esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications
## 2 Mathematical Preliminaries
### Thrust Vector Model of an Ideal Solar Sail  
The propulsive acceleration vector provided by an ideal (i.e., flat and perfectly reflecting) solar sail depends on its attitude relative to the Sun [16; 39]. In particular, when the degradation effects due to the space environment are neglected [40; 41; 42], the propulsive acceleration vector \(\mathbf{a}_{p}\) can be written as  
\[\mathbf{a}_{p}=\beta\,\frac{\mu_{\odot}}{r^{2}}\,\cos^{2}\alpha\,\mathbf{\hat{n}} \tag{1}\]  
where \(\beta\) is the lightness number, that is, the ratio of the maximum propulsive acceleration magnitude at a given distance from the Sun to the local magnitude of gravitational acceleration; \(\mu_{\odot}\simeq 1.3271\times 10^{11}\,\mathrm{km}^{3}/\mathrm{s}^{2}\) is the Sun's gravitational parameter; \(r\) is the Sun-spacecraft distance; \(\mathbf{\hat{n}}\) is the unit vector normal to the sail plane pointing outward from the Sun; and \(\alpha\in[0,\,\pi/2]\,\mathrm{rad}\) is the sail cone angle, that is, the angle between the Sun-spacecraft line and the direction of \(\mathbf{\hat{n}}\).  
In particular, the lightness number \(\beta\) is the typical performance parameter of an ideal solar sail and can be written as  
\[\beta\triangleq\frac{2\,I_{\oplus}\,A\,r_{\oplus}^{2}}{m\,c\,\mu_{\odot}} \tag{2}\]  
where \(I_{\oplus}\simeq 1360.8\,\mathrm{\ W/m^{2}}\) is the solar constant (i.e., the solar irradiance at the reference distance \(r_{\oplus}\triangleq 1\,\mathrm{au}\) from the Sun), \(A\) is the surface area of the sail, \(m\) is the total mass of the spacecraft (assumed to be constant), and \(c\) is the speed of light in vacuum. From Equation (2), it turns out that, basically, the sail lightness number is inversely proportional to the sail loading \(m/A\), defined as the ratio of the spacecraft mass to the sail exposed area [16; 39].
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview
### Network Infrastructure
#### 3.3.1 Peer-to-Peer Communication  
The peer-to-peer communication is usually facilitated by a series of uplink timeslots (Figure 2). Usually, the PAN coordinator in this case, uses a non beacon enabled mode and the transmission is always carried out using unslotted random channel access using CSMA. Each device will be able to communicate with another device in its vicinity using this mode. Active channel scanning is used in this mode to discover the neighbors in its vicinity.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Stochastic Models
#### 3.2.1 Art3d  
ART3D v. 2.0 is an independent FORTRAN code that operates using straightforward text files and has the capability to address numerous coupled reactive transport equations. This versatile software offers three distinct modes of operation: forward mode, enabling the prediction of concentrations within a plume; backward mode, facilitating the estimation of parameter data from monitoring wells; and stochastic mode, allowing for the evaluation of the probability of exceedances at specified locations within the plume [456; 457]. The code incorporates modules designed for automatic parameter estimation and stochastic analysis, employing Monte Carlo methods. Additionally, the output file generated by ART3D can be converted into a 3D image for visualization using compatible software tools. The ART3D code encompasses a comprehensive set of equations, including those representing one-dimensional advection, three-dimensional dispersion, linear sorption, first-order biodegradation, and multiple chemical reactions [458]. Optimization