esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 2 Preparatory Knowledge
### Feature Parameter Selection Based on the Improved Relief Algorithm
#### 2.3.2 Feature Interaction  
Interaction Information is a concept used to measure the degree of interaction between random variables. It is based on the principles of information theory and is used to describe the amount of information contained when variables occur together, as well as the gain in information relative to when they occur separately. Therefore, Interaction Information is also known as Interaction Gain (IG). In this article, IG refers to the measure used in feature selection to assess the extent to which interactions among multiple features improve the predictive performance of the target variable. The definition of three-way Interaction Gain is given as follows:  
\[IG(f_{i};f_{j};C)=I(f_{i};f_{j};C)-I(f_{i};C)-I(f_{j};C) \tag{2}\]  
In the formula, \(I(f_{i};f_{j};C)\) represents the interaction information between feature variables \(f_{i}\) and \(f_{j}\) within category \(C\). When \(IG(f_{i};f_{j};C)>0\), it indicates that the information provided by the joint occurrence of feature variables \(f_{i}\) and \(f_{j}\) is greater than the sum of the information provided by the two individual feature variables. This suggests the presence of interaction between the two variables. Otherwise, it indicates that the information provided by the joint occurrence of the two variables is independent of category \(C\), or there is redundant information.
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
## 3 The Virtual Musculoskeletal Methodology
### Modeling the Muscular Skeletal System
#### 3.1.1 The Model of the Muscle  
The three-element Hill muscle model is a representation of the muscle mechanical response. The model is constituted by a contractile element (CE) and two nonlinear spring elements: one in series (SE) and another in parallel (PE). Due to the series connection of the tendon and the muscle belly, by ignoring the angle between them, the force of the muscle is equal to the force produced by the parallel units consisting of CE and PE.  
The force-length relation of PE is  
Figure 2: Biology structure of musculoskeletal system.  
\[\frac{F^{\text{PE}}}{F_{\text{max}}}=K^{\text{PE}}=\left\{\begin{array}{ll}0& \text{if }L^{\text{PE}}<L^{\text{PE}}_{\text{slack}}\\ k^{\text{PE}}\Big{[}(\frac{L^{\text{PE}}}{L^{\text{PE}}_{\text{slack}}})^{2}-1 \Big{]}&\text{if }L^{\text{PE}}\geq L^{\text{PE}}_{\text{slack}}\end{array}\right., \tag{12}\]  
where \(F^{\text{PE}}\) is the force generated by PE, \(L^{\text{PE}}\) is the current length of PE while \(L^{\text{PE}}_{\text{slack}}\) is the length when muscle is slack, \(k^{\text{PE}}\) is a coefficient relating to the property of muscle fibers, and \(K^{\text{PE}}\) is the coefficient to describe the rate to the maximum force.  
The force-length equation and the force-speed equation illustrate the feature of CE. In Equations (13) and (14), the force is a quadratic function to the length and a hyperbolic function to the speed.  
\[\frac{F^{CE}_{L}}{F_{\text{max}}}=K^{CE}_{L}=1-\left(\frac{L^{CE}-L^{CE}_{0}}{ w}\right)^{2} \tag{13}\]  
\[\frac{F^{CE}_{v}}{F_{\text{max}}}=K^{CE}_{v}=\frac{F_{\text{max}}v_{\text{max }}\beta-\alpha v}{F_{\text{max}}(\beta v_{\text{max}}+v)} \tag{14}\]  
where \(L^{\text{CE}}\) is the muscle length, \(L^{\text{CE}}_{0}\) is the length when the muscle achieves maximum isometric contraction force \(F_{\text{max}}\), \(w\) is used to control the width of the curve, \(K^{\text{CE}}_{L}\) is the force-length coefficient of contraction element, \(v_{\text{max}}\) is the maximum speed of muscle contraction, \(\alpha\) and \(\beta\) are coefficients relating to characteristics of muscle fibers, and \(K^{\text{CE}}_{v}\) is the force-speed coefficient.
