esa-sceva/satcom-chunk-collection huggingface dataset:
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 3 Results and Discussion
### I-V Characteristics  
Figure 4 shows the measured IV characteristics of the selected CASPAD. An anode-to-ring voltage of 15 V is applied in order to have a high-speed transfer of photo-electrons toward the avalanching p-n junction. Also, the backside substrate voltage is held at the same voltage as the ring voltage in order to guide the photo-electrons which are generated deeply in the epilayer (from e.g., NIR light) upwards to the lateral field between the ring and the anode. The p-n junction breaks down at a reverse voltage of 52.5 V and the associated reverse current at the onset of breakdown is \(\sim\)20 pA. Hence, a high dark count rate is foreseen because most of these carriers flow through the avalanche multiplication region. The dark counts are due to contributions from the surface states, the silicide contact layer and band-to-band tunneling. The measurements in the following sections are defined in terms of excess bias voltage (V\({}_{\text{ex}}\)) which is the voltage above the breakdown voltage (V\({}_{\text{bd}}\)) of 52.4 V and the anode-ring voltage was maintained at \(+15\) V was found to be the optimum voltage for fast transfer of photo-electrons [14].  
Figure 4: Measured IV characteristics of the detector in the dark (with anode-ring voltage of 15 V).  
Figure 3: Simulated electric field profile at the cathode-anode junction at the central multiplication area of the CASPAD at an excess bias voltage (V\({}_{\text{ex}}\)) of 2 V.
## 3 Results  
As already pointed out, the evaluated time period involved three years (2008-2010) and three stations (CF, CO, and FB: see Table 2). The variables used for the intercomparisons are: net radiation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vineyard** & **Abbreviation** & **Latitude** & **Longitude** & **Elevation (m a.s.l.)** \\ \hline Cocconato & CO & \(45^{\circ}05^{\prime}\) N & \(8^{\circ}03^{\prime}\) E & 311 \\ Fubine & FB & \(44^{\circ}58^{\prime}\) N & \(8^{\circ}26^{\prime}\) E & 210 \\ Castiglione Falletto & CF & \(44^{\circ}37^{\prime}\) N & \(7^{\circ}59^{\prime}\) E & 275 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coordinates of the three experimental sites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\#** & **Experiment** & **LSM** & **Boundary Conditions** & **Vegetation Type** & **Soil Type** \\ \hline
1 & EXP1a & UTOPIA & \begin{tabular}{c} Vineyards \\ observations \\ \end{tabular} & \
\begin{tabular}{c} Vineyards \\ observations \\ \end{tabular} &
\begin{tabular}{c} \(4,7,5\) \\ \(4,7,5\) \\ \end{tabular} \\
2 & EXP1b & UTOPIA & \
\begin{tabular}{c} Vineyards \\ observations \\ \end{tabular} &
\begin{tabular}{c} Irrigated crop \\ \(4,7,5\) \\ \(5,5,8\) \\ \end{tabular} \\
3 & EXP2a & UTOPIA & GLDAS-2.1 & Vineyards & \(5,5,8\) \\
4 & EXP2b & UTOPIA & GLDAS-2.1 & Irrigated crop & \(5,5,8\) \\
5 & EXP3a & UTOPIA & GLDAS-2.0 & Vineyards & \(5,5,8\) \\
6 & EXP3b & UTOPIA & GLDAS-2.0 & Irrigated crop & \(5,5,8\) \\
7 & EXP4 & NOAH & From GLDAS-2.1 & _Irrigated crop_ & \(5,5,8\) \\
8 & EXP5 & NOAH & From GLDAS-2.0 & _Irrigated crop_ & \(5,5,8\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of experiments performed, with the explicit indication of the boundary condition used, as well as vegetation and soil types. For NOAH, we have retrieved the data from the GLDAS web site. Italic style indicates that the types of vegetation and soil were set by the GLDAS team. For soil types, the three numbers refer, respectively, to the sites: CF, CO, and FB. The used soil types are thus defined in the following way: 4 (silt loan), 5 (loam), 7 (silty clay loan), 8 (clay loan).  