within ART3D is facilitated through the utilization of the PORT library, originally developed by David M. Gay at Bell Laboratories [459]. The parameters subjected to optimization include the retardation factor, percolation rate, dispersion coefficients, decay constants, and concentrations in the source zone. In the Monte Carlo analysis, these parameters can be randomized, with options to specify either a uniform distribution or a normal distribution for each randomized parameter. The ART3D tool underwent testing at a Superfund site in Louisiana, where it was also compared to the results obtained from modelling with BIOCHLOR. This comparison allowed for a meaningful assessment of deterministic and probabilistic approaches [460]. During the testing phase, ART3D was executed over a simulation period spanning several hundred years, with a time step of 25 years. Both simulations with and without the natural attenuation option were conducted. The model provided insights into the declining concentrations of tetrachloroethene (PCE), trichloroethylene (TCE), dichloroethane (DCE), and vinyl chloride (VC). Notably, vinyl chloride emerged as the most problematic substance in the analysed area, presenting a contamination risk to the soil for the next 300 years. ART3D is integrated into the GMS software package and can be downloaded along with the source code as a standalone model.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### Sinr  
Considering path loss, \(N_{dB}\), and small-scale fading as in [11], the modelling of the channels that links MS \(d\) and BS \(p\) can be obtained as:  
\[N_{dB}\Big{(}C_{d,p}\Big{)}=\log_{10}\Big{(}C_{d,p}\Big{)}10\alpha+K \tag{2}\]  
\(K\) is a constant representing path loss at a meter from the BS, and \(\alpha\) represents the pathloss exponent that varies depending on the environment of operation. Distance between MS \(d\) and BS \(p\) is \(C_{d,p}\) which is measured in meters.  
\(Z\) represent cell layers, \(O\) or \(I\), having MS \(b\) located in the cell served by a BS \(p\). During the scheduling period, \(t\), the \(n\)th subcarrier instantaneous SINR \(W_{p,I}^{Z}(t)\), is obtained by:  
\[W_{p,I}^{Z}(t)=\frac{\left|D_{d,p,I}(t)\right|^{2}P_{w}G_{T}N\big{(}c_{0,p} \big{)}}{I_{p,I}^{Z}(t)+N_{0}F_{n}\Delta b}. \tag{3}\]  
The BS antenna gain is represented by \(G_{T}\), whereas \(P_{w}\) is the power assigned to each subcarrier. \(N\big{(}c_{0,p}\big{)}\) represents the path loss. The frequency response due to small-scale fading is \(D_{d,p,I}(t)\sim J\mathcal{N}(0,1)\) within the channel that connects MS \(u\) and BS \(b\) during scheduling period, \(t\), on the \(n\)th subcarrier. The power spectral noise density (PSD) is \(N_{0}\), and the noise factor is represented by \(F_{n}\). \(\Delta b\) represents the subcarrier frequency bandwidth. The interference term \(I_{p,I}^{Z}(t)\) is given by:  
\[I_{p,I}^{Z}(t)=\sum_{d\in\Phi_{I}^{Z}}P_{w}G_{T}N\Big{(}c_{d,p}\Big{)}\left|D _{d,p,I}(t)\right|^{2} \tag{4}\]  
where \(N\Big{(}c_{d,p}\Big{)}\) represents the path loss, and \(D_{d,p,I}(t)\) represents the small-scale fading. The set of interfering BSs that depend on the FFR layer of subcarrier and the subcarrier itself is represented by \(\Phi_{n}^{Z}\). Figure 1 shows the interfering base station set as follows:  
\[\Phi_{I}^{Z}=\left\{\begin{array}{rl}\{1,2,\ldots,18\},&l\in B_{I}\\ \{8,10,12,14,16,18\},&l\in B_{O}\end{array}\right. \tag{5}\]If we assume a homogeneous power allocation, then each subcarrier power allocation is  
\[P_{w}=\frac{P_{T}}{R_{I}+R_{O}} \tag{6}\]  
\(P_{T}\) represents the available power at the BS transmitter.
