esa-sceva/satcom-chunk-collection huggingface dataset:
# Cloud PricingOps: A Decision Support Framework to Explore Pricing Policies of Cloud Services
## 2 Related Work
### Cloud Decision Support Frameworks
#### 2.2.1 Theoretical Frameworks  
The theoretical roots of decision support systems essentially lie in solving a multi-criteria decision-making (MCDM) [16]. The core of multi-criteria decision-making (MCDM) is to assist decision-makers who have various preferences. Typically, MCDM problems do not have a single optimal solution; instead, the goal is to find the most suitable solution based on the decision-makers' preferences. In cloud computing, decision support systemstypically apply multi-criteria decision-making (MCDM) solutions using various methods, such as the analytical hierarchy process (AHP).  
The method's effectiveness relies on the appropriate selection of criteria. To aid in this, popular frameworks can be utilized to guide criteria selection, or the primary factors influencing the decision-making process itself can be considered as criteria.  
The adoption of cloud computing within the Technology-Organization-Environment framework [17] was explored in [18; 19]. Specifically, in [18], the authors developed a decision support model for adopting a cloud computing system by analyzing critical variables in a hierarchical structure. This involved examining decision areas such as technology, organization, and environment, along with seven factors and twenty-three attributes. Additionally, the research explored a comparative analysis between demanders and providers of cloud computing adoption to suggest important factors for successful adoption, such as top management support, competitive pressure, and compatibility. The study employed the Analytic Hierarchy Process (AHP) and Delphi analysis to identified these underlying decision factors for cloud computing adoption.  
Additionally, in [19], the authors identified and ranked critical factors affecting cloud computing adoption in the Indian context. A mixed-methods approach, combining qualitative and quantitative techniques, was utilized. Experts from 13 organizations, comprising 8 cloud service providers and 5 cloud service users, participated in interviews. Content analysis helped validate existing factors and reveal new ones relevant to the Indian setting.  
In [20], an integrated approach was proposed that combined the Analytic Hierarchy Process (AHP) and the TOPSIS method to identify the optimal machine learning-as-a-Service (MLaaS) cloud. Key functional attributes of MLaaS were established as criteria, with the AHP used to calculate the weights of these criteria, while the TOPSIS method was employed to rank the alternatives.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 3 Study Cases
### Application 6: Earth Observation for Vessel Detection
A similar approach is also used for change detection (CD) in urban areas, which adopts a convolutional neural network (CNN) algorithm to process the SAR-generated dataset and retrieve changes of urban structures and buildings (e.g., [269]).
## 1 Introduction
Thus, cellulose grafting using bacterial sources could contribute to exceeding these limitations, providing polymers with excellent mechanical properties, increased surface area and aspect ratio, and low coefficients of thermal expansion while being a good candidate for crosslinking or grafting [6]. Traditionally the symbiotic culture of bacteria and yeast (SCOBY) is used to obtain a fermented drink based on black or green tea, known as Kombucha, rich in antioxidants, phenols, glucuronic acid, and B complex vitamins [14]. The fermentation of the sweetened tea is ensured by the microbial consortium included in an exogenous cellulosic matrix, produced by the acetic bacteria in the consortium, which provides protection to all species of acetic bacteria (_Komagateiobacter_, _Glucobacter_, and _Acetobacter_), probiotic lactic bacteria (_Lactobacillus_, _Bifidobacterium_) and yeasts resistant to osmotic stress (_Brettanomyces_, _Zygosaccharomyces_) that develop in symbiosis [15], forming simultaneously with fermentation three-dimensional layers in the form of a hydrogel [16]. Since the SCOBY cell biomass is formed in quantities that overpasses what is needed to obtain the traditionally fermented drink, an unused surplus of cellulosic membrane results. The high economic potential for valorization of bacterial cellulose compared to vegetable cellulose is due to its high chemical purity, as bacterial cellulose is not accompanied by hemicellulose, pectin, or lignin. Microbial cellulose is characterized by high biodegradability and high biocompatibility with food products, properties characteristic of cellulosic materials (high mechanical resistance, good chemical stability), high crystallinity (over 60%), low density (1.6 g\(\cdot\)cm\({}^{-3}\)) [17], easier purification compared to the polluting and expensive methods applied to vegetable cellulose, as well as the regeneration at low costs and in a relatively short time compared to the regeneration of vegetable cellulose [18; 19; 20]. The greatly simplified structure of bacterial cellulose compared to the three-dimensional structure of plant cellulose, where cellulose microfibrils are surrounded by hemicellulose and lignin, increases the accessibility of bacterial cellulose. This study evaluates the potential of bacterial nanocellulose from SCOBY grafted in yeast films to improve the film properties and study the influence of concentrations of glycerol as plasticizer and emulsifier Tween 80 on mechanical properties, volatile fingerprint, and antimicrobial activity of films. To the best of authors' knowledge, this is the first study to evaluate the possibility of combining these two microbial resources in order to obtain a biopolymeric film.
