known heights (mm).  
Figure 12: Experiment result for measuring 5.00 mm plane: (**a**) Reconstructed result and (**b**) cutaway view at 150 column in (**a**).
# Quantum Neural Networks Approach for Water Discharge Forecast
## 2 Methodology and Data Series
### Quantum Neural Network  
#### 2.2.1 Basic Concept of Quantum Neural Network  
The Quantum Neural Network (QNN) is a computational model that integrates quantum computing and neural networks. It aims to leverage quantum computing properties such as quantum superposition, quantum entanglement, and quantum parallelism to enhance the expressiveness and computational efficiency of neural networks. QNNs replace classical neural networks' traditional weights and activation functions with Variational Quantum Circuits (VQCs) to perform learning tasks [37].  
The qubit is the fundamental unit that carries information, whose state can be described by  
\[\ket{\phi}=\alpha\ket{0}+\beta\ket{1} \tag{1}\]  
where \(\alpha\) and \(\beta\) are complex numbers whose sum of squared modulus is 1.  
Quantum data includes the quantum states (\(\ket{0}\), \(\ket{1}\)), superposition states (e.g., \(\ket{0}+\ket{1}\)), and entangled states. Quantum registers are used to organize the data.  
Quantum Gates (e.g., Hadamard, Pauli, CNOT, Toffoli) have the role of manipulating the qubits for computation. For example, the U gate performs a single-qubit rotation, whereas Rx (Ry, and Rz) performs a rotation around the Ox (Oy, and Oz) axis [47].  
In the QNN structure, input data are first mapped to the quantum state space and then transformed through parameterized quantum gate operations. The final computation results are obtained via quantum measurement, and classical optimization methods are used to adjust circuit parameters to minimize the loss function, thus completing the training process. Compared to classical neural networks, QNNs take advantage of the unique physical properties of quantum computers, which may provide computational acceleration and superior feature mapping capabilities in specific tasks.
## 5 Conclusions  
In this paper, a long-term corrosion depth prediction model for carbon steel applicable of the Ningbo area was established, a mixed climate multi-wind direction extreme wind speed estimation method was developed and wind-induced fragility curves for the transmission tower under different corrosion ages were obtained, which can be used to quantitatively evaluate the effect of long-term corrosion on the deterioration of wind resistance of a steel transmission tower during its service life. The specific findings are as follows:  
(1) The corrosion rates of carbon steel in different atmospheric environments in the Ningbo area are in descending order: industrial-marine (I-M) atmosphere, marine (M) atmosphere, urban/industrial (U/I) atmosphere and rural (R) atmosphere. Among them, the corrosion depth of carbon steel exceeds 1600 \(\upmu\)m under the 100-year corrosion age of the I-M atmospheric environment, which indicates that, in such an atmospheric environment, transmission towers are the most vulnerable to corrosion;  
(2) The wind climate in the Ningbo area is mainly controlled by typhoons, and the design wind speed given by the current load code is on the risky side;  
(3) With the increase of the corrosion ages, the nominal mechanical properties of steel decrease, making the probability of minor damage, moderate damage and severe damage to the transmission tower increase. Under the wind loads of 50-year return periods in the most unfavorable wind direction in the Ningbo area, the tower will hardly experience severe damage and collapse, the probability of moderate damage is within 10% and the probability of minor damage is controlled between 10% and 40%.  
It is worth mentioning that the maximum displacement of the top of the transmission tower was selected as the load effect index in this paper, and the probability of the maximum displacement of the top exceeding the limit value under different corrosion years was taken as the failure probability. In fact, there are various structural failure modes of transmission towers under wind loads, and wind-induced fatigue is one of the important failure factors. It is of great practical significance to conduct a structural wind-induced fatigue analysis based on the cumulative damage theory and probabilistic fracture mechanics theory for the transmission tower structures in future studies.  
Conceptualization, Q.L.; Writing--original draft preparation, H.J. and Q.Q.; Writing--review and editing, Q.L. and Y.L.; Supervision, J.Z., J.M., W.F. and M.H. All authors have read and agreed to the published version of the manuscript.  