## 2 Dynamics Modeling
 \tag{8}\]  
where \(f_{x}(t)\), \(f_{y}(t)\), and \(f_{z}(t)\) are unknown time-varying disturbances used to simulate the influence from the wind gusts; \(C_{dx}\dot{x}\), \(C_{dz}\dot{z}\), and \(C_{d\theta}\dot{\theta}\) are correlated to the state-dependent aerodynamic drag forces. \(C_{dx}\), \(C_{dy}\), and \(C_{dz}\) are the associated viscous drag coefficients. Substituting (6) into (7) gives the dynamic equations shown as follows:\[m\ddot{x} = F\mathrm{s}_{\theta}+d_{x} \tag{9}\] \[m\dot{z} = F\mathrm{c}_{\theta}-mg+d_{z}\] (10) \[\dot{J}\theta = M+d_{\theta} \tag{11}\]  
Hence, on the basis of the dynamics (9)-(11), given the desired trajectories in \(x\)- and \(z\)-directions, the control laws \(F\) and \(M\) are designed. Then, based on the actuator model (4), the desired rotation speeds of BLDC are obtained to achieve the trajectory tracking demands. It should be noticed that the BLDC motors have a command deadzone in the low-speed region. This imperfection induces the undesired response and instability of the system. To address this issue, the minimum motor speed command, \(\Omega_{min}\), is considered in this study. Furthermore, to provide enough margin of control torque in the landing phase, the minimum force \(F_{min}\) and the maximum \(F_{max}\) are imposed. The quantities of \(F_{max}\), \(F_{min}\), and \(\Omega_{min}\) are set by  
\[F_{max} = 3 mg\] \[F_{min} = \frac{mg}{2} \tag{12}\] \[\Omega_{min} = \Omega_{idle}\]  
where \(\Omega_{idle}\) is the idle speed value that is dependent on the hardware configuration. From (4) and (12), the maximum value of the magnitude of control torque can be given by  
\[M_{max}^{-} = (F-2C_{T}\Omega_{idle}^{2})L\geq(F_{min}-2C_{T}\Omega_{idle}^{2})L \tag{13}\] \[M_{max}^{+} = 2 LC_{T}\Omega_{max}^{2}-F\geq 2 LC_{T}\Omega_{max}^{2}-F_{max} \tag{14}\]  
where \(F\) is the desired control force to manipulate the \(z\)-directional flight dynamics and will be designed later. \(M_{max}^{+}\) and \(M_{max}^{-}\) are the maximum torques at the upper and lower limits of the BLDC motor speed, respectively. Note that usually \(\Omega_{min}\) is more important to consider than \(\Omega_{max}\). This is because the motor speed cannot be negative. Generally, \(M_{max}^{+}\) and \(M_{max}^{-}\) satisfy  
\[M_{max}^{+}\geq M_{max}^{-} \tag{15}\]  
Conclusively, the following saturation conditions are considered in the flight controller implementation:  
\[F_{max} \geq \Gamma\geq F_{min} \tag{16}\] \[m\dot{z} = F\mathrm{c}_{\theta}-mg+d_{z}\]0 (17) \[m\dot{z} = F\mathrm{c}_{\theta}-mg+d_{z}\]1  
for \(i=1,2\).
## 2 Materials and Methods
### Sample Preparation  
As illustrated in Figure 1a, a vertical stack of layers was chosen as the device architecture for investigating the proximity effect. To provide better heat-sinking, SC-O-SC structures were prepared on highly doped 350 \(\upmu\)m-thick Si wafers, with 0.5 \(\upmu\)m of thermal oxide to insulate the devices electrically from the substrate. All inorganic materials were deposited using a high vacuum DC magnetron sputtering system (AJA Int., Hingham, MA, USA) with a base pressure of 1 \(\times\) 10\({}^{-8}\) Torr. Thin films of Nb (60 nm) served as the bottom electrical contact. Nb was deposited at ambient temperature from a 99.95% Nb target (ACI Alloys Inc., San Jose, CA, USA), in an ultra-high purity (UHP) Argon environment at a pressure of 2.4 mTorr and deposition rate of 0.18 nm/s. Electrodes were defined by depositing through a stainless-steel mechanical shadow mask with 1 mm-wide lines.  