## 1 Introduction  
Volatile aroma compounds are a vital component of wine quality, with more than eight hundred being identified in wine, including monoterrenes, aldehydes, ketones, esters, organic acids, and alcohols [1, 2]. These compounds, which are derived from grapes, fermentation, or post-fermentation treatments such as oak storage and bottle aging, are found in must and wines mostly in the form of glycosidic conjugates [3]. Glycosidase enzymes are effective tools for the release of aroma compounds due to their ability to break the bond-linkage between a sugar moiety and an aglycone [4]. A large portion of glycosides remains in young wines as a result of the limited impact of glycosidases from grape and _Saccharomyces cerevisiae_ in winemaking [5]. Therefore, the use of \(\beta\)-glucosidasees would be of interest to wine producers and wineries seeking to improve wine aroma quality since they can promote the liberation of aroma compounds from monoterrene glycoside precursors present in young wines [6, 7].  
Most enzymes used in winemaking are derived from fungi, such as _Aspergillus niger_, and a non-specific mixture of enzymes that contain multiple enzymatic activities [8], which are commonly used by some wineries to enhance the release of aroma compounds [9]. However, such enzymes may produce unpredictable adverse reactions [8]. Some studies have shown that the continuous use of these commercial enzymes can significantly reduce the aroma complexity and alcoholic strength of wines [10; 11]. Alternatively, other reports have highlighted the \(\beta\)-glucosidase producing potential of non-_Saccharomyces_ yeasts [12]. Several literature reports have shown that some \(\beta\)-glucosidaseases from non-_Saccharomyces_ yeasts could function at low pH, low temperature, and high ethanol concentrations [13]. However, the activity of \(\beta\)-glucosidase in grape must and wine has not been extensively explored; hence, their impacts on wine flavor are still unclear [9; 14].  
Chardonnay is a white grape variety known as a neutral aromatic variety that produces wines without a peculiar set of aroma compounds [15]. It easily adjusts to many different terroirs in almost every wine-growing region [16]. Furthermore, the Chardonnay variety has gained popularity in wine-making regions because of its aging ability and its ease in reflecting the area where it is grown [17]. To the best of our knowledge, there are few reports on the use of purified \(\beta\)-glucosidase from natural yeasts to enhance the aroma of Chardonnay wines [18]. Therefore, in this study, we purified two \(\beta\)-glucosidaseases (Mg-\(\beta\)gl and Hu-\(\beta\)gl) isolated from the native non-_Saccharomyces_ yeasts _Meyerczyma guilliermondii_ NM218 and _Hanseniaspora uvarum_ BF345 (chosen for their high \(\beta\)-glucosidase activity). The biochemical properties of both enzymes were studied and applied to the aging of Chardonnay young wines to determine whether they could be used to improve the aroma of the wines.
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes
### Analysis of Merkle Tree-Based Authentication  
Prior to authentication, the prover and verifier need to agree on the hash function to be used in Merkle tree construction, the size of the data blocks, and the rules for identifying specific data. In the authentication process, the prover \(\mathcal{P}\) first sends an identifier of the data to the verifier \(\mathcal{V}\) and proves complete possession of the data in question.
## 2 Materials and Methods
### Data Analysis
#### 2.4.1 Statistical Data Analysis Method  
Due to the non-constant nature of the background \(BG2(t_{BG2})\
eq BG2_{sys}\big{(}t_{sys}\big{)}\) in PV in situ measurements, the developed data analysis method incorporates multiple backgrounds \(BG2_{i}\). Herein, it is assumed that the statistical properties of the multiple backgrounds \(BG2_{i}\) do not change. Therefore, the method is further referenced as the statistical data analysis method.  
The statistical data analysis method works as follows. With the help of an algorithm, all local maxima of more than 10 dB above the noise floor are marked in the system measurements \(M_{sys}\), further called \"peaks\". This is carried out to address the \(\pm 3\) dB accuracy level of the measurements system.  
