esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
studied the influence of different bearing types and bearing length/diameter ratio parameters on vibration characteristics and concluded that, for the rotor system with an oil wedge bearing, the stability of large/long ratio journal bearing was better than that of short length/diameter ratio bearing, and the instability speed increased with the increase of the bearing's length/diameter ratio [16]. [PERSON] et al. analyzed the influence of bearing position and aperture change on the static stiffness and natural frequency of a motorized spindle in a rotor bearing system. It was concluded that an increase in span and aperture could improve the static stiffness of the spindle, and the change in bearing position at the back end obviously affected the frequency and mode of the spindle [17]. [PERSON] et al. analyzed the influence of longitudinal stiffness change of the ship thrust bearing, stiffness change of the aft stern bearing support and position change on the natural frequency [18]. [PERSON] equated the bearing support to different support modes and studied the influence of the stern bearing under different support modes on the rotational vibration of shafting [19]. [PERSON] adopted the optimization design method to optimize the bearing stiffness and damping, which could improve the stability of the system [20]. By using the Taguchi method, [PERSON] studied the vibration of a rotor system under different coupling types, disc positions and rotating speeds and found out the parameter combination which could minimize vibration deformation [21]. In summation, the research on smart springs is mainly on the torsional vibration control of helicopter blades. The influence of support parameters on the vibration characteristics of shafting is mainly about the influence of bearing stiffness and support position parameters on the vibration characteristics of the system, but research on the influence of support damping and support mass parameters on the vibration characteristics of shafting is less frequent. At present, research on reducing the vibration of the tail shaft with smart springs lacks theoretical orientation and verification. It is necessary to further study the application of smart springs in the vibration damping system of the tail drive shaft. Based on the finite element method, the finite element model of shafting is established. The influence of the smart spring support parameters on the dynamic response of the system is studied, which provides a reference for the structural design and vibration control of the smart spring support.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Performance Optimization Model Based on Game Theory
#### 4.2.1 Performance Optimization Theory  
Time is divided into a number of discrete time slots. For a given slot, the stations sense the channel before sending data packets by probability \(p\). If the channel is busy, the stations defer data packet transmission until the next time slot. If a channel is sensed to be idle, the stations send data packets to the other stations by \(\tau_{1}\). The receiver replies to the ACK via \(\tau_{2}\). If more than two stations send data packets at the same time, the collision causes a communication failure. The HMM is used to dynamically estimate the number of competitive stations and compute \(\tau_{1}\) and \(\tau_{2}\). This process is finitely repeated to obtain the optimal access probability for achieving optimum performance.
# Inland Water Level Monitoring from Satellite Observations:
## 2 Methodological Framework
### Bias Control  
Two different types of bias were controlled in the scoping review: publication bias and rater bias [51]. For the minimization of publication bias, five electronic literature databases were searched and three types (scientific papers, conferences, and review papers) of publications were included enabling the authors to obtain a more comprehensive and wide range of publications [52]. For the minimization of rater bias, four researchers from a variety of disciplines were involved in the scoping review providing a large perspective on the selection and the charting processes [37].
# Computer Modelling of Heliostat Fields by Ray-Tracing Techniques: Simulating the Sun
## 2 Sun Position  
To construct a comprehensive ray-tracing model that simulates a heliostat field collecting radiation from the Sun and transferring it to a target, the first step is to calculate the local Sun's position, as a function of time, date, latitude and longitude. This can be achieved by implementing the nine-step algorithm developed by the astronomy community [22; 23], shown in Figure 1. Essentially, this algorithm includes three layers of astronomical calculations, to successively transform current time (steps 1, 2, 3), Sun's anomaly (position) (steps 5, 6) and Sun's coordinates (steps 8, 9), linked by steps 4 and 7.  
Figure 1: The nine-step algorithm to calculate the Sun’s position, involving successively the calculation of different times, anomalies and coordinates.  
For simplicity, a geocentric model (Sun moving around Earth) is always used, instead of a heliocentric model (Earth moving around Sun). The variable names used in this article are exactly the same as in [PERSON]'s [22] and [PERSON] and [PERSON]'s [23] books.
# Geovisualization of Buildings: AI vs. Procedural Modeling
## 3 Comparative Analysis, Results, and Discussion
### What Is Needed for Detailed Descriptions of Buildings?  
To create the perfect building model, a comprehensive description is needed that includes the following details:  
* Building Style: information about the architectural style of the building, such as modern, contemporary, traditional, or a specific architectural movement (e.g., Art Deco, Bauhaus, Postmodern, Gothic, etc.).
* Exterior Features: description of exterior features such as facade design, materials (e.g., concrete, glass, steel, brick), color schemes, decorative elements, and any unique architectural elements (e.g., balconies, avnings, ornamentation).
* Building Layout and Overall Shape and Dimensions: details about the layout of the building, including the number of floors, floor plans, dimensions, proportions, and any specific structural elements (e.g., columns, beams, cantilevers), and also information about the overall shape of the building (e.g., rectangular, cylindrical, angular) and its dimensions, including length, width, and height.
* Windows and Doors: specifications for windows and doors, including their placement, size, style (e.g., sliding, casement, double-hung, bay), and materials (e.g., glass, wood, metal) (and specifically for doors, e.g., single, double, sliding).
* Roof Design: description of the roof design, including its shape (e.g., flat, pitched, gabled, hipped,...), materials (e.g., shingles, tiles, metal), and any additional features (e.g., skylights, rooftop gardens, dormers).
* Landscaping: information about landscing elements surrounding the building, such as gardens, pathways, trees, and outdoor amenities (e.g., seating areas, fountains).
* Description of Interior Layout and Features: (unnecessary if modeling only the outside of a model of a building) details about interior spaces, including room layouts, partitioning, ceiling heights, flooring materials, lighting fixtures, and any specific interior design elements (e.g., staircase design, built-in furniture).
* Contextual Information: contextual details such as the building's location, surroundings, site constraints, and any specific environmental considerations (e.g., climate, orientation, sustainability features).
* Facade Design: specific details about the facade design, including materials (e.g., concrete, glass, brick, metal), textures, patterns, and decorative elements (e.g., columns, arches, balconies).
* Finishing Details: specific finishing details such as paint colors, surface treatments, and decorative elements both inside and outside the building.  
By providing a thorough description encompassing these aspects, we can ensure that the resulting building model accurately reflects the desired design intent and meets the project requirements. Additionally, accompanying visual references such as architectural drawings, sketches, or reference images can further aid in the accurate interpretation and realization of the building model.
## 3 Three-Dimensional Laser Scanning Technology and Application Scheme
### Internal and External Process of 3D Laser Scanning  
#### 3.4.1 External Process  
The first is the selection of the station. Taking advantage of the long scanning distance of the SX10, the station is set up on a known point based on the existing control points in the tunnel. Since the data point density of the 3D laser scanner will decrease rapidly with the increase of the incident angle, the accuracy of the laser scanning result will be reduced. In order to ensure the accuracy of the data, the measurement point should be selected as a control point with a certain distance from the scanning position and stable geological conditions. After that, adjust the gear setting, and through the real-time image transmission on the controller screen, take a polygonal method to roughly scan the surrounding rock conditions in the mileage near the face. The hoop scanning density is 1 mrad, the longitudinal spacing is 50 mm, and the maximum scanning distance is set to 30 m. In such a scanning cycle, multiple repeated scans will be performed, and the time of each scan is 12 min, and the overlapping parts of the data segments can complement each other after each scan, thereby improving the accuracy of the point cloud data.
## 5 Secure End-to-End Encryption for Iot Communication Scheme  
The design of the secure MQTT protocol aims to provide information security guarantees in the form of data confidentiality and authentication. This design can be applied  
Figure 1: Research Flow.  
to hardware devices that do not support SSL/TLS so that they can communicate securely even through the internet. The general design of the protocol can be seen in Figure 2.  
Figure 2 provides an explanation of the working system of the Secure End-to-End Scheme on the MQTT Protocol. Here is the explanation:  
1. The dealer sends a \(seed_{G}\) via Galatuncci's secret sharing as a component in calculating the group key \(K_{G}\);
2. Each device that receives the seed\({}_{G}\) can compute the group key \(K_{G}\). Each device that is registered and has the same license will have the same group key \(K_{G}\);
3. Each device that has the same \(K_{G}\) group key can communicate with the session key \(K_{C}\) derived from the group key \(K_{G}\). The cryptographic algorithm used in communication is lightweight cryptography (LWC), which provides data confidentiality and authentication features.  
Secure end-to-end MQTT with lightweight cryptography designed based on Galatuncci's secret sharing scheme [19] and lightweight cryptography. The secure end-to-end MQTT protocol was designed with the aim of providing encryption for message payloads sent over the MQTT protocol. The secret sharing scheme used is intended to make it easier for dealers to generate group keys, device management, and key updates. Updates are made if there is an addition or deletion of devices.  
The secure end-to-end MQTT design is divided into three stages, i.e., registration and key generation, communication, and rekeying.
## 3 Statistical Analysis
### Daily Values
#### 3.2.3 Daily Radiation Indices  
The variation of the daily values of radiation indices throughout the year is shown in Figure 26. \(K_{D}\) shows lower values in the summer, while \(K_{T}\) shows higher values. The variability of each daily radiation index is shown in Figure 27. As indicated by the length of boxplots, the indices show high variability during the winter and spring seasons since they are affected by the cloud cover. The pattern of \(K_{B}\), \(K_{BT}\) and \(K_{T}\) is similar with highest values during the summer. In contrast, \(K_{D}\) has its highest values in the winter. Daily Clearness Index (\(K_{T}\)) and Relative Sunshine Duration (\(\sigma\))  
\(K_{T}\) is an objective measure of the influence of cloud cover on solar radiation flux. As indicated earlier, the sky conditions could be classified in four intervals of clearness index. The monthly statistics of \(K_{T}\) are reported in Table 9. Also listed in the table is the number of days according to the above classification for each month of the year. The annual average of \(K_{T}\) is 0.620 with a standard deviation of 0.121. The monthly average values of the daily clearness index range between 0.497 in November to 0.692 in July. The annual number of cloudy days is 17 (4.66%), the respective number of partially cloudy days is 137 (37.53%) and 211 days are classified as clear days representing the 57.81% of the annual number of days. As it can be seen the summer months are classified mainly as clear days. It has to be noted that the year of measurements is considered as a dry year. Figure 26: Daily values of radiation indices: (**a**) diffuse fraction (\(K_{D}\)); (**b**) direct horizontal fraction (\(K_{B}\)); (**c**) direct clearness index (\(K_{BT}\)); and (**d**) clearness index (\(K_{T}\)), throughout the year. The annual average on clear days of the direct normal beam radiation is 27.3 MJ/m\({}^{2}\), while the respective value of direct horizontal is 17.7 MJ/m\({}^{2}\). The annual average of diffuse radiation is 4.2 MJ/m\({}^{2}\), while the global radiation is 23.9 MJ/m\({}^{2}\). Furthermore, the annual mean of daily sunshine duration on clear days is 11.5 h. The maximum daily sunshine duration is 14.0 h and it was recorded in May. The major difference between day types is the significant reduction in the beam radiation which also affects the global irradiation level. For example, the annual average daily direct normal radiation available for energy conversion on partially cloudy days is reduced by a factor of 0.61 relative to that available on clear days. The respective reduction of global radiation is 0.65. The descriptive statistics of the sunshine duration as well as the relative sunshine duration are presented in Table 10 for each month of the year. The annual daily average of  
Figure 27: Monthly boxplots for each daily radiation index: (**a**) diffuse fraction (\(K_{D}\)); (**b**) direct horizontal fraction (\(K_{\rm{B}}\)); (**c**) direct clearness index (\(K_{BT}\)); and (**d**) clearness index (\(K_{\rm{T}}\)).
## 3 Results
### Dj
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Day 1**} & \multicolumn{3}{c}{**Day 2**} & \multicolumn{3}{c}{**ICC 095\%CD**} & \multicolumn{3}{c}{**SEM**} & \multicolumn{3}{c}{**CV 095\%CD**} \\  & **Mean** & **SD** & **Mean** & **SD** & **Value** & **ICC** & **UL** & **LL** & **SEM** & **MD** & **MD** & **CV (\%)** & **LL** & **UL** \\ \hline \multicolumn{2}{c}{Jump outcomes} & & & & & & & & & & & & & & & \\ Jump Height (map-Mom) [cm] & 26.8 & 4.9 & 27.1 & 3.4 & 0.332 & 0.81 & 0.55 & 0.93 & 1.9 & 5.2 & 4.4 & 6.1 & 1.7 & 10.4 \\ Jump Height (flight) [cm] & 26.8 & 4.9 & 27.1 & 3.4 & 0.332 & 0.81 & 0.53 & 0.93 & 1.9 & 5.3 & 4.4 & 6.2 & 1.8 & 10.6 \\ RSImod(m/s) & 1.08 & 0.20 & 0.97 & 0.15 & 0.003 & 0.73 & 0.38 & 0.90 & 0.12 & 0.33 & 0.28 & 10.6 & 3.0 & 18.3 \\ Kinetics & 1694 & 183.8 & 1601 & 220.4 & 0.010 & 0.78 & 0.47 & 0.92 & 117.8 & 326.5 & 274.0 & 5.8 & 1.7 & 10.0 \\ Concentrative Imple [N s] & 146.4 & 16.4 & 148.8 & 16.5 & 0.107 & 0.91 & 0.76 & 0.97 & 5.4 & 15.0 & 12.6 & 3.0 & 0.9 & 5.2 \\ Eccentric Mean Fore [N] & 2087 & 324.1 & 1912 & 339.5 & 0.001 & 0.89 & 0.70 & 0.96 & 16.9 & 470.2 & 394.6 & 6.8 & 1.9 & 11.6 \\ Eccentric Impulse [N s] & 161.4 & 22.5 & 158.9 & 21.4 & 0.014 & 0.99 & 0.96 & 0.99 & 3.3 & 9.2 & 7.7 & 1.7 & 0.5 & 2.9 \\ Kinematics & & & & & & & & & & & & & & \\ Concentric Mean Power [W] & 6482 & 684.2 & 6063 & 758.3 & 0.004 & 0.74 & 0.40 & 0.90 & 478.8 & 1327 & 1113 & 6.6 & 1.9 & 11.3 \\ Competitive Mean Power/ BM & 101.4 & 10.7 & 93.8 & 8.1 & 0.001 & 0.70 & 0.33 & 0.88 & 7.5 & 20.8 & 17.4 & 7.1 & 2.0 & 12.1 \\ Peak Power [W] & 9019 & 109 & 8210 & 1201 & 0.01 & 0.70 & 0.34 & 0.89 & 842.9 & 2336 & 1960 & 8.0 & 2.3 & 138.8 \\ Peak Power/ BM [W/Ag] & 140.9 & 15.2 & 127.1 & 14.2 & 0.001 & 0.65 & 0.24 & 0.86 & 13.2 & 36.5 & 30.6 & 8.4 & 2.4 & 14.4 \\ Concentric Peak Velocity [m/s] & 2.42 & 0.18 & 2.44 & 0.13 & 0.211 & 0.81 & 0.54 & 0.93 & 0.07 & 0.20 & 0.17 & 2.5 & 0.7 & 4.3 \\ Jump strategy & & & & & & & & & & & & & & \\ Conduct Time [s] & 0.3 & 0.3 & 0.3 & 0.0 & 0.002 & 0.49 & 0.02 & 0.79 & 0.0 & 0.1 & 0.1 & 9.1 & 2.6 & 15.7 \\ Countermovement Depth [cm] & 17.8 & 2.0 & 19.7 & 3.1 & 0.004 & 0.56 & 0.11 & 0.82 & 2.2 & 6.1 & 5.2 & 8.1 & 2.3 & 13.9 \\ \hline \multicolumn{2}{c}{SD = standard deviation; ICC = intra-class correlation coefficient; CI = confidence interval; LL = lower limit; UL = upper limit; SEM = standard error;} \\ \multicolumn{2}{c}{of measurement; MD = minimum difference; and CV = coefficient of variation.} & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 5: Within week reliability of DJ F-T derived variables.
## 2 LiCSAR System Architecture
### LiCSAR FrameBatch Processing  
The LiCSAR FrameBatch package is a set of data structures and algorithms designed to automate frame processing using LiCSAR. It is optimised to run on a computing cluster facility. Our frame processing strategy consists of four parts described below and depicted in Figure 5. The only required user parameters for starting batch processing for a frame are the frame identifier, a toggle for \"auto-download\" functionality, and, optionally, a start/end date.
## 3 Proposed Method
### Hand Gesture Spotting
#### 3.3.2 Hand Gesture Spotting  
In real-time hand gesture recognition, it is still challenging to determine when a gesture begins and when it ends from a hand trajectory. This session presents a new solution for hand gesture spotting to mark the start and end points of a gesture, as shown in Figure 6.  
Figure 5: Results of fingertip detection: (**a**) depth images, (**b**) fingertip detection.  
Figure 6: Proposed scheme for gesture spotting.  
In this step, the depth resolution is obtained from the Kinect V2 sensor as 512 x 424 pixels, and we define six hand gestures of swiping left (SL), swiping right (SR), swiping down (SD), swiping up (SU), zoom in (ZI), and zoom out (ZO) as six different gesture trajectories. A remaining gesture is meaningless (M), which is defined as a free trajectory.  
During tracking, the hand trajectory is recognized in every frame using the methods mentioned in Section 3.4. Figure 6 shows the proposed hand gesture spotting scheme of our system.
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Hardware Design of the System
#### 3.5.4 Energy Management  
The power supply solution is a design that makes the system power self-sufficient, and the method of energy management is different for router and leaf nodes. The leaf node operates with battery only and is programmed to make strict use of energy. On the other hand, the router node uses an energy controller, solar panels, and lithium batteries. In this case, the solar controller derives energy to charge the lithium batteries while powering the overall system. The chosen component for this task is the BQ25570 device that extracts power in the range of microwatts (uW) to milliwatts (mW). It is used with high output impedance DC sources, such as photovoltaic (solar) or thermal electric generators (TEG) that enable the circuit to better handle the overvoltage. In addition, the battery management features ensure that a rechargeable battery is not overcharged or depleted beyond the safe limits of the battery and system operation.
## 4 CFD Modelling
### Numerical Setup, Initial and Boundary Conditions  
For the solution the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) solver of ANSYS Fluent was used to solve the three-dimensional, incompressible, steady state Reynolds Average Navier-Stokes (RANS) using second order numerical schemes and the QUICK (Quadratic Upstream Interpolation for Convective Kinematics) higher-order differencing scheme that considers a three-point upstream weighted quadratic interpolation for the cell face values. As described above, the computational domain was structured with hexahedral elements that were aligned with the flow and refined at the area of interest to improve the accuracy [15]. Boundary conditions are given at Table 1.  
Figure 4: (**a**) Map of the project. (**b**) Positioning of the wind turbines in Google Earth. (**c**) Extraction to Cartesian coordinates. (**d**) Positioning of the wind turbines in the computational domain and detail of the grid refinement close to the area of interest.  
Two simulations with different turbulence intensities were conducted, \(TI=8\%\) and \(TI=15\%\), as described in Section 1. The initial values and the boundary conditions for the turbulence quantities have been calculated separately for each case according to the \(TI\) and were introduced with user defined functions using the set of equations presented in Section 4.2 and 4.3. Atmospheric air was chosen as fluid, with constant density \(\rho=1.225\,\mathrm{kg/m^{3}}\) and dynamic viscosity \(\mu=1.225\,\mathrm{kg/m^{3}}\). A positive value of \(365\,\mathrm{m}\) has been used for the Monin-Obukhov length (\(L\)) to model a stable atmospheric stratification. A reference speed of \(U_{ref}=8\,\mathrm{m/s}\) at HUB height \(H_{ref}=80\,\mathrm{m}\) has been used in both cases.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion
### Non-Linear GWS Behavior Linked to Extreme Rainfall  
We identified episodic GWS increases during El Nino events, interrupting otherwise declining decadal trends. This non-linearity echoes the observations by [36], who showed that episodic extreme precipitation produced recharge pulses in 32 of 37 large aquifers globally (R = 0.62-0.86 correlation with in situ data), emphasizing the role of intense rainfall events. Likewise, ref. [52] contrasted GRACE outcomes with global hydrological models and found significant underestimation of pluri-annual TWS variability linked to extremes.
## Results and Discussion
### Effect of the Supporting Electrolyte
In the case of the acetate buffer as the supporting electrolyte, the formation of Pb(CH\({}_{3}\)COO)\({}_{2}\) can decrease the availability of Pb(II) in solution; in addition, the acetate ions can compete with Pb(II) ions for the active sites available on the graphite-cork surface. As a result, a poor current response is achieved, with limitations on the selectivity and sensitivity of the modified electrode. Indeed, lead concentrations below 10 uM (Figure 1a), which affect the LOD, cannot be efficiently detected. In the case of NaNO\({}_{3}\), the lack of complexing activity by nitrate anions and the possibility of preferential interactions between the composite material (cork-graphite) and Pb(II) ions in solution allow significant improvements in the current response, with consequent benefits in terms of the linearity of the response, although superficial adsorption phenomena may be highlighted for lead concentrations below 10 uM (Figure 1b). Finally, well-defined voltammetric signals were observed at the GrRAC-70% electrode when H\({}_{2}\)SO\({}_{4}\) was used (Figure 1c). Due to the acidic conditions, lead is present in solution in its cationic form, Pb\({}^{2+}\), and the cork surface is also completely protonated and positively charged. When the working electrode is negatively polarized, the lead ions compete with protons for surface sites; however, the surface accumulation of Pb ions is favored due to ion exchange mechanisms with active sites. In fact, the current response increased linearly without any significant deviations. Therefore, H\({}_{2}\)SO\({}_{4}\) was selected as the supporting electrolyte for the subsequent experiments.