(RNET, available at CF and CO), sensible heat flux (SHF, measured at all stations), latent heat flux (LHF, available at FB), soil temperature (TS, available at CO and FB), and soil volumetric water content (VWC, available at CO), both relative to the first soil layers, 10 cm deep. Statistical results of intercomparisons were shown in comprehensive tables containing all values, while figures depict only the most relevant results, by using violin plots, and are reported in the Supplementary material. In all cases, the original data are expressed as daily means. A preliminary analysis on three input data used in the UTOPIA simulations (solar global radiation, precipitation, and temperature) is reported in the first subsection, with the aim of underlining the differences in the forcing data and possibly interpret eventual differences in the output data.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 4 Results (Geophysical Applications)
### Modelling of Irregular Interfaces between Strata  
It is indeed well known that real life features between different geological bodies are not as regular as those shown in Section 4.1 or as smooth as those shown in Section 4.2. For a correct modelling of these irregularities, an interpolation of a function to delineate realistically the interfaces could yield the best results. Nevertheless, in order to show that irregularities change dramatically the trajectories of reflected and refracted rays, in this section the model presented in Section 4.1 is modified with a sawtooth function.  
Suppose an irregular horizontal interface between two strata with different constant P-wave speed. In order to model it and thus, to show the effect of adjacent initial conditions geodesics in an interface with different flanks, the interface is modeled as a sawtooth function. The corresponding velocity model would be given by:  
\[v(x,y)=10-3\arctan\biggl{(}\arctan\biggl{(}2\biggl{(}\frac{1}{4}\sin\biggl{(} \frac{x(t)}{2}\biggr{)}+\frac{1}{2}\sin\biggl{(}\frac{x(t)}{4}\biggr{)}-\sin \biggl{(}\frac{x(t)}{8}\biggr{)}-\frac{1}{3}\sin\biggl{(}\frac{3x(t)}{8}\biggr{)} -\frac{1}{5}\sin\biggl{(}\frac{5x(t)}{8}\biggr{)}\biggr{)}+z(t)+100\biggr{)} \biggr{)}. \tag{23}\]  
In Figure 3, two different ray trajectories for slightly different initial conditions are shown. Both rays in both figures come out with \(v_{x}=10\) m/s and \(v_{z}=10\tan(50^{\circ})\), \(10\tan(51^{\circ})\) m/s. In Figure 3a: \(x_{0}=400\) km and \(z_{0}=0\) km. In Figure 3b: \(x_{0}=370\) km and \(z_{0}=0\) km.  
Figure 2: Three refracted seismic rays (black lines) in a synthetic simplified speed profile in Earth’s mantle. Rays in real life are \(R_{1}\), \(R_{2}\) and \(R_{3}\), while the original straight trajectory of the rays, \(R_{1}^{\prime}\), \(R_{2}^{\prime}\) and \(R_{3}^{\prime}\), are shown in dashed yellow lines.
# Formation Kinetics and Morphology Characteristics of Natural Gas Hydrates in Sandstone Fractures
## 2 Methodology
### Experimental Material  
The materials required for this study primarily consist of CH\({}_{4}\) gas, N\({}_{2}\) gas, de-aerated water, L-tryptophan solution, and artificial sandstone fracture models. The purity of the CH\({}_{4}\) and N\({}_{2}\) gases is 99.99%, and these are supplied by Xuzhou Luyou Co., Ltd., Xuzhou, China. The laboratory-produced de-aerated water is employed to eliminate potential interference from dissolved impurities during the formation of CH\({}_{4}\) hydrate. To accelerate the rate of hydrate formation, a 0.2 wt% L-tryptophan solution is selected as the kinetic promoter [51]. The L-tryptophan solution is prepared in-house by dissolving high-purity L-tryptophan powder in de-aerated water to precisely control the concentration and ensure the effective control of the kinetic promoter. Additionally, based on prior research, a disturbance rate of 500 rpm is selected to facilitate the formation of the hydrate. The main components of the artificial sandstone are quartz sand (50.6%), low-sodium feldspar (21.2%), kaolinite (14.3%), and biotite (13.9%), with a density of 2.38 g/cm\({}^{3}\). The specific artificial sandstone fracture model is shown in Figure 3.  
Figure 3: Artificial sandstone fracture models.
# Mobility Prediction of Mobile Wireless Nodes
## 2 Mobility Models
### Gauss-Markov  
Gauss-Markov mobility is a well-known model proposed by [PERSON] and [PERSON] [5]. This model applies a Gaussian concept. The future speed and direction of a node depend on its current average speed and direction, as well as on a Gaussian random noise process. The mobile node will move for a fixed period, the the Gauss-Markov model parameters are calculated again [6; 7].
# Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile
###### Abstract  
DNA origami has emerged as a versatile method to synthesize nanostructures with high precision. This bottom-up self-assembly approach can produce not only complex static architectures, but also dynamic reconfigurable structures with tunable properties. While DNA origami has been explored increasingly for diverse applications, such as biomedical and biophysical tools, related mechanics are also under active investigation. Here we studied the structural properties of DNA origami and investigated the energy needed to deform the DNA structures. We used a single-layer rectangular DNA origami tile as a model system and studied its cyclization process. This origami tile was designed with an inherent twist by placing crossovers every 16 base-pairs (bp), corresponding to a helical pitch of 10.67 bp/turn, which is slightly different from that of native B-form DNA (\(\sim\)10.5 bp/turn). We used molecular dynamics (MD) simulations based on a coarse-grained model on an open-source computational platform, oxDNA. We calculated the energies needed to overcome the initial curvature and induce mechanical deformation by applying linear spring forces. We found that the initial curvature may be overcome gradually during cyclization and a total of \(\sim\)33.1 kcal/mol is required to complete the deformation. These results provide insights into the DNA origami mechanics and should be useful for diverse applications such as adaptive reconfiguration and energy absorption.  
DNA nanotechnology; DNA origami; self-assembly; DNA helicity; mechanics; deformation; cyclization; MD simulations; numerical analysis; oxDNA +
Footnote †: journal: Applied Sciences
# Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
## 4 Opportunities
### AI for Renewable Energy Forecasting  
A consistent challenge with the variable sources of energy such a renewable energy is their unreliability. Weather-dependent power sources will often fluctuate in their strength. Weather can often be unpredictable, which can result in destabilization of the power supply generated from weather-dependent energy sources such as solar and wind. This means that greater precautions could be taken to harness and preserve the energy that was generated. The AI-based approaches can extract a combination of data from local satellite, meteorological stations to provide a precise hour-ahead estimation of weather conditions, including wind, temperature, lightning density, and humidity [122]. This will help in balancing the energy supply and demand in smart buildings that are equipped with hybrid micro-grids.
## 4 Earthquake Motions  
As explained earlier, earthquake motion is used as the input motion in this study to account for interactions between all characteristics of vertical motion. A total of 48 sets of vertical and horizontal motions (two sets of motions from one strong\(-\)motion station) from 5 disastrous earthquake events in Taiwan (listed in Table 4) were used in the numerical simulation. To include the magnitude and distance characteristics of vertical motion, selected sets of motions possess \(Mw\) = 5.8-7.6 and epicenter distance = 2 km-39 km. The wave propagation distance affects the high\(-\)frequency wave of the earthquake motion. To study the frequency content on the uplift, motions close to (2 km) and far away from (37 km) the epicenter were selected.  
The comparisons of earthquake motion indices (peak acceleration; Arias intensity, \(Ia\); and cumulative absolute velocity, \(CAV\)) in Figure 6 show that, in general, the energy of the vertical motion was less than that of the horizontal motion.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Event (Date)**} & \multicolumn{2}{c}{**Epicenter**} & \multirow{2}{*}{\(\mathbf{M_{w}}\)} & **Sets of** & **Epicenter** \\  & **(Latitude/Longitude)** & & & **Motions** & **Distance** \\ \hline Chi\(-\)Chi earthquake & & & & \\ (1999/09/21) & 23.85 N/120.82 E & 7.6 & 16 & 5–37 km \\ Chiayi earthquake & & & & \\ (1999/10/22) & 23.51 N/120.40 E & 5.9 & 8 & 5–33 km \\ Chiashien earthquake & & & & \\ (2010/03/04) & 22.97 N/120.71 E & 5.8 & 6 & 18–37 km \\ Tainan earthquake & & & & \\ (2016/02/06) & 22.92 N/120.54 E & 6.5 & 10 & 2–37 km \\ Hualian earthquake & & & & \\ (2018/02/06) & 24.10 N/121.73 E & 6.2 & 8 & 8–39 km \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information regarding earthquake events.  
Figure 5: Comparisons of centrifuge test and simulation results (modified from [1]).