The statistical data analysis method compares the peaks \(M_{sys}\) with all measured backgrounds \(BG2_{i}\), according to Equation (2).  
\[P_{sys}=\min_{i=1\to n}M_{sys}-BG2_{i} \tag{2}\]  
All measured backgrounds \(i=1\to n\) are used to create a margin between peaks \(M_{sys}\) and background measurements \(BG2_{i}\). The lowest margin \(P_{sys}\) is used for the comparison of the peaks.  
Each peak is categorized in the EMC limit category and peak-to-background margin \(P_{sys}\) category. The EMC limit category shows if a peak is above or below the EMC limit line, while the peak-to-background margin \(P_{sys}\) category gives the magnitude of each peak in 5 steps, 0-5 dB, 6-10 dB, 11-15 dB, 16-20 dB, and \(>\)20 dB. As an example, the margin \(P_{sys}\) category >20 dB, means that the peak is more than 20 dB higher than the highest background. Figure 3 shows an example of a system measurement graph that illustrates: a system measurement (pink), the margin \(P_{sys}\) category (dot: yellow; orange; green; blue; red), and the EMC limit line (purple). Samples of 3 representative backgrounds are shown in diverse colors (see legend: 2202, 2214, and 2217).  
To enable fast comparison, all system measurement graphs from the same system configuration are assembled into one system summary graph, meaning that all margin \(P_{sys}\) categories of positions B and E and in directions X, Y, and Z are summarized in this graph.  
When such a graph has many peaks and high amplitudes close to or above the EMC limit, then the system is more likely to cause interference with radiocommunication than a system summary graph with fewer peaks and lower amplitudes.  
From the system summary graph, the frequency histogram is generated to see if any peaks are repeated in a fixed frequency pattern and which frequency has the most accumulations.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
###### Abstract  
The significant growth of wireless communications systems in the last years has led to the adoption of a wide range of applications not only for the general public but, also, including utilities and administrative authorities. In this context, the notable expansion of new services for smart cities requires, in some specific cases, the construction of underground tunnels in order to enable the maintenance and operation works of utilities, as well as to reduce the visual impact within the city center. One of the main challenges is that, inherently, underground service tunnels lack coverage from exterior wireless communication systems, which can be potentially dangerous for maintenance personnel working within the tunnels. Accordingly, wireless coverage should be deployed within the underground installation in order to guarantee real-time connectivity for safety maintenance, remote surveillance or monitoring operations. In this work, wireless channel characterization for complex urban tunnel environments was analyzed based on the assessment of LoRaWAN and ZigBee technologies operating at 868 MHz. For that purpose, a real urban utility tunnel was modeled and simulated by means of an in-house three-dimensional ray-launching (3D-RL) code. The utility tunnel scenario is a complex and singular environment in terms of radio wave propagation due to the limited dimensions and metallic elements within it, such as service trays, user pathways or handrails, which were considered in the simulations. The simulated 3D-RL algorithm was calibrated and verified with experimental measurements, after which, the simulation and measurement results showed good agreement. Besides, a complete wireless sensor network (WSN) deployment within the tunnels was presented, providing remote cloud data access applications and services, allowing infrastructure security and safety work conditions. The obtained results provided an adequate radio planning approach for the deployment of wireless systems in complex urban utility scenarios, with optimal coverage and enhanced quality of service.  
