esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency-Domain Lifting-Line Aerodynamic Modelling for Wing Aeroelasticity
###### Abstract  
A frequency-domain lifting-line solution algorithm for the prediction of the unsteady aerodynamics of wings is presented. The Biot-Savart law is applied to determine the normalwash generated by the wake vorticity distribution, whereas steady and unsteady airfoil theories ([PERSON]'s and [PERSON]'s, respectively) are used to evaluate the sectional aerodynamic loads, namely the lift and pitching moment. The wake vorticity released at the trailing edge derives from the bound circulation through the Kutta condition and is convected downstream with the velocity of the undisturbed flow. The local bound circulation is obtained by the application of the Kutta-Joukowski theorem, extended to unsteady flows. Assuming a bending and torsion wing, this paper provides the aerodynamic matrix of the transfer functions, relating the generalised aerodynamic loads to the Lagrangian coordinates of the elastic deformation. Its rational approximation yields a reduced-order state-space aerodynamic model suitable for an aeroelastic stability analysis and control purposes. The numerical investigation examines the influence of both the wake shed/trailed vorticity modelling and different approximations of the Kutta-Joukowski theorem for unsteady flows on the aerodynamic transfer functions given by the developed frequency-domain lifting-line solver. The accuracy of the solver is assessed by comparison with the predictions obtained by a three-dimensional boundary-element-method solver for potential flows. It is shown that, at least for the frequency range considered, regardless of the approximation of the Kutta-Joukowski theorem applied, the formulation based on the [PERSON] theory provides predictions that are in very good agreement with the results from the boundary element method for a slender wing. This agreement worsens as the wing aspect ratio decreases. A lower level of accuracy is obtained by the application of the sectional loads given by the Glauert theory. In this case, the predictions are more sensitive to the approximation used to express the Kutta-Joukowski theorem for unsteady flows.  
aeroelasticity; lifting-line theory; reduced-order modelling; unsteady aerodynamics; wake vorticity modelling +
Footnote †: journal: Applied Physics Letters
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 3 Architectures and Protocols for IoT Communications via Satellites
### Lpwan
#### 3.2.2 LoRa/LoRaWAN
Together, these components create a comprehensive architecture that extends the connectivity of the LoRaWAN. This ensures that the relayed IoT data reach end-users' business applications effectively. The implementation of this architecture can differ in various satellite constellations under development. Currently, four different models are being implemented with LoRa:  
* DtS-IoT model with satellites in LEOs: IoT nodes connect directly to satellites in LEO orbits, as seen in the Lacuna or Astrocast constellations [60]. * ItS-IoT model with satellites in LEOs: IoT nodes connect to a terrestrial gateway and retransmit data through a satellite constellation in an LEO. FOSSA Systems and Swarm use this model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Criterion** & **LoRa/LoRaWAN** & **NB-IoT** \\ \hline \multirow{4}{*}{Implementation models} & Private or public satellite networks, dependent on LoRa network coverage and local or regional regulations. & Implementation of satellite networks via cellular network operators, private or public networks, but uses licensed bands. \\ \hline \multirow{4}{*}{Appropriate use cases} & Suitable for NewSpace applications where low power consumption and low cost are crucial, such as remote monitoring and sensors on small satellites or isolated ground stations. & Preferable for applications that require greater bandwidth and reliability, such as large-scale data transmission or IoT devices in areas with good cellular coverage. \\ \hline \multirow{4}{*}{Innovations and trends} & Continuous developments in energy efficiency and range. & Advances in integration with 5G; improvements in speed and capacity. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 5: LoRaWAN architecture for satellite communications in an LEO. * DTS-IoT model with satellites in GEOs: IoT nodes connect directly to satellites in GEOs in this model. Echostar uses it along with S-band LR-FHSS to communicate with GEO satellites [60; 61]. * ItS-IoT model with satellites in GEOs: IoT nodes connect to a terrestrial gateway, retransmitting data through other satellite connectivity options. Inmarsat uses this model [5]. For some satellite IoT operators, such as FOSSA Systems, the hardware design of its IoT satellites for LoRa is founded upon a complete COTS architecture, utilizing a single AVR microcontroller and commercially sourced components. These satellites are configured in the PocketQube format, featuring dimensions of \(5\times 5\times 5\) cm and weighing less than 250 g. The design process prioritized the implementation of an agile and iterative methodology, mitigating potential points of failure and leveraging lessons learned from previous endeavors [25]. In addition, FOSSA has developed a new satellite generation, FOSSAat FEROX, aimed at providing secure, accessible, and reliable IoT communications anywhere from LEOs and carrying custom payloads for constellations [62].