## 2 Modeling and Research Methods
### Temporally Topological Measures
#### 2.2.1 Node Degree and Distribution  
The degree of each node \(v(t)\in V(t)\), denoted as \(d(t,v)\), is defined as the number of edges that connect to it. To calculate the degree of a given node \(v(t)\), the adjacency matrix \(A(t)\) can be utilized. If the \(v\)th row of \(A(t)\) indicates the adjacent nodes to node \(v\), then the degree of node \(v\) can be obtained using the following equation:  
\[d(t,v)=\sum_{i=1}^{n}A_{vi}(t) \tag{1}\]where \(A_{ij}(t)\) indicates whether there is at least one flight between nodes \(v\) and \(j\) at time \(t\), and is equal to 0 if there are no flights that connect them.  
The temporal average degree of a network refers to the average number of edges connecting each node in the network at a given time \(t\). Higher average degrees in a network imply that nodes within this network are more likely to be interconnected, resulting in higher overall network connectivity. A mathematical formula for the temporal average degree is as follows:  
\[\bar{d}(t)=\frac{1}{N}\sum_{i=1}^{n}\sum_{j=1}^{n}A_{ij} \tag{2}\]
# A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies
###### Abstract  
Recent advances in unmanned aerial vehicles (UAVs), or drones, have made them able to communicate and collaborate, forming flying ad hoc networks (FANETs). FANETs are becoming popular in many application domains, including precision agriculture, goods delivery, construction, environment and climate monitoring, and military surveillance. These interesting new avenues for the use of UAVs are motivating researchers to rethink the existing research on FANETs. Therefore, this paper provides a comprehensive and thorough review of the different types of UAVs used in FANETs, their mobility models, main characteristics, and applications, as well as the routing protocols used in this type of network. Other important contributions of this paper include the investigation of emerging technologies integrated with FANETs.  
unmanned aerial vehicles; UAVs; drones; flying ad hoc network; cloud-based UAV systems; cellular networks +
Footnote †: journal:  
new perspectives for the use of these devices. The main contributions of this paper can be summarized as follows:  
* In addition to an extensive study of the existing academic research, this paper presents a comprehensive UAV classification taxonomy covering gaps not considered in previous surveys.
* A comprehensive effort specifically discusses FANET characteristics and applications, connecting them to the most commonly used routing protocols, UAV mobility models, and Cloud-based UAV Managing Systems (CBUMS).
* A prospective discussion is presented on current investigations of emerging technologies integrated with FANETs and the possibilities they can open for future applications.  
The overall structure of this paper is as follows: A comprehensive UAV classification based on UAV size, weight, range and endurance, altitude, flying mechanism, airspace class, degree of autonomy, engine, and applications is provided in Section 2. Section 3 discusses the main characteristics of FANETs. Sections 4-6 address the main applications of FANETs, routing protocols in FANETs, and UAV mobility models, respectively. The main technologies integrated with UAV-networked systems and the main components of cloud-based UAV-networks are discussed in Sections 7 and 8, respectively. Section 9 discusses the various simulation tools used to design UAV-based networks. Section 10 discusses possible future directions, including the challenges of new technologies integrated with FANETs. Finally, Section 11 concludes the paper by highlighting the main findings of this review.
In particular, the mechanical properties of the reinforced-soil interface have an important impact on the overall stability and long-term service performance of reinforced soil engineering. To explore the interface characteristics and reinforcement mechanism between geogrids and coarse-grained soil under negative temperatures, this paper takes the typical coarse-grained soil in Xinjiang as the material and carries out a direct shear test of the reinforcement-soil interface under different normal stresses, water contents and temperatures. The curve characteristics of the shear displacement-shear stress, the change trend of the peak shear stress and the formation mechanism between the geogrid and coarse-grained soil interface under freezing and nonfreezing conditions are thoroughly analyzed. The formation mechanism of the dilatancy characteristics of the reinforced-soil interface is explained by combining the Mohr-Coulomb strength criterion and apparent friction coefficient. It is concluded that the trend of the shear displacement-shear stress curve between the geogrid and coarse-grained soil interface under the nonfreezing state and freezing state is basically the same. In a state of low normal stress, the curve has no obvious peak, which is closer to the ideal elastic-plastic double linear model. In a state of high normal stress, the curves have more obvious peaks, and the curve type is closer to the elastic-strain softening type. In the nonfreezing state, the shear strength of the reinforcement-soil interface has a great correlation with the water content, which is different in the freezing state. The main sources of the difference are the cementation of pore ice in the soil skeleton in the frozen state, the improvement of the strength of the soil particles themselves, and the further interlocking effect of the geogrid on the soil. In comparison with the reinforced coarse-grained soil under the nonfreezing state the shear strength under the frozen state is significantly improved. In comparison with coarse-grained soil reinforced by geogrids in the nonfreezing state (0 \({}^{\circ}\)C), the shear strength of the frozen state (\(-5\) \({}^{\circ}\)C) is significantly improved. Under normal stresses of 40 kPa, 60 kPa, and 80 kPa, when the water content is 2%, the corresponding peak shear stress increases by 19.39%, 21.71% and 11.34%, respectively. When the water content is 4.5%, the corresponding peak shear stress decreases by 29.98%, 16.17%, and 13.83%. When the water content is 7%, the corresponding peak shear stress decreases by 50.85%, 18.64%, and 21.96%. The apparent friction coefficient between the geogrid coarse-grained soil interface in the nonfrozen state and frozen state decreases with increasing normal stress. With the decrease in temperature, the dilatancy phenomenon of the reinforced soil composite is more obvious. The research results can provide a reference for the construction of reinforced engineering in seasonal frozen soil areas.