Figure 21: Wind-induced fragility curves of the transmission tower with different corrosion ages under the industrial-marine atmospheric environment in the Ningbo area. (**a**) Minor damage. (**b**) moderate damage and (**c**) severe damage and collapse.  
Funding: This research was funded by The National Natural Science Foundation of China (51908496 and 51820105012), The Natural Science Foundation of Zhejiang Province and Ningbo City (LQ20E080001 and 2021J168) and the Science and Technology Special Project of Ningbo Fenghua District (202008502).
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 5 Evaluation Results
### Global Analysis: GPS Locations of Outliers  
As previously mentioned, as a global analysis operation, the outliers detected by the RC and IF algorithms have been linked with the GPS locations available in the datasets. Only measurements marked as outliers by both algorithms have been considered. In Figure 13, the GPS locations where PM10 values above 70 ppm have been collected by the bpost cars, which have been marked as outliers by both RC and IF outlier detection algorithms, are shown on a map of the city. Regarding bpost Car 1 measurements, most of the data samples marked as outliers have been collected in the warehouse (area highlighted in orange in Figure 13), where the bpost cars usually stay at night. These high values of PM10 can be related to organic compounds, which were inside the warehouse at the time of the measurements. On the other hand, the outliers measured by bpost Car 2 have been collected across the city of Antwerp. These values can be related to high traffic volumes in the city at the time of the measurements. Furthermore, a large number of samples collected by bpost Car 2 have been marked as outliers, when the car stayed in the warehouse. These outliers can be explained by the high relative humidity and the high values of the PM10 indicator.  
Global analysis operations should be conducted in the cloud layer so that unusual measurements can be understood. This way, citizens and government agencies can receive live input of the city traffic, which should be used to temporarily modify traffic rules, e.g., reduce the maximum driving speed on certain highways. Furthermore, in the long term, these global operations can provide an effective way on how city policies should be changed.  
Figure 13: GPS locations collected by the bpost cars (bpost1, red/bpost2, blue). These values have been marked as outliers by both outlier detection algorithms considered in the evaluation.
## 4 Experimental Results
### Data Washing Algorithm  
The Data Washing algorithm was implemented using the denoising autoencoder proposed in [35]. As described in Section 3.2.1, a lightweight Gaussian white noise, which here equals 0.05 times Gaussian white noise, was added prior to the second denoising process. The autoencoder was trained using the Cifar100 [36] dataset. The performance of the Data Washing algorithm was evaluated for four different attack models, namely, the TensorFlowClog and Category Diverse paralysis attack [33] models, and the Feature Collisions (FC) and Convex Polytope (CP) target attack models. The paralysis attacks were conducted using the parameters described in Section 4.1. The FC attacks [6] were implemented using the model results obtained after 100 training epochs, with parameter setting \(\beta=0.2\times 2048^{2}/\text{dim}_{b}{}^{2}\), a learning rate of 127,500, and a maximum number of iterations equal to 1500. Finally, the CP attacks [4] were implemented using the results obtained from 60 training epochs, namely, an initial value of \(c^{(i)}=1/10\), a maximum change limit of \(\epsilon=0.1\), a learning rate of 0.04, and a maximum number of iterations equal to 4000. CP attack produced five attack data items for each target.
## 1 Introduction
### Extracting Single Lava Fields between March 1998 and April 2007  
At Piton de la Fournaise, lava flows are often superimposed on those emitted during earlier eruptions (Figure 1). Such superimposition of two or multiple lava flows can induce more complex processes, such as compaction in the rough interface between two flows, subsequent thermal contraction of the whole composite lava, and the acceleration of creep in the substrate resulting from the heat transfer from the above to the underlying flow [2]. In this study, we therefore only consider the displacement restricted to single lava flow fields (Figure 3), attempting to avoid the considerations of interactions between lava flows. The selection of lava flow fields followed three principles:  
1. No superimposition. Only single lava flow (emplaced between March 1998 and April 2007) covering areas were selected by spatial analysis. For instance, the light blue area in Figure 3 is only covered by the Apr 2007 lava flow, neither by lava flow emplaced before nor after Apr 2007.