wireless sensor networks; LoRaWAN; ZigBee; 3D Ray-Launching; tunnel environment; smart cities 20
# Constructing a Microstrip Phase Shifter with Low Phase Error Using a Y-Resonator
## 2 Proposed Structure  
The topology of the proposed Y-type dual-mode microstrip phase shifter is shown in Figure 1. The Y-type dual-mode resonator is used in this novel structure. This dual-mode resonator consists of three microstrip lines connected at 120\({}^{\circ}\) intervals, with the left branch equal to the right branch. There is cross-coupling between port 1 and port 2, and there are two paths for signal transmission from port 1 to port 2: one is that the signal from port 1 is coupled to the Y-shaped resonator mainly through the parallel coupled line structure; the other is that a weak-end coupled direct signal path is formed through the input/output feeder roots. This coupling structure allows the filter to produce two finite transmission zeros in the upper and lower stop bands, resulting in a quasi-elliptic function response. The quasi-elliptical response combines the high selectivity of elliptical filters with the equal-ripple characteristics of the passband. However, unlike an ideal elliptical filter, the quasi-elliptical response can exhibit unequal amplitude ripple outside the band. This design enhances out-of-band rejection by introducing transmission zeros at the edges of the passband while also preserving better signal integrity within the passband. This response improves passband and out-of-band performance. The reference line is usually a uniform transmission line, and the phase shift of the phase shifter is controlled by adjusting the electrical length of the reference line.  
Figure 2 shows a schematic diagram of the Y-type microstrip dual-mode resonator with an odd-even mode resonance equivalent circuit. From Figure 2a, it can be seen that the resonator is symmetric about ee, the odd-mode excitation in the symmetry plane will be shorted, and the odd-mode resonance equivalent circuit is shown in Figure 2c. The characteristic impedance can be defined as follows:  
\[Z_{ino}=j\frac{Z_{1}}{cot\theta_{1}}. \tag{1}\]  
Here, \(\theta_{1}=\beta*c\) is the electrical length of the Y-resonator branch, and the odd-mode resonance frequency \(f_{0}\) can be obtained from the resonance condition that the odd-mode resonance derivative is zero:  
\[f_{0}=\frac{c}{4L_{3}\sqrt{\varepsilon}}. \tag{2}\]  
For even-mode excitation, no current flows in the symmetry plane, and the even-mode resonant equivalent circuit is shown in Figure 2b. Using the transmission line theory and neglecting the effect of junction discontinuity, the characteristic impedance can be defined as follows:  
\[Z_{ine}=j\frac{Z_{1}^{2}tan\theta_{1}tan\theta_{2}-Z_{1}Z_{2}}{2Z_{2}tan\theta _{1}+Z_{1}tan\theta_{2}}. \tag{3}\]Here, \(\theta_{2}=\beta*(c+d)\) is the electrical length of the main branch of the Y-type resonator, and the even-mode resonant frequency \(f_{e}\) exists from the resonance condition where the even-mode resonant derivative is zero:  
\[f_{e}=\frac{c}{4L_{2}\sqrt{\varepsilon_{e}}}. \tag{4}\]  
According to Equations (2) and (4), the appropriate branch lengths can be selected to obtain two appropriate resonant frequencies.
## 3 Model for Tracking User Interactions in AR-Based Serious Games
### Conceptual Model
#### 3.1.1 User  
To enable objective mapping of user characteristics and interaction parameters to adapt the solution to the individual in the context of accessibility, one must develop a user model that represents preferences, capacities, and affective processes and their relationship with the task at hand [59]. In other words, depending on the context in which the solution is to be adapted to the user, different characteristics of the user are considered. In the context of accessibility, it is extremely important to consider the disabilities that the user has for defining the user interaction tracking model.  
Having said that, the features that one user profile could contain to help determine the direction of the adaptation of the interaction mechanisms and AR content are, for example, age, gender, type of disabilities, previous experience with technology, and user preferences. Such a user profile can be created during the registration process and can be dynamically upgraded depending on the user interaction data. If subjective evaluation is integrated into  
Figure 1: A conceptual model for tracking user interactions based on features related to the user, serious game, augmented reality, context and accessibility.  
a game, tracking subjective metrics could help identify user preferences related to different game elements.  