###### Abstract  
The latest research on applying beam oscillation in laser beam fusion cutting revealed significant process improvements regarding speed and quality. The reason for this increasing process efficiency remains unexplained; however, theoretical investigations suggest the change in energy deposition (respectively heat conduction) as the cause. The present paper aims to analyze the energy deposition by a novel temperature measurement method. For this purpose, a conventional laser beam cutting setup was equipped with beam oscillation technology and a high-speed temperature measurement setup. Various characteristics of the temperature distribution in the process zone (spatial and temporal resolved temperature profiles, maximum and average values, as well as melt pool size) were evaluated for different conditions of beam oscillation (amplitude, frequency, cutting speed). Additionally, the geometrical properties of the process zone, defining the absorptivity have been measured. The comparison with static beam shaping reveals strong temperature volatility, which is induced by the way of energy deposition and an improved absorptivity over a substantial part of the cut front, with the overall result of enhanced heat conduction. For the first time, changed mechanisms applying beam oscillation instead of static beam shaping have been experimentally identified. Based on these measurements, a previously developed explanatory model was not only confirmed but also extended.  
laser cutting; beam oscillation; dynamic beam shaping; thermographic analysis; energy deposition; heat conduction; melt film; melt pool; process temperature; process efficiency
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 3 System Model
### Wireless Link Model  
In this section, the path loss based model [28] is adopted to setup the wireless link. In radio propagation, the signal needs to overcome path loss to reach the receiver. This transmission loss is always related with the distance from the sender to the receiver and their ambient environment, such as obstacles, transmission medium, etc.  
At the receiver side, the wireless transceiver continues measuring the received signal strength \(P_{recc}\). Therefore, the Signal to Noise Ratio (SNR) can be expressed in Equation (1), where \(P_{tran}\) is the transmission power and \(P_{noise}\) is the noise strength. The receiver can feedback the received signal indicator value through the handshake packets. In this way, the sender can track the variation of path loss and select an efficient transmission power.  
\[SNR=P_{recc}-P_{noise}=P_{tran}+164-20\cdot log(\frac{r}{\lambda})+2.5\cdot SF \tag{1}\]  
Here \(r\) is the communication range, \(\lambda\) is the wavelength and \(SF\) the spreading factor. With a proprietary Chirp Spread Spectrum modulation scheme, the bit error rate \(e_{b}\) can be indicated as a function of SNR, as shown in Equation (2), where alpha and beta depend on the specific configuration of the SF and coding rate [29].  
\[e_{b}=10^{\alpha\cdot e^{\beta SNR}} \tag{2}\]  
Due to the assumption that the bit errors are distributed randomly and independently, the packet error rate \(e_{p}\) can be deduced in Equation (3), where \(l\) is the packet size:  
\[e_{p}=1-(1-e_{b})^{l} \tag{3}\]  
If a packet is not acknowledged, the sender will resend the packet. The average retransmissions of a packet \(\overline{k}\) can be described as:  
Figure 3: DC-DC Buck-Boost power supply circuit for wireless node.  
\[\overline{k}=\sum_{i=1}^{\infty}i(1-e_{p})e_{p}^{i-1}=\frac{1}{1-e_{p}} \tag{4}\]