## 18 Shortcomings of Non-Traveling Wave (NTW) Based Method  
Following are the shortcomings associated with NTW based fault location methods:  
Sampling frequency and interpolation of voltage and current values in non-traveling wave-based method play a significant role in creating an impact on results of fault estimation through non traveling wave based method. Accuracy of fault location is improved as sampling frequency increases. Further interpolation of original samples and values of the fault location function can improve the accuracy of finding fault location. Bias and difficulty in handling high sampling rate are the drawbacks linked to these ways of improvement of NTW results.  
NTW method requires little modification and sensitivity to attain accurate results of fault location. Practically, it is not possible that voltage and current values are measured at high synchronism. Therefore, aligning and matching are required. Thus, fault location function is modified as:  
\[f(x,\xi)=\sum_{l=t_{1}}^{t_{2}}\left|u_{lj}(x,t)-u_{ik}(l-x,t+\xi)\right|:\ x \in[0,1],\xi\in[0,T] \tag{142}\]  
where \(\xi\) is the time difference between non-synchronized data. \(T\) is the maximum time lag between data at two ends of TL. Minimum function is modified as:  
\[f\Big{(}x_{f},\xi\Big{)}=\min\{f(x,\xi):\ x\in[0,1];\xi\in[0,T]\} \tag{143}\]  
where \(f\Big{(}x_{f},\ \xi\Big{)}\) is a function of the fault distance and time difference between non--synchronized data. There is a possibility that aligning and matching may result in the change of characteristic curve of fault. Convergence issues are also raised with alignment.
# Fast Fractional-Order Terminal Sliding Mode Control for Seven-Axis Robot Manipulator
## 3 Controller Design
### Frotsmcspo
#### 3.2.2 Fast Fractional-Order Sliding Control/Reaching Law  
The control law of the new proposed controller is designed based on the fast-TSM-type [3]. It applies the fractional-order derivative to build the desired dynamics of the sliding surface as  
\[{}_{c}D_{0,t}^{\beta}\mathbf{s}=-\mathbf{k}_{1}\mathbf{s}-\mathbf{k}_{2}sig( \mathbf{s})^{\mathbf{r}_{2}}-D_{0,t}^{\beta+\alpha-2}(\mathbf{k}_{3}sgn( \mathbf{s})+\widetilde{\mathbf{\Psi}}) \tag{26}\]  
where \(\beta=[\beta_{1},\ldots,\beta_{n}]^{T}\in R^{n}\), \((\beta_{1}>0.5)\), \(\mathbf{k}_{1}=diag(k_{1j})\in R^{m\alpha n}\), \((k_{1j}>0)\), \(\mathbf{k}_{2}=diag(k_{2j})\in R^{n\alpha n}\), \((k_{2j}>0)\), \(sig(\mathbf{s})^{\mathbf{r}_{2}}\triangleq[sig(s_{1})^{\mathbf{r}_{21}}, \ldots,sig(s_{n})^{\mathbf{r}_{2n}}]^{T},0<r_{2_{1}},\ldots,r_{2_{n}}<1\) and \(\mathbf{k}_{3}=diag(k_{3j})\in R^{m\alpha n}\), \((k_{3j}\geq\Omega_{j})\). \(\Omega_{j}\) is assumed as the boundary of perturbation estimation error \(\widetilde{\mathbf{\Psi}}_{j}\) for the \(j\)-th joint. \(\widetilde{\mathbf{\Psi}}_{j}\) can be estimated by using (17) with the assumption of real perturbation boundary \(\Phi_{j}\). The order \((\beta_{j}\) and \(\alpha_{j})\) of the fractional derivative is always selected from 0.7 to 0.9. The influence of the selection of fractional-orders on the sliding surface design and the reaching law design is discussed in Appendix A.  
Compared to the control law of previous research such as FOTSMC [9], in designing the control law, the desired dynamics of the sliding surface is formed after a first-order derivative, i.e., \((\beta_{j}=1\) in (26)). In FFOTSMC, the fast control law \((\beta_{j}\in[0.5,1)\) in (26)) adopts the fractional-order derivative. The fast control law results in a faster convergence speed to the sliding surface, which is the main contribution of this research.  