The following are examples of how a particular difficulty or impairment a user has can affect the adaptation of a solution to improve accessibility:  
* physical disabilities--understanding the user's physical disabilities can be crucial for adapting the interaction mechanisms and interface of AR-based solutions. This includes assessment of fine motor skills, hand mobility, coordination and the ability to follow movements. This information can help adapt ways of interaction by suggesting input methods that best suit a user's need, such as gestures or voice commands;
* visual impairments--users may have different visual impairments. Therefore, factors such as visual acuity, colours visible to the user, sensitivity to light or contrast, and other visual impairments, should be considered. This information can help to adapt the AR virtual objects, but also UI elements to make them more visible to the user;
* auditory impairments--there are users with varying degrees of auditory impairment. Adapting to such users may therefore involve providing alternative means of communication such as text displays or vibrations, or adapting the sound and sound elements to the user's needs;
* cognitive, learning and neurological disabilities--each user also has different cognitive abilities and understanding. To improve accessibility, comprehension, concentration, memory and other cognitive factors must be taken into account. The user interface and interaction should be simple, intuitive and customised so that users can easily use the solution.
# Settlement Foundations by Exploring the Collapse of Unsaturated Soils
## 5 Conclusions  
This study shows that the finite element method, in conjunction with an appropriate constitutive model, can be applied to simulate the mechanical and hydraulic properties of unsaturated soil and foundation stability under transient flow conditions. The EBMM constitutive model can successfully represent the mechanical behavior of unsaturated soils, especially in predicting geotechnical issues such as the collapse of the soil. The evolution of settlements was explored for a footing under infiltration during a 60-day rainfall event. The finding shows that 60 days of infiltration caused a significant increase in the pressure head, resulting in a reduction in soil suction above the water table. This reduction in suction results in additional settlement because of the wetting-induced collapse of the soil. The maximum vertical displacement was not observed at the center of footing but shifted towards the corners, where it was exposed to more infiltration and soil suction reduction. The study points out that the rate of collapse potential decreases over time, and negligible settlement is observed as the soil approaches full saturation.  
Furthermore, there was good concordance between the predictions made by the analytical models and the numerical simulation results, which validates the reliability of the numerical method adopted in this study. Overall, this study offers invaluable insights into how unsaturated soils behave under infiltration, highlighting the significance of considering the infiltration influences on soil stability and foundation design, particularly in regions prone to heavy rainfall, where a decrease in suction can lead to significant reductions in soil strength and increased settlement.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: Volumetric plastic strain (\(-\)): (**a**) before infiltration and (**b**) after filtration.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Precipitation Products' Inter-Comparison over East and Southern Africa 1983-2017
## 2 Materials and Methods  
### Precipitation Products: TAMSAT3, CHIRPS, and MSWEP  
Three state of the art daily precipitation satellite datasets were used for this study, TAMSAT3, CHIRPS, and MSWEP, remapped on a common 0.25\({}^{\circ}\) grid using a local area averaging to interpolate data from their higher native resolution to the common one (function \"area_hi2 lores\" from the NCAR-Command Language--NCL software package [22]). The function accounts for fractional contributions of the input high-resolution grid points to the scope of each low-resolution grid point. This is not a strictly conservative remapping, which represents the most suitable interpolation method used for precipitation fields [23].  
Nevertheless, it represents a good compromise between a robust interpolation appropriate for most purposes and a quick approach suitable to be applied to large data sets.  
The selection of the datasets was driven by the availability of more than 30 years of data, necessary to give more significance to the regional precipitation climatology of the two regions and to the analysis of inter-product stability. Moreover, the previous use of these precipitation products in African precipitation analysis and monitoring and their operational applications in agriculture and famine early warning systems is another key factor, which calls for the necessity to intercompare them and increase the level of confidence of their users. This is particularly true for TAMSAT3, a product developed mainly for monitoring droughts in Africa [24] and enhancing resilience to agricultural drought [25], and for CHIRPS, with its inclusion in the Famine Early Warning System Network initiative (FEWS NET--[[https://fews.net](https://fews.net)]([https://fews.net](https://fews.net)), accessed on 21 October 2021) [26]. MSWEP, which uses a completely different approach taking advantage of the complementary strengths of gauge-, satellite- (both TIR and microwave), and reanalysis-based data, was also considered in order to evaluate the behavior of the two TIR-based products against a multi-data source product.