esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Aware SPICE Compatible Compact Modeling of IGZO Inverters on a Flexible Substrate
## 2 Fabrication and Measurement  
In this study, a-IGZO was processed on a flexible PET substrate by taking advantage of its low-temperature process. The 3-dimensional (3D) structure and circuit diagram of the NMOS-only inverter are shown in Figure 1a,b. Copper (Cu) gate metal with a thickness of 20 nm was deposited using an electron-beam (e-beam) evaporator. The 40 nm Al\({}_{2}\)O\({}_{3}\) gate insulator was deposited using an ALD system at 80 \({}^{\circ}\)C. In detail, an ALD Al\({}_{2}\)O\({}_{3}\) film was formed using Al(CH\({}_{3}\))\({}_{3}\) (trimethylaluminum; TMA) and H\({}_{2}\)O.  
Thereafter, a target with the ratio of In\({}_{2}\)O\({}_{3}\).Ga\({}_{2}\)O\({}_{3}\).ZnO = 1:1:1 mol\(\%\) was radio frequency (RF) sputtered to form a 35 nm IGZO channel under the condition of Ar:O\({}_{2}\) = 3:0.1 sccm. Source and drain electrodes (40 nm Cu) were also deposited using an e-beam evaporator. The gate insulator was wet-etched using buffer oxide  
Figure 1: (**a**) 3-dimensional (3D) structure and (**b**) circuit diagram of an NMOS-only indium gallium zinc oxide (IGZO) inverter. (**c**) Top view of the fabricated IGZO inverter. (**d**) Schematic of bending test tool and bending measurement using the fabricated flexible device.  
interconnection, followed by a metallization performed with 60 nm Cu to connect the inverter circuit. A top view of the fabricated device for which inverter manufacturing has been completed is shown in Figure 1c.  
The mechanical stress to the IGZO inverters was applied with a bending radius of 10 mm, and the electrical stress was tested by applying DC/AC stress to V\({}_{\text{GS}}\) and V\({}_{\text{DS}}\) using Keithley 4200. The schematics of the bending test, bending tool, and bending measurement of the fabricated flexible device are shown in Figure 1d. It should be noted that some local surfaces with small dimensions might experience negligible bending stress.
## 3 Materials and Test Design
### Effect of Specimen Cutting on the Test Results  
The test pieces are cut and, thereafter, assembled together for the rutting test. A rubber pad is used to fill the small gap at the edge of the mould to eliminate the influence of specimen cutting on test results. However, the change in the spatial position of aggregate particles in the asphalt mixture subjected to load after cutting is different from that of the uncut specimen. In order to compare their differences, 70# asphalt AC-13C mixture and SMA-13 mixture are used in the comparative tests. Six groups of specimens for rutting are made for each mixture. In the test, three groups of specimens are cut and photographed after 1-h loading, and the other three groups are cut after 2-h loading to obtain the images of their cross-sections. By comparing the quantity of contact points between the cut and uncut specimens, the difference between the two test methods can be verified. The results are summarised in Table 7.  
The results listed in Table 7 indicate that the number of contact points in the cut specimens has relative errors at all loading stages compared with the uncut specimens; the errors in SMA-13 and 70# asphalt AC-13C are approximately 5.4% and 9.8%, respectively. Although specimen cutting has caused some errors on the test results, these are still within the allowable range of accuracy.
# Combined Navigation and Tracking with Applications to Low Earth Orbit Satellites +
## 3 Xona PULSAR(tm) Demonstration Platform
### Approximate LEO Orbit Calculation  
To be effective, aiding should be applied at every loop closure, typically with a period of 80-200 ms. This rate of calculation multiplied by the number of tracked signals represents a considerable amount of processing when satellite orbits are derived from Keplerian parameters. To reduce this load, the full ephemeris algorithm is computed only on a sparse time grid, roughly every 30 to 60 s. At these epochs (\(t_{0}\) in Figure 7), the ephemeris errors are small and only determined by the accuracy of the orbital model.  