Substituting \(\ddot{\mathbf{e}}\) by \(\ddot{\mathbf{x}}-\ddot{\mathbf{x}}_{d}\) and using the dynamics (14), the control input \(\ddot{\mathbf{u}}\) of FFOTSCM with SPO is given as (see Appendix B)  
\[\widetilde{\mathbf{u}}_{\text{FFOTSCP}}=\mathbf{\eta}^{-1}[D_{0,t}^{2-\beta- \alpha}(-\mathbf{k}_{1}\mathbf{s}-\mathbf{k}_{2}sig(\mathbf{s})^{\mathbf{r}_{ 2}})-D_{0,t}^{1}D_{0,t}^{-\alpha}D_{0,t}^{1}(\mathbf{\lambda}_{1}sig(\mathbf{ e})^{\mathbf{r}_{1}})-\overset{\wedge}{\mathbf{\Psi}}+\ddot{\mathbf{x}}_{d}- \mathbf{k}_{3}sgn(\mathbf{s})] \tag{27}\]  
**Remark 1**.: _The singularity of conventional TSMC is avoided by the fractional calculus, which is discussed in the numerical method (see Section 3.4) for implementing the fractional-order derivative._
## 3 Results and Discussion
### Molecular Flexibility Simulations
Lasofoxifene also forms a frequent hydrogen bond with GLU353 (58.14%), indicating a conserved interaction site within the binding pocket. Raloxifene shows multiple hydrogen bonds, particularly with GLU353 (67.58%) and GLY521 (48.05%), suggesting broader contact regions that may enhance anchoring within ER\(\alpha\). PBD-17 and PBD-20 exhibit lower hydrogen bond occupancy, although specific interactions such as ARG394-S20553 (20.68%) in the PBD-20 system provide additional binding support. The presence of consistent hydrogen bonding, especially with GLU353, is a hallmark of effective ER\(\alpha\) antagonists. Raloxifene's dual-site hydrogen bonding may compensate for its higher RMSF by stabilizing critical binding residues. The lower hydrogen bond frequency in PBD-17 implies weaker or less sustained interactions. In contrast, PBD-20 achieves modest but focused hydrogen bonding that complements its favorable energy profile. These patterns indicate that 4-OHT and raloxifene maintain the most stable hydrogen bond interactions with ER\(\alpha\) during the simulation. Figure 8: Rg and SASA values of the protein and ligands during 200 ns of MD simulation. The binding free energy of each ER\(\alpha\)-ligand complex was calculated using the MM-PBSA method and is summarized in Table 7. Among all complexes, lasofoxifene exhibits the most favorable total binding energy (\(-157.886\) kJ/mol), primarily driven by strong van der Waals interactions (\(-278.701\) kJ/mol). However, this is offset by its high polar solvation energy (\(+102.413\) kJ/mol), which reduces the net binding strength. The 4-OHT complex shows a balanced energy profile, with a total binding energy of \(-145.307\) kJ/mol supported by moderate van der Waals (\(-241.168\) kJ/mol) and electrostatic (\(-46.459\) kJ/mol) contributions. PBD-20 also demonstrates a favorable total binding energy (\(-139.462\) kJ/mol), indicating that its interaction with ER\(\alpha\) is energetically stable. Raloxifene's total binding energy (\(-121.156\) kJ/mol) was lower, but still significant, with decent van der Waals and electrostatic components. In contrast, PBD-17 has the weakest binding energy (\(-58.229\) kJ/mol), reflecting less favorable interaction and dynamic behavior during the simulation. The consistent SASA and non-polar contributions across all systems suggest similar solvent exposure profiles. Ligands with lower polar solvation penalties and stronger non-covalent interactions exhibit better binding affinity. These results confirm that lasofoxifene, 4-OHT, and PBD-20 possess the most energetically stable interactions with ER\(\alpha\) among the tested compounds.