2. Emplacements on volcano flanks. We extracted the lava fields situated away from the summit cone and affected by long-term displacement rather than transient eruptive displacement.
3. InSAR coherent area. The selected areas were required to be coherent in InSAR data. Some flow areas where significant displacement was supposed to occur, however, did not remain to be coherent in InSAR data. These areas were not taken into account in the analysis.  
Nine lava flow fields were eventually extracted, including the Mar 1998a-b, Feb 2000, June 2000, Oct 2000, June 2001, Nov 2002, Aug 2004, and Apr 2007 lava fields (named after their date of emplacements; Figure 3).
# Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic
## 3 Methodology
### Membership Function Parameters  
Initially, membership functions are established for each of the identified environmental variables. For the controller, three reference moods and three output states are defined: activated, relaxed, and neutral. These states constitute the membership functions and describe how the different measurements are assigned to each fuzzy category.  
Using linguistic descriptors, fuzzy rules that relate environmental variables to behavioral states and associated colors are created. For instance, a rule might state \"The desired state is 'Relaxed' IF UV radiation is low, temperature is HIGH, and relative humidity is HIGH THEN the behavioral state must be relaxed, and the lighting must be blue with HIGH power\" (shows Figure 4). The reference acts as the control input, alongside readings from sensors measuring relative humidity, temperature, UV index, and acoustic pressure. The values considered are averaged, as per Table 2.  
Figure 3: Design output controller.  
The program performs conceivable combinations of all variables, aligning with the references provided in the Table 2, to achieve an impact aligned with the intended stimulus, recognizing that input and output conditions are discrete. Conditions use the fuzzy inference engine to assess fuzzy rules and determine the corresponding lighting color and required power. The inference engine will calculate the membership degrees of the environmental variables to the membership functions and will apply fuzzy rules to obtain results.  
The output of the inference engine generates an 8-bit PWM signal to control RGB lights, adjusting the intensity levels of each color (red, green, and blue) according to the colors associated with the given behavioral state.  
Data can be gathered to develop a lighting system that enhances the desired behavior, not only by relying on lighting references, but also by incorporating a suggested mood in the environment [38].
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 4 Application and Results
### Data Processing
#### 4.2.2 Validation of the Marginal Distribution
* Expert Validation  
Expert face validation is used to assess the practical applicability of a proposed method by having a subject-matter expert evaluate the results of a proposed method [63]. The illustrative case study results and sensitivity analysis were discussed with three experts (i.e., a director, a project manager, and project coordinator) from a large construction company in Alberta, Canada--each with an average of 15 years of practical experience in construction management and risk analysis. Definitions of the terms representative, comprehensive, and ease of use (described as follows) were provided to the experts. Then, experts were asked whether or not they believed that the proposed method was characterized by each definition. Results are detailed as follows. The experts agreed that the proposed method was representative, defined here as the ability of a method to representatively express the knowledge of the expert as a probability distribution function. In particular, findings that distribution shape is sensitive to the mapping values of the range impact increased their confidence that the representation was appropriate. The experts also agreed that the proposed method was comprehensive, defined here as the ability of a method to include available information regarding the risk impact. They were satisfied with the level of information that the method allows them to incorporate while deriving the probability distribution. In contrast, the experts indicated that the proposed method was, in its current form, not easy to use--particularly by analysts that may not be familiar with fuzzy logic computations. They agreed that full computerization of the approach would considerably facilitate its application in industry. Accordingly, this method was computerized within an in-house developed simulation engine, _SimphonyProject.NET_[47], developed for integrated assessment of risks.
## 4 Factors Affecting the Quality of User Experience  
### Legal-Regulatory Factors  
Legal-regulatory factors include the application of standards and legal norms, and special attention in this paper is given to norms defined by international and European standardization bodies in the field of telecommunications, which are related to the quality of user experience. These are the 3 rd Generation Partnership Project (3 GPP), the ETSI and the ITU-T. In the focus of the research are the following documents of the aforementioned organizations, which can be classified into three categories:  
* Technical specifications (TS): ETSI TS 103 294 V1.1.1 (2014-12) [5], ETSI TS 102 250-2 V2.4.1 (2015-05) [43], ETSI TS 102 250-1 V2.2.1 (2011-04) [44].