For MEO, a simple constant acceleration hypothesis (4) can propagate the coordinates \(x_{0},v_{0},a_{0}\) from \(t_{0}\) to \(t_{REF}\), efficiently and with good accuracy.  
\[\begin{split}\mathbf{x}^{p}(t_{REF})=\mathbf{x}^{p}(t_{0})+\dot{\mathbf{x}}^{ p}(t_{0})\Delta t+\frac{1}{2}\ddot{x}^{p}(t_{0})\Delta t^{2}\\ \Delta t=(t_{REF}-t_{0})\end{split} \tag{4}\]  
For LEO, due to higher orbit dynamics, satellite coordinates had to be integrated with steps of 3-5 s. At epochs \(t_{1}\dots t_{k}\), accelerations were recomputed as from (5) (see [13] for notation). The constant acceleration model (4) was still used, but only for the remaining small \(\Delta t\) until \(t_{REF}\). This combined method was a good trade-off between accuracy and the computational load of a standard Keplerian model (like that employed in GPS).  
\[\begin{split}\ddot{x}=-\frac{\mu x}{r^{3}}+F\Big{(}1-5(z/r)^{2} \Big{)}(x/r)+2\dot{y}\dot{\Omega}_{e}+x\dot{\Omega}_{e}^{2}\\ \ddot{y}=-\frac{\mu y}{r^{3}}+F\Big{(}1-5(z/r)^{2}\Big{)}(y/r)-2 \dot{x}\dot{\Omega}_{e}+y\dot{\Omega}_{e}^{2}\\ \ddot{z}=-\frac{\mu z}{r^{3}}+F\Big{(}3-5(z/r)^{2}\Big{)}(z/r) \end{split} \tag{5}\]  
where \(\text{F}=-(3/2)I_{2}\big{(}\mu/r^{2}\big{)}(R_{E}/r)^{2}\), \(r=\sqrt{x^{2}+y^{2}+z^{2}}\), and \(I_{2},\mu,R_{E},\dot{\Omega}_{e}\) are the WGS-84 parameters [13].
# Evaluating the Capture Efficiency of Microchannel Plates Through Photoelectron Detection
## 3 Results
### Data Processing  
We developed a special data processing method to evaluate the count of detected electrons, which is related to the number of pulses in the signal output by the MCP. First, we established an appropriate threshold to convert the raw signals into binary format. We chose the average peak-to-peak value of the signal as the threshold reference to ensure the detection of low pulse signals while effectively excluding weak noise interference. Peaks exceeding the threshold are identified as valid pulses, while those below are considered background noise. After this conversion, the original analog signals turn into a sequence of discrete pulses. Next, we performed a statistical analysis of the intervals between adjacent pulses, as shown in Figure 4. The experimental results demonstrate that the pulse intervals exhibit an approximately exponential distribution. This behavior may be attributed to the independent operation of each microchannel in the MCP, which aligns  
Figure 3: Variance of the MCP signal with V\({}_{\text{M-Z}}\) obtained with and without UV irradiation.  
Figure 2: Scanning electron microscopy (SEM) images of (**a**) hexagonal MCP and (**b**) circular tapered MCP.  
with the independence assumption of the Poisson process, in which the probability of detecting \(k\) electrons within a unit time \(t\) is given by  
\[P(k)=\frac{(\lambda k)^{k}e^{-\lambda t}}{k!} \tag{1}\]  
when \(k=0\), it indicates that no electrons arrive within time \(t\), and the probability is  
\[P(0)\!=e^{-\lambda t} \tag{2}\]  
Thus, the probability that at least one electron arrives within time \(t\) is  
\[f(\Delta t\leq t)\!=1-\mathrm{e}^{-\lambda t} \tag{3}\]  
Taking the derivative with respect to \(t\), we obtain the probability density function (PDF) of the pulse interval:  
\[\mathrm{P}(\Delta t)\ =\ \lambda\mathrm{e}^{-\lambda\Delta t} \tag{4}\]  
Additionally, the stochastic nature of the electron multiplication process within the channels further reinforces the characteristics of the exponential distribution. Specifically, the random arrival of electrons and their subsequent amplification in the channels result in pulse intervals that follow an exponential distribution, as predicted by Poisson statistics.  