## 3 Generation of Surface Texture Panel Data
### Surface Texture Panel Data  
The factors that influence pavement friction forces can be grouped into four categories-- pavement surface characteristics, vehicle operational parameters, tire properties, and environmental factors [31]. Pavement surface texture is characterized by the asperities present in a pavement surface, which has a significant impact on the skid resistance [32; 33; 34]. The main factors affecting the surface texture of asphalt pavement are pavement materials, traffic load and climate environment [35]. Based on a careful analysis of previous research conclusions on asphalt pavement surface texture, 7 influencing factors are chosen for our panel data model, as shown in Table 3. These factors include the cumulative number of axle loads, the monthly average temperature, the monthly average relative humidity, the monthly rainfall, CA, FAC, and FAF. The surface texture panel data set is generated by taking the 7 influencing factors as independent variables (\(x_{ikt}\) in Equation (4)) and the sensor measured texture depth as dependent variables (\(y_{ik}\) in Equation (4)), taking into account the time scales of field SMTD data and multi-sections of the same surface asphalt layer.  
The data sources of the 7 influencing factors presented in Table 3 are introduced in the following way. Based on the deflection equivalent method, the applied traffic loads are converted into the equivalent axle loads. The axle load conversion index is 4.35 according to Chinese Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2017) [36]. The datasets of air temperature, humidity, and rainfall are obtained from the on-site meteorological station [14]. The aggregate parameters CA, FAC, and FAF were calculated based on the Bailey method. For the asphalt mixtures of surface layer, the half sieve, main controlling sieve, first controlling sieve for fine aggregate, and second controlling sieve for fine aggregate are 4.75 mm (NMPS/2), 2.36 mm (PCS), 0.6 mm (SCS), and 0.15 mm (TCS), respectively [11]. The calculated results of CA, FAC, and FAF are shown in Table 4.
# Crossover Effect in Cement-Based Materials: A Review
## 8 Conclusions  
This paper presents a detailed literature review about the COE phenomenon in CBMs due to accelerated initial curing. The basic concept and mechanism of COE, various methods of accelerating for strength gain at the early ages and reasons for strength decrease at the later ages of CBMs, effect of cementitious materials on COE and materials controlling COE have been discussed in detail. The following conclusions are drawn from this study.  
1. Pure and blended cement products subjected to accelerated curing temperatures show faster hydration and non-homogeneous scattering of hydration products that may result in the COE of their compressive strengths.
2. The COE in compressive strengths of pure and blended cement products depends on the initial curing temperatures from lower to higher values and the amount of internal water present for hydration.
3. The detection of the COE depends on the duration of testing, loss of water loss, temperature and techniques of curing, porosity, type of cement used, proportions of mineral additions, mineralogical characteristics of mineral additions and specific surface and composition of cement used.
4. The COE in pure and blended cement products can appear even after 7 days onwards.
5. The compressive strength loss at later ages in accelerated cured specimens ranges from 2.2% to 31% at 40-60 \({}^{\circ}\)C for accelerated prolong curing.
6. The COE due to accelerated curing can occur at curing temperature as low as 40 \({}^{\circ}\)C.
7. The COE can be controlled using slag blended cement with 80% replacement of GGBPS by OPC, 25-50% addition of slag powder in blended cement mortars, type V Portland cement concrete under an accelerated curing temperature of up to 50 \({}^{\circ}\)C as well as blended mortars with different percentages of natural pozzolan.
# Communication Network Standards for Smart Grid Infrastructures
## 2 Network Standards That Support Communication in Smart Grid Infrastructures
### Smart Transmission Systems  
The objective of power transmission systems is to transmit power from one point to another in a reliable, safe, and environmentally friendly way [2; 3; 11]. Typical transmission systems include the flexible AC transmission systems (FACTS) [3; 12] and the high-voltage direct current [1; 3; 6; 13]. FACTS technology is used for the reliable transfer of large amounts of energy. However, in cases where technical or economic expediencies require the transmission of energy over long distances as well as the interconnection of asynchronous electrical networks, the use of HVDC technology is mandatory. Thus, the main requirement that must be met is the complete integration of the advanced technology equipment of the FACTS and HVDC transmission systems towards the optimization of the load flow and stability of the overall network.  
The sub-stations that support the monitoring and coordination processes of FACTS and HVDC technologies typically use serial connections, as defined by the IEC 60870-5-101 & 104 and DNP3 Secure, at low bit rates of 64 kbps. Even when Ethernet technology is used, transmission rates remain at extremely low levels, as no interface is designed to utilize higher bandwidth, which in turn refrains them from sending large amounts of data. In addition, the IEC 60870-5 [14] standard cannot fully cooperate with the range of requirements of IP network technology, which leads to operational incompatibilities. This type of communication is only possible and is covered by IEC 61850, Ed. 1.0-2.009-1242/136 and later, as the data exchange in these standards is purely based on TCP/IP and provides interfaces for HVDC and FACTS [7; 12; 13].
## 2 Related Work
### SIN Concept and Its Architecture  
Researchers have presented various interpretations for SIN [1; 2; 3; 26]. Combined with the main research plan of the National Nature and Science Fund of China, \"Space information networks based theory and key technology\" [27], we define the concept of SIN as follows.  
SIN is an integrated information network system and networks infrastructure that is carried by space platforms. It is composed of a satellite system (such as geostationary earth orbit (GEO) satellites, nonsynchronous medium earth orbit (MEO) satellites, nonsynchronous low earth orbit (LEO) satellites, mainly responsible for processing the load), other information systems (balloons or UAVs (Unmanned Aerial Vehicles) in near-space), or terminals (ground station, mainly responsible for control) [27]. It can support real-time data acquisition, transmission, and processing of mass data, and achieve systematic information service application through integrated network interconnecting. It provides integrated investigation, navigation, communications, and other services, and realizes battlefield situational awareness such as communication broadcasting; investigation and surveillance; intelligence detection; navigation and positioning; missile warning; weather, hydrology, and terrain; and so on.  
Combining the concept and functional requirements, we design the composition and architecture of SIN (Figure 1). In this paper, we believe that SIN is mainly composed of a geostationary satellite system (GEO layer), medium and low orbital satellite system (MEO/LEO layer), near spacecraft system (Near Space layer), and ground terminal (Ground layer). The equipment entities contained within the four floors have various operating rules and characteristics. Among them, the GEO and ground layers are relatively stable, and the MEO/LEO layer and near-space layers are more complex. When designing the top-level architecture of SIN, the basic idea is [28]: building a space-based (GEO and LEO) backbone network to solve global coverage issues; building a near-space backbone network to solve regional strengthening issues; and integrating and interconnecting with the ground backbone network to solve the problem of integration of the sky and the ground. Among them, the sub-networks of the stationary orbit follow the star topology, the sub-networks in the low-orbit orbit follow the constellation topology, and the sub-networks in the adjoining space also follow the constellation topology, but its structure is relatively simple.
_ISPRS Int. J. Geo-Inf._**2015**, \(4\), 32-46; doi:10.3390/ijgi4010032  
[1] ISPRS International Journal of _Geo-Information_**ISSN 2220-9964**  
www.mdpi.com/journal/ijgi/  
Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization  
[1] [PERSON] 1,* and [PERSON] 2  
[2] Department of Geography, Texas State University, 601 University Drive, San Marcos,  
TX 78666, USA  
[3] Blanton & Associates, Inc., 5 Lakeway Centre Court, Suite 200, Austin,  
TX 78734, USA; E-Mail: [EMAIL_ADDRESS]  
[*] Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-512-245-1337; Fax: +1-512-245-8353.  
[4] Academic Editors: [PERSON] and [PERSON]  
_Received: 25 August 2014 / Accepted: 22 December 2014 / Published: 30 December 2014_  
Expanding traditional time geography, this study examines personal exposure to air pollution and personal pollutant intake, and defines personal health danger zones by accounting for individual level space-time behavior. A 3D personal air pollution and health risk map is constructed to visualize individual space-time path, personal Air Quality Indexes (AQIs), and personal health danger zones. Personal air pollution exposure level and its variation through space and time is measured by a portable air pollutant sensor coupled with a portable GPS unit. Personal pollutant intake is estimated by accounting for air pollutant concentration in immediate surroundings, individual's biophysical characteristics, and individual's space-time activities. Personal air pollution danger zones are defined by comparing personal pollutant intake with air quality standard; these zones are particular space-time-activity segments along an individual's space-time path. Being able to identify personal air pollution danger zones can help plan for proper actions aiming at controlling health impacts from air pollution. As a case study, this paper reports on an examination and visualization of an individual's two-day ozone exposure, intake and danger zones in Houston, Texas.  
air pollution exposure; air pollutant intake; space-time path; time geography; personal health danger zone