* Technical reports (TR): ETSI TR 103 488 V1.1.1 (2019-01) [45], 3 GPP TR 26.944 V10.0.0 (2011-03) [46], ETSI TR 102 643 V1.0.1 (2009-12) [6].
* Recommendations (R): ITU-T Recommendation P.10/G.100 (11/2017) [10], ITU-T Recommendation G.1000 [47], ITU-T Recommendation P.10/G.100, Amendment 2 [8].  
Within the documents, particularly important are normative or prescriptive definitions of terms related to \(QoE\) and QoS [24], which affect the correct establishment of relations between them. Considering that normativity represents theory, order and truth, it means that this type of definition prescribes \"how something should look in reality\" in order to achieve a satisfactory level of \(QoE\). Apart from the definitions, the very important content of the mentioned norms are procedures for measuring \(QoE\) and glossaries of terms.  
Subscribers sign contracts with the operator, which regulate interrelations regarding the provision/use of a service. According to the General Terms and Conditions for the provision of telecommunication services of the Mtel operator, a subscriber is _\"any legal or physical person who has concluded a contract with Mtel for using telecommunications services that are the subject of these General Terms and Conditions\"_[48]. Among other things, contracts can also regulate the minimum levels of QoS that the operator should deliver to users, as well as the maintenance of network components and services. Terminal user equipment/devicesare covered by manufacturers' guarantees. Unlike the General Conditions that apply to all services, the conditions for the provision of individual telecommunication services, such as prices, are regulated by Special Conditions.
# Body Area Networks in Healthcare: A Brief State of the Art
## Data Link Layer Properties  
### Properties According to IEEE 802.15.16 Standard  
The data link layer includes the medium access control (MAC) sublayer, which manages access and contention to the channel. Data are transmitted using time-domain multiplexing. The hub splits the time axis into a series of super-frames. The MAC specification supports QoS, medical implant communication service (MICS), emergency communications, interference mitigation, and macroscopic and microscopic power management. IEEE 802.15.6 has the following operation modes: beacon mode with beacon period super-frame boundaries; non-beacon mode with super-frame boundaries; and non-beacon mode without super-frame boundaries.  
A survey on different MAC protocols and their comparison for radio BANs can be consulted [44]. In a study, the focus was put on the integration of fuzzy-logic based routing with a time-division multiple access MAC protocol [45]. In addition, the improvement of the IEEE 802.15.4 MAC protocol has been addressed in [46]. The authors present the so-called BANMAC, which is a MAC protocol capable of monitoring and predicting channel fluctuations and scheduling transmissions, improving packet loss and reducing power consumption.
## 3 Results
### Simulation Results
#### 3.3.1 Simulation Results of MCRF  
The occurrence probability map of the simulation results obtained from the sample pixels is shown in Figure 6. The deeper the hue is, the higher the likelihood of a category to be correct, and vice versa. The black part indicates that the confidence level of the type is nearly 1, i.e., it can be considered almost 100% certain. The white part implies that the confidence level of the type is approximately 0, which can be considered impossible. The color of the forest is the deepest, and the range is the widest, suggesting that the reliability of the forest land is highest. Although the scope of the cultivated land is small, the color is deep. As such, the simulation of the cultivated land is reliable. For grassland, few dark places and many gray areas are found, implying that the simulation of the grassland is insufficiently reliable. The light tone part can be considered the warning area for further survey. Wetlands and water bodies are always accompanied by each other, thus their hues are almost complementary, and the range of gray colors is large. Therefore, wetlands and water bodies are easily misclassified.  
The optimal prediction map of the simulation results is shown in Figure 7, which was the assembly of the most likely type of each location. The coincidence degree of the forest, cultivated land, and part of grassland, wetland, and water bodies was higher than that of the original classification products (Globeland30-2015), and the other places with deviation were likely to be the ones with false classifications.