## 3 Results and Discussion
### Physical Activity
#### 3.5.2 Is It Possible to Make a Specific Recommendation on Physical Exercise?  
**Recommendation 22**.: _GARIN recommends regular physical activity according to the WHO guidelines for adults with chronic diseases._  
Grade of recommendation GPP, (96%) Strong consensus.  
**Recommendation 23**.: _GARIN advises incorporating behavioral change strategies to increase physical activity engagement in COPD patients._  
Grade of recommendation O, (86%) Consensus.  
Comment:  
People with COPD are less active than people without COPD. Most reduce their activity levels in the early stages of the disease, walk at a slower pace, and generally do not meet the physical activity criteria recommended by the WHO. Patients avoid activities that involve physical exertion and increased symptoms, thus perpetuating a vicious cycle in which lack of exercise further compromises the physical ability to participate in any activity [30; 37; 56; 104; 134; 135; 136]. Physical inactivity leads to higher rates of morbidity, increased risk of premature mortality and hospitalization, and decreased quality of life [137]. Therefore, physical activity is identified as a potentially modifiable target that could be related to the patient's disease progression and quality of life [30; 138; 139; 113; 135].  
Although strategies to increase physical activity have been proposed, some studies have found that these only lead to a 2% increase in the time spent in daily activity, and therefore, recommendations to decrease the time spent in sedentary activities are advocated [137].  
The members of GARIN have found no evidence of specific recommendations for physical activity in daily life for COPD patients and, therefore, consider that it is advisable to promote activities following the WHO recommendations for the general population [140].
# Forward Fall Detection Using Inertial Data and Machine Learning
## 3 Development of the Fall Detection Models  
After the acceleration data is obtained and normalized for normal gait, damped forward fall, and undamped forward fall events, the data is used as input to train one deep learning model, using the LSTM sequential neural network at first, and a KNN supervised classifier.  
Long Short-Term Memory Neural Networks (LSTM) represent the predominant type of recurrent neural network (RNN) utilized in fall detection [25,26]. As demonstrated in [25], the basic topology of an LSTM network was developed using a 3-neuron input layer, two Gate Recurrent Unit (GRU) layers of 20 neurons each, and a final stage of two neuron Soft-Max layers [25]. The RNN is trained using raw acceleration data and the results obtained showcase a better performance than traditional models such as Naive Bayes and Support Vector Machines.  
As a second step for detecting falls, we chose the k-NN classifier, which is among the simplest classifiers [27] but has performance comparable to the most complicated classifiers. This classifier's core primarily measures the similarity or distance between the train and test data. Finding the query's nearest neighbor is the primary goal of k-NN. Therefore, distance is the main k-NN parameter, and the way the distances are calculated greatly affects how accurate the predictions are. For the k-NN classifier, we specify the number of the cross-validation to 5-fold (i.e., k = 5), meaning that the dataset is split into 5 subsets.  
Figure 11: Training data acquired through simulation for the forward fall: (**a**) Recorded speed on the X and Y axis [m/s]; (**b**) Recorded acceleration on the X and Y axis.  
Figure 10: Forward fall simulation.  
For each iteration, the model is trained on 4 subsets (80% of the data) and tested on the 1 remaining subset (20%).  
The obtained training data is labeled by considering the gait output as 0 or \"No fall\", the damped forward fall as 1 or Damped_Fall, and the forward fall as 2 or Forward_Fall. The number outputs are used for the LSTM network and the string outputs for the KNN model.
## 6 Open Challenges and Related Work
### Open Challenges  
In a smart home, the above four algorithms can meet the data transmission requirements of active wireless sensors. However, such sensors require battery power and have a limited life. Passive low-power transmission can greatly extend the life of the household wireless sensors but is limited by power consumption. Therefore, the research on packet detection algorithms is still an important subject. At present, there are still three main challenges.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 3 Analog Fronthaul Signal Processing
### Signal Combining at the Fronthaul Output
In fact, the useful signal is received at the BBU not only through the direct path, which has unit gain, but also from the remaining \(l_{S}-1\) interfering paths, each with gain \(\gamma\), which constructively contribute to the overall SNR after the combiner. More precisely, it can be observed that, in case of full redundancy, the SNR of the radio signal \(\mathbf{Y}\) at the BBU is increased by the analog fronthaul links by a factor of \((1+\gamma(l_{S}-1))^{2}/(1/\eta)=l_{S}(1+\gamma(l_{S}-1))^{2}\). As a result, in this case, for a coupling factor \(\gamma>0\), the SNR at the BBU increases with the cube of the number of fronthaul links \(l_{S}\). In contrast, for \(\mu=1/l_{S}\), the equivalent fronthaul channel reflects the fact that signals forwarded over the different pairs interfere with each other, and is equal to \(\mathbf{H}_{\mathbf{c}}^{\eta}=\mathbf{H}_{\mathbf{c}}\). The beneficial effect of redundantly transmitting radio signals over different pairs is reflected also in the power of the noise after the combiner, which is reduced proportionally to the bandwidth amplification factor \(\eta\).