MEH-PPV was purchased from Sigma-Aldrich, St. Louis, MO, USA (Aldrich 541443) and used without further purification. The MEH-PPV was dissolved in chloroform (Sigma-Aldrich) at a concentration of 4 mg/mL and then thin films were formed on top of the Nb layer via spin-coating in air (PMW32, Headway Research, Garland, TX, USA), with the conditions calibrated to yield a film thickness of 40 nm \(\pm\) 3 nm. After spin-coating, samples were dried overnight in high vacuum (10\({}^{-7}\) Torr) before deposition of the top electrode. Sample exposure to the ambient environment during spin-coating was kept to the necessary minimum.  
For the top contact layers, first a protective layer of 5 nm Al (99.99%, ACI Alloys, Inc.) was DC sputtered at a very low rate of 0.004 nm/s and 12 mTorr Argon pressure. Following this, the NbN top electrode was deposited in situ. NbN films between 15 nm and 30 nm in thickness were sputter-deposited from the Nb target in a mixed UHP Ar and Nitrogen environment of 73% and 27%, respectively, at a rate of 0.04 nm/s and pressure of 2 mTorr. A cross-sectional TEM of the top layers appears in Figure 1b. For patterning top electrode layers, the mechanical mask was oriented perpendicular to the bottom electrode lines, in order to create the cross junction illustrated in Figure 1c.
## 2 Experiments
### Surface Characterization
#### 2.4.2 Surface Wettability  
The surface wettability data and CA measurement images of all the test samples were recorded using a contact angle goniometer (NTU MEMS Thermal Control Lab, Brand: Model 100 SB, Sindatek Instruments Co., Ltd., Taipei, Taiwan). A 3 \(\upmu\)L water droplet was dropped onto each test sample for the CA measurement. The static CA and receding CA for Novec-7100 measured in directions parallel (\(\theta_{1}\)) and normal (\(\theta_{2}\)) to the microgrooves are displayed in Table 4. The deviation between the parallel CA and normal CA is known as the degree of anisotropy (\(\Delta\theta_{12}\)). This concept could be observed in [PERSON]'s study [40]. The characteristic of the anisotropic wettability may also appear on a surface with a unidirectional structure [22; 47]. The images of the static CA and dynamic CA measurements are shown in Figure 10.  
Figure 9: Scanning electron microscopy images for (**a**) S2, (**b**) S3, and (**c**) S4 surfaces.
## 1 Introduction
The rationale behind this comparison lies in their contrasting physicochemical properties, environmental relevance, and growing interest in their roles as pollutant carriers. Biochar, a carbon-rich material produced from biomass, is widely recognized for its porous structure and surface functional groups, making it an effective adsorbent for organic contaminants in the environment. In contrast, microplastics, which have become ubiquitous in aquatic environments due to their persistence and widespread pollution, can act as vectors for hydrophobic organic compounds. By comparing biochar and microplastics, we aim to provide insights into their adsorption capacities and mechanisms for removing 4-MBC, a common UV filter, under identical conditions. This comparison enables exploration of the potential of biochar as a sustainable alternative to the plastic materials commonly used in reactor designs for wastewater treatment, indicating possible water contamination with microplastics and related compounds. The comprehensive comparison will provide new insights into the importance of studying both materials in the context of environmental pollution and remediation. Moreover, the use of biochar aligns with global efforts to manage the vast quantities of biomass waste produced annually, transforming waste into a valuable resource that can be utilized in wastewater treatment. The interactions between biochar and 4-MBC, along with the effects of microplastics as a novel carrier, require further investigation to establish their behavior and adsorption capacity for further applications in real water treatment [35; 36; 37; 38; 39; 40; 41]. Thus, this study aims to bridge these knowledge gaps by exploring the combined challenges posed by organic UV filters and microplastics in aquatic environments. Specifically, the research seeks to:Produce and characterize biochar derived from different biomass sources (corn, hemp, and straw) and evaluate its adsorption potential for removing 4-MBC from water. Investigate the adsorption behaviors of 4-MBC on commonly found microplastics (granulated polyethylene (PEg), polyethylene tetraphthalate (PET), polypropylene (PP), and polylactic acid (PLA)) in water to better understand the interactions between organic contaminants and MPs. Provide insights into the combined effects of biochar and microplastics on the removal of emerging contaminants like UV filters, offering potential solutions for improving water treatment methods. By addressing these intertwined environmental challenges, the research hopes to contribute to more effective strategies for managing pollutants in water resources.