Then, the average value of the pulse intervals (\(T_{\mathrm{s}}\)) was used to represent the average time required for an electron to enter the MCP channels, which was defined as  
\[T_{\mathrm{s}}=\frac{1}{N}\!\sum_{i=1}^{N}\Delta t_{i} \tag{5}\]  
where \(\Delta t_{i}\) represents the \(i\)-th pulse interval, and \(N\) is the total number of pulse intervals. In practice, as \(\Delta t_{i}\) is exponentially distributed when the sampling time is long enough, a fitting curve can be acquired:  
\[f(\Delta t;\ \lambda)=\lambda\mathrm{e}^{-\lambda\Delta t} \tag{6}\]  
where \(\lambda\) is the reciprocal of \(T_{\mathrm{s}}\). Subsequently, the value of \(T_{\mathrm{s}}\) can be determined using this equation.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 1 Introduction
PWV is retrieved by the atmospheric precorrected differential absorption (APDA) algorithm, which simulates the total radiance and path radiance received by B8A and B9, and constructs the relationship between PWV and the radiance ratio after removing the path radiance. [PERSON] et al. [40] validated the AOT and PWV provided by ESA-L2A using the data from 94 Aerosol Robotic Network (AERONET) stations over Europe and adjacent regions. [PERSON] et al. [39] compared the AOT, PWV, and surface reflectance from ESA-L2A products and S2C-L2A outputs. [PERSON] et al. [41] constructed a relationship between radiance ratios and PWV using the APDA algorithm, and compared the Sentinel-2 retrieved PWV with the AERONET measurements. The above methods have good performance, but these PWV retrieval models are built for a global scale, and may not obtain optimal results for local areas. Moreover, these algorithms ignore the reflectance difference of multiple land-cover types. It is necessary to propose a Sentinel-2 PWV retrieval method that considers multiple land-cover types and is more suitable for specific areas. The objective of this paper is to develop an algorithm for retrieving Level 2A PWV data using Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. First, the radiative transfer process is simulated using the radiative transfer model MODTRAN 5, and lookup tables (LUTs) for PWV retrieval are constructed. Next, the PWV products of GPS, Terra-MODIS PWV product (MOD05), and ESA-L2A are used to validate the accuracy of the S2-L2A PWV. Then, the spatial distribution features of S2-L2A PWV are analyzed. In order to present the study logically, we structured the remainder of the paper as follows: Section 2 introduces the materials and methods; Section 3 presents the validation and spatial distribution results; Section 4 discusses the advantages, errors and transferability of the proposed algorithm; and the last section provides some conclusions. This study provides technical support for large-scale accurate prediction of PWV and offers a data source for remote sensing applications, such as meteorological forecasts and ecological management.
# Scaled Modeling and Measurement for Studying Radio Wave Propagation in Tunnels
## 3 Scaling the Ray Tracing Model
In general, the model does not specify the total number of reflected rays playing roles in the link budget (1). When omni-directional antennas are used, there is, in principle, a large number of reflection paths between the transmitter and the receiver antennas. However, the high order rays are attenuated due to the multiple reflections, and their contribution to the overall link budget is negligible (as discussed in [31]). Employing directive antennas results in even a smaller number of contributing rays determined by the transmitting antenna beam width and by the field of view of the receiving antenna. In directive links, the number of rays can be evaluated via the beam-width of the antenna; \(\Theta_{v}\) in the vertical and \(\Theta_{h}\) in the horizontal dimensions according to:  
\[M=int[\frac{R(0,\ 0)}{H}\tan(\frac{\Theta_{v}}{2})],\ \ Vertical, \tag{5}\]  
\[N=int[\frac{R(0,\ 0)}{W}\tan(\frac{\Theta_{h}}{2})],\ \ Horizontal. \tag{6}\]  
We note that the received power is a result of a coherent summation of the ensemble of rays arriving at the receiving antenna and collected by its aperture. This effect is considered via the antenna gain \(G[m,n]\) for each of the \((m,n)\) propagating ray directions. The study was performed in a 75 m pedestrian tunnel made of concrete, with height \(H=1.85\) m and width \(W=1\) m, as shown in Figure 2.  
The received signal strength was measured along the tunnel at intervals of 25 cm between measurements. The measurements were performed for three frequencies: 1, 2.4, and 10 GHz, while 94 GHz was used in a 1/10 th down-scaled model. Table 1 summarizes the parameters of the different experiments in UHF, SHF, and W-band regimes. Figure 1: A two-dimensional illustration of ray tracing inside a tunnel from the (**a**) side walls, and (**b**) ceiling/floor reflections. The graphs in Figure 3 show the results of link budget simulations along a tunnel with concrete walls for the three frequencies 1, 2.4, and 10 GHz. Since the dielectric permittivity \(\varepsilon_{r}\) of concrete is spread between 6 and 8, depending on its ingredients and condition [34], a comparison was made between the expected results for different permittivity values. Figure 3 shows an almost identical expected performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Band**} & **UHF** & **UHF** & **SHF** & **W-Band** \\  & **Real Tunnel** & **Real Tunnel** & **Real Tunnel and** & **Scaling for 10 GHz** \\ \hline Frequency & & & & \\ (GHz) & 1 & 2.4 & 10 & 94 \\ Wavelength (cm) & 29.98 & 12.49 & 2.998 & 3.19 \\ \(W/\lambda\) & 3.335 & 8.006 & 33.35 & 313.5 \\ \(H/\lambda\) & 6.17 & 14.81 & 61.7 & 579.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Wave frequencies and respective scaling ratios. Figure 3: _Cont_. Figure 2: Straight tunnel with concrete walls of rectangular profile with a height of 1.85 m, width of 1 m (**a**) a schema, and (**b**) a real tunnel.
# Prediction of Injuries in CrossFit Training: A Machine Learning Perspective
## 3 Results  
### Identification of Important Risk Factors  
Figure 1 shows the classification accuracy (10 FCV) of the six competing ML models with respect to the number features, as they have been ranked by the proposed FS algorithm. An increase on the classification performance of all ML algorithms was observed for the first 6 selected features, whereas the inclusion of additional features had no significant effect on the classification accuracies. The overall best 10 FCV accuracy was obtained by Adaboost at the first six selected features (0.7466). In light of these results, the first six selected features were finally kept for the subsequent analysis.  
Table 3 cites the six most important risk factors as they have been selected by the proposed FS methodology. Risk factors associated with the frequency/intensity of exercise (three variables) as well as medical history (two variables) were selected. Gender was also proved to be a contributing factor and was included in the final feature subset.  
Independent-samples \(t\)-tests were conducted to compare the three continuous factors (F2-F4) with the outcome (injured/non-injured). Chi-square tests of independence were performed to examine the relation between the outcome and the qualitative factors F1, F5 and F6. The results of the statistical analysis verify that there are significant changes between the two subpopulations (C0: non-injured and C1: injured) in all the six selected risk factors. Table 4 below highlights the distribution of samples in the entire population and the two sub-cohorts for each of the six selected risk factors.  
Figure 1: Accuracy with respect to the number of selected features for all the competing algorithms.  
\begin{table}
\begin{tabular}{l l l c c} \hline \hline \# & **Risk Factor Category** & **Questionnaire Items** & **\(t\)-Statistics** & **Pearson Chi-Square** & \(p\)**-Value** \\ \hline F1 & Demographics & Gender & & 36.35 & 0.00 * \\ F2 & CrossFit experience & How long have you been participating in & 11.462 & & 0.00 * \\ F3 & Days/weeks of training & On average, how many days a week do & 4.121 & & 0.00 * \\ F4 & Training duration & On average, what is the duration of your & 7.352 & & 0.00 * \\ F5 & Prior to CrossFit level of & What was the level of your athletic & & \\  & activity & activity—fitness in the last 1 year before & 38.43 & 0.00 * \\  & & you start CrossFit? & & \\  & & Did you mention to your CrossFit trainer & & \\  & & from the beginning (before you started & & \\ F6 & Medical history/previous & training with him) a detailed medical & & 26.84 & 0.00 * \\  & injuries & history (with previous injuries or & & \\  & & accompanying health problems you may & & \\  & & have had)? & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of the selected features with respect to the outcome (presence of injury).
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 4 Experimental Setup
### Software Configuration  
The server and client applications developed in the C programming language employ HTTP communication to enable efficient data exchange over a network. It is important to highlight that HTTP was utilized primarily for experimental evaluation and to facilitate ease of implementation. The proposed protocol is designed to be protocol-agnostic, allowing for adaptation and deployment across industry-standard smart grid communication protocols, such as IEC 61850, DNP3, or MQTT over TLS. Furthermore, the security mechanisms implemented, including ECDSA for authentication, ECDH for key exchange, and AES-256 encryption, operate independently of HTTP, ensuring compatibility with secure transport layers.  
The communication protocol encompasses three fundamental operations:  
1. /authenticate: handles client authentication using ECDSA.
2. /exchange-ecc: establishes a shared secret using ECC-based ECDH.
3. /send-message: facilitates secure message exchange using AES-256-CBC encryption.  
The server was hosted on port 3000 and was configured to handle incoming HTTP requests at [[http://192.168.1.1:3000](http://192.168.1.1:3000)]([http://192.168.1.1:3000](http://192.168.1.1:3000)). Clients connect to the server using their designated IP addresses and interact with predefined API endpoints for secure communication. For cryptographic operations, we utilized Open Secure Sockets Layer (OpenSSL) libraries,  
Figure 4: Overview of server–client communication architecture.  
implementing ECDSA for authentication, ECDH for secret key establishment, and AES-256-CBC for encryption and decryption.  
Both server and client operations were orchestrated using a custom Makefile, which streamlines the processes of key generation, compilation, and execution. The ECC keys for both parties were pre-generated utilizing a script and securely stored in designated files for future reference. This methodology significantly enhanced security by circumventing the need for public key transmission across the network. The private keys were securely stored in designated files, while the public keys were embedded in certificates issued by a trusted Certificate Authority (CA). These certificates allow the public keys to be shared and verified securely without exposing sensitive private key information.
# PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
###### Abstract  
The fifth-generation (5G) communications systems are expecting to support users with diverse quality-of-service (QoS) requirements. Beside these requirements, the task with utmost importance is to support the emergency communication services during natural or man-made disasters. Most of the conventional base stations are not properly functional during a disaster situation, so deployment of emergency base stations such as mobile personal cell (mPC) is crucial. An mPC having moving capability can move in the disaster area to provide emergency communication services. However, mPC deployment causes severe co-channel interference to the users in its vicinity. The problem in the existing resource allocation schemes is its support for static environment, that does not fit well for mPC. So, a resource allocation scheme for mPC users is desired that can dynamically allocate resources based on users' location and its connection establishment priority. In this paper, we propose a public safety users priority-based context-aware resource allocation (PS-CARA) scheme for users sum-rate maximization in disaster environment. Simulations results demonstrate that the proposed PS-CARA scheme can increase the user average and edge rate around 10.3% and 32.8%, respectively because of context information availability and by prioritizing the public safety users. The simulation results ensure that call blocking probability is also reduced considerably under the PS-CARA scheme.  
context-aware resource allocation; interference reduction; internet-of-things; public safety; 5G systems +
Footnote †: journal: Sensors
## 3 Experimental Section
### Expression of VMPSTS in L. lactis  
For protein expression, overnight culture of _L. lactis_ NZ9000 harboring pNZ:VMPSTS or empty pNZ8048 (as negative control) were inoculated into fresh GM17 medium at 5% (v/v) and grown to an OD