###### Abstract  
Odometry is a computation method that provides a periodic estimation of the relative displacements performed by a mobile robot based on its inverse kinematic matrix, its previous orientation and position, and the estimation of the angular rotational velocity of its driving wheels. Odometry is cumulatively updated from tens to hundreds of times per second, so any inaccuracy in the definition of the inverse kinematic matrix of a robot leads to systematic trajectory errors. This paper proposes a non-parametric calibration of the inverse kinematic (IK) matrix of a three-wheeled omnidirectional mobile robot based on the use of genetic algorithms (GA) to minimize the positioning error registered in a set of calibration trajectories. The application of this non-parametric procedure has provided an average improvement of 82% in the estimation of the final position and orientation of the mobile robot. This is similar to the improvement achieved with analogous parametric methods. The advantage of this non-parametric approach is that it covers a larger search space because it eliminates the need to define feasible physical limits to the search performed to calibrate the inverse kinematic matrix of the mobile robot.  
odometry; odometry calibration; inverse kinematic; omnidirectional mobile robot Article
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network
### Hardware  
#### 3.1.1 The Gateway  
For this network, a low-cost LoRa gateway based on a Raspberry Pi [75] was developed. Raspberry Pi has been chosen for different reasons: (i) it can run an operating system based on Linux [76], (ii) it includes multiple communication protocols such as serial peripheral interface (SPI) and TCP/IP, (iii) it has a large development community providing a great amount of code examples and libraries, (iv) its accessibility and hardware price, (v) numerous developments based on Raspberry Pi technology for volcano monitoring show its reliability for its application within this field [15,16,77,78], and (vi) the existence of different great potential units such as Raspberry Shake and Raspberry Boom [79] that could complement the presented network, achieving a complete R-Pi-based IoT monitoring network.  
Among Raspberry Pi boards, the model 3B [80] has been chosen because it was the latest product available when the development was carried out. However, the designed gateway is compatible with all the existing Raspberry Pi boards.  
Raspberry Pi and a Ra-02 LoRa module were used to build the gateway core along with a 3-dBi directional antenna (Figure 3).  
The total consumption of this device was not a key point, since it was thought to be deployed in a place with access to the electrical supply network.  
Figure 3: Gateway. Configuration (**a**) and (**b**) and general hardware scheme (**c**).  
Figure 2: Network operation scheme.
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 4 Threat Scenarios and Results
### Detection Results
Firstly, the AE-GRU-based methods leverage the power of deep learning architectures, specifically autoencoders and GRUs, to effectively model the complex temporal dependencies inherent in time-series data such as network traffic. This enables them to capture the subtle patterns and anomalies that may be missed by the standalone methods. Additionally, the AE-GRU models are capable of learning the high-level representations of data features through unsupervised learning, allowing them to create more informative and discriminative feature representations compared to the standalone methods. Figure 10 depicts the average performance metrics of the anomaly detection methods considered. The benefit of evaluating the average performance metrics is providing a comprehensive assessment of each method's overall effectiveness across different types of attacks. By averaging the results over multiple scenarios, we gain insights into the methods' robustness and adaptability in detecting the anomalies commonly encountered in smart grid networks. We observe that the AE-GRU-based EE and LOF methods stand out as the top performers, achieving high accuracy and F1-scores of 0.91 and 0.95, respectively. These methods also demonstrate superior precision and recall, indicating their ability to effectively identify anomalies while minimizing false positives and false negatives. Furthermore, Figure 10 underscores the advantage of leveraging advanced techniques such as the AE-GRU-EE, which combines autoencoder-based feature extraction with a semi-supervised anomaly detection method, resulting in a superior detection performance. Similarly, the AE-GRU-LOF method, a density-based outlier detection method, proves highly effective in anomaly detection tasks. Figure 9: Detection performance comparison of (**a**) the AE-GRU-based and (**b**) standalone anomaly detection methods during the rogue device attack scenario.
# Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function
## 4 Momentum Wheel Reliability Calculation Based on Copula Function  
### Several Copula Functions  
The single degenerate quantities of the equipment are independent of each other. According to the mutually independent random variable theory in probability theory, the residual distribution function of the equipment remaining life obtained under the single degenerate quantity is multiplied, and the joint distribution function of the remaining life of the equipment can be obtained. If the correlation of the single-degenerate quantities of the equipment is known, that is, the correlation coefficient and the covariance are known, the joint distribution of the remaining life of the equipment can be obtained through the covariance matrix in the mathematical statistics. If the correlation of the single-degenerate quantities of the equipment is unknown, neither of the above two methods are feasible [12]. In engineering practice, the correlation between degenerate variables is often unknown. Given the marginal distribution of individual degenerative quantities, how to determine their joint distribution becomes a very important issue. There is a correlation between the residual amount of lubricant in the satellite's momentum wheel and the current, but the correlation coefficient is unknown. In view of this situation, this paper chooses Copula function to derive the joint distribution function of the remaining life of the equipment.  
Table 1 shows three widely used binary copula functions, where \(u\) and \(v\), are the residual lifetime marginal distribution functions of the momentum wheel corresponding to the remaining lubricant quantity and current, i.e., \(u=F_{1}(t)\), \(v=F_{2}(t)\); \(\gamma\) is the correlation coefficient, when \(\gamma\) at the lower boundary value, the random variable \(u\), \(v\) tends to beindependent. When \(\gamma\) takes the upper boundary value, the random variable \(u\), \(v\) tends to be completely correlated.  
The selection of Different Copula functions significantly influences the description of the correlation between variables. The probability density of the Frank Copula function has symmetry and can be used to describe the correlation between variables with symmetric structures. It is not appropriate to describe the correlation between asymmetric structures of variables; the Gumbel Copula function mainly describes the features with strong tail-related features. The correlation between the variables cannot capture the correlation between the tails of the variables; the Clayton Copula function emphasizes that the variables have stronger characteristics of the lower tail, and the change of the correlation of the variables at the upper tail is not sensitive.
## 3 Proposed System
### System Development  
Figure 2 shows a simulation designed for the demonstration testing. Prior to the experiment, a demo model was created and tested to confirm that efficient training and interaction are possible with the proposed system. The test assesses the proficiency of the tools required for engineering and involves three people, the minimum standard for multi-user participation. All three users accessed the simulation simultaneously in different locations via different PCs, VR devices, and networks. Two users used an Oculus Rift S, whereas the other had an HTC Vive Pro Eye. The users were instructed to put on the VR devices after playing the simulation on a PC.  
Figure 3 shows the test procedure. First, the user selects a role in the lobby between Expert and Beginner. In the demo test, one user was an Expert and the other two were Beginners. All three users selected the same mid-level room for entry. At this stage, the  
Figure 2: Simulation designed for demo testing.  
network environment must be smooth so that all users can meet in the same virtual environment. Upon entering the room, users can receive guidance regarding the simulation through TTS. This guidance describes the features included in the simulation and how to use them. Finally, the expert user explains the tools on the workbench to the beginner users. In addition, beginners can learn how to use these tools directly in a virtual environment. During these demo tests, it was confirmed that expert and beginner users can interact normally.  
We then discussed potential improvements with the users. It was pointed out that the TTS sounds overlap with those of other users when entering the room for the first time. We also received a suggestion to implement interaction through button clicks, in addition to direct interaction with objects, in case the target object is far away or requires repeated motions.
# Active Tunable Elastic Metasurface for Abnormal Flexural Wave Transmission
###### Abstract  
An active elastic metasurface has more flexibility than a passively modulated elastic metasurface, owing to the manipulation of the phase gradient that can be realized without changing the geometrical configuration. In this study, a negative proportional feedback control system was employed to provide positive active control stiffness for adaptive unit cells, with the aim of achieving the active modulation of the phase gradient. The relationship between the control gain and the phase velocity of the flexural wave was derived, and the transfer coefficients and phase shifts of the flexural wave through the adaptive unit cells were resolved using the transfer matrix method. Finite element simulations for wave propagations in the adaptive unit cells were conducted, and they verified the analytic solutions. Based on this theoretical and numerical work, we designed active elastic metasurfaces with adaptive unit cells with sub-wavelength thicknesses according to the generalized Snell's law. These metasurfaces show flexibility in achieving abnormal functions for transmitted waves, including negative refraction and wave focusing, and transforming guided waves at different operating frequencies by manipulating the control gain. Therefore, the proposed active metasurface has great potential in the fields of the tunable manipulation of elastic waves and the design of smart devices.  
active elastic metasurface; flexural wave controlling; feedback control gain; generalized Snell's law; piezoelectric effects +
Footnote †: journal: Applied
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 4 The Performance Evaluation of MIMO Antenna Systems  
### The Realized Peak Gain and Efficiency  
Gain, as one of the critical parameters of the antenna, is used to measure the antenna's ability to receive and transmit signals. Figure 8 shows the gain and radiation efficiency of the proposed MIMO antenna. It is presented that the peak gain of the proposed antenna is around 3 dBi for the 2.5 GHz band, and it is around 3.8 dBi for the 4.7 GHz band, while the measured radiation efficiency is higher than 62% and 66% in the operating bands, respectively.