## 4 Classification and Features of Smartphone Apps for Solar PV Energy  
In this study, 100 apps for solar PV applications were evaluated, and their current status, limitations, and future perspectives were reviewed. Table 2 summarizes the topics, functions, and platforms of those apps; 60% of them were Android (Google) apps, 20% were iOS (Apple) apps, and 20% were found in both platforms. The influence of the market share by smartphone OS (Android 85% vs. iOS 15%) was clear [20]. However, the apps investigated in this study did not include all of apps in the solar PV field.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Topic of Apps** & **\%** & **Function of Apps** & **\%** & **Platform** & **\%** \\ \hline Computation of sun position & 10 & Database (DB) \& Visualization & 9 & Android & 60 \\ PV system optimal settings & 17 & Measurement & 18 & iOS & 20 \\ PV site evaluation & 11 & Calculation (simple) & 17 & Both & 20 \\ Potential assessment of PV system & 16 & Analysis \& Simulation & 26 & \\ Environmental \& economic & 16 & Monitoring \& Control & 21 & \\ assessment of PV system & 21 & References & 9 & \\ Education \& Learning for PV system & 9 & & \\ \hline \(\Sigma\) & 100 & \(\Sigma\) & 100 & \(\Sigma\) & 100 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of smart-device applications for solar PV energy field.  
The reviewed 100 solar PV apps were classified into seven groups according to their topic or main theme: computation of sun position, PV system optimal settings, site investigation or evaluation for PV system, potential assessment for PV system, environmental or economic assessment for PV system, monitoring and control of PV system, education and learning for PV system. This classification was established by considering the general procedures in solar power planning and management.  
The main function of the apps was categorized into six groups, such as visualization from database (DB), measurement using smartphone built-in sensors, simple calculation, analysis and simulation, monitoring and control, and references. In most cases, the apps included only one functional classification, but when two functional categories were observed, (e.g., measurement and calculation), only one main functional classification was selected according to the authors' judgment. The classification ratio is listed in Table 2. Within one topic, two or three functional classification were usually included. Mobile apps perform their functions via basic memory-based computing. However, communication functions and sensors (internal or external) might also be essential. This can be interpreted as their main distinction from desktop software.
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
## 3 Mechanical Design
### Guidance System
#### 3.2.1 Sensor Integration  
The \(CDPR\) guidance system is often equipped with position [47] and force sensors [48] to monitor the robot performance in real-time, if required by the control strategy. In this prototype, one of the swivel pulleys of the proximal group is equipped with an incremental encoder. The encoder is coaxial with the swivel pulley, thus it measures the angle between the cable and the frame: this feature can be used for platform pose estimation through direct kinematics [49]. In addition, the fixed pulleys of the proximal guidance group are  
Figure 10: Layout of the guidance system of the robot in standard cable arrangement (top view): (1), (6) fixed pulleys, and (2), (3), (4), (5) swivel pulleys; \(a\) and \(b\) sides of the parallelogram defined by the cable loop.  
equipped with a load sensor, a shear beam load cell (Phidget CZL635 micro load-cell, load range 20 kg). Figure 11 shows the force measurement unit: the load cell (1) is directly connected to the pulley support (2) and structurally constraints the fixed pulleys (3) to the machine frame (4) via an auxiliary bracket (5). The system is designed for the load direction of the pulley to be always orthogonal to the shear beam load cell, to ensure the optimal working conditions of the sensor. The load cell, ideally, measures the double of the cable tension value.
# The Impact of Orthodontic Treatment on Pre-Existing Gingival Recessions: A Retrospective Study
## 2 Materials and Methods
### Measurements  
#### 2.2.1 Patient Records  
* General data: age (years), gender (male, female), duration of orthodontic treatment (months).
* Tooth extraction (no extraction, extractions in the maxillary or mandibular dental arch, or extractions in both dental arches).  
All measurements were performed on plaster models before (T0) and after (T1) orthodontic treatment (the day of bracket debond) by the blinded calibrated observer (GA). No gingival enlargement at T1 was noted in patient records.  
* The extent of gingival recession was measured in millimeters, using a digital caliper (Fino, Germany) before (T0) and after (T1) OT. Measurements were recorded to the nearest 0.01 mm (Figure 2). The change of GR (T0-T1) was categorized into three groups (worsened, did not change, improved (this group consists of healed GR, which became better but did not disappear, and fully healed-which disappeared after OT). Recession change of \(\geq\)0.5 mm was recorded as improvement or worsening [23; 24].
* Overbite (OB), measured in millimeters before OT. OB values were categorized into three groups (<1 mm; 1-3 mm; >3 mm) [25].
* Overjet (OJ) was measured in millimeters before OT. Data were categorized into two groups (<4 mm; \(\geq\)4 mm) [25].
* Sagittal relationship (between maxillary and mandibular amines). Class I was considered when upper canine displacement from the ideal position was less than 1 mm.
* Crowding (yes/no) was determined according to the plaster models before OT.
* Position of teeth with GRs in the dental arch was assessed and classified to: (1) labial tooth position due to crowding, (2) labial/buccal root inclination, and (3) unfavorable occlusal contacts with antagonist teeth (cusp-to cusp or deep overbite).
## 3 Results and Discussion
### Gpp-Et Coupling  
Figure 2b shows an example of daily variation of C-W coupling in July 2003 at the Qianyanzhou site. The C-W coupling was measured by the correlation coefficient \(r\) between hourly GPP and ET in the daytime (6 a.m.-6 p.m.). The \(r\) shows large day-to-day variations between 0 and 1, which can be commonly seen in other months at both the Qianyanzhou and Dinghushan sites (not shown here). Notably, around July 15, the C-W coupling strength was reduced each day continuously for 5 days from an \(r\) close to 1 to nearly zero, when VPD kept increasing. Although WUE\({}_{\text{dt}}\) also decreased during these days (Figure 2a), the response of the C-W coupling appeared more sensitive to the change in VPD than that of WUE\({}_{\text{dt}}\).  
C-W coupling strength was similar at both sites, with an average \(r\) around 0.56; thus means that GPP and ET are moderately coupled during summer daytime on a sub-daily time scale. The histogram of \(r\) over 2003-2005 in the summer showed similar distributions for both broadleaved and coniferous forests (Figure 6). This histogram differs from that of WUE\({}_{\text{dt}}\) shown in Figure 3, suggesting different perspectives from which WUE and \(r\) describe C-W coupling. The \(r\) occurrence pattern shows a skewed Bell curve, being least between 0 and 0.2 (-10%) and most between 0.6 and 0.8 (-33%). The mean \(r\) values for both forests are close to 0.6, indicating a modest level of diurnal C-W coupling in the forests.  
Figure 5: Regression coefficients of meteorological conditions (SR, T\({}_{\text{a}}\), VPD, and SWC) with WUE\({}_{\text{dt}}\) in the broadleaved forest at Dinghushan and in the coniferous forest at Qianyanzhou. “**” indicates that \(r\) is at the 90% significance level (\(p<0.10\)), and “***” at the 99% level (\(p<0.01\)).
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 2 Current Status of Research on Grouting Materials
### Inorganic Grouting Materials
[PERSON] et al. [34] examined the impact of phosphogypsum and flue gas desulfurization gypsum on the performance and microstructure of thermally activated recycled concrete. Different gypsum types extended the initial setting times and improved the fluidity and mechanical properties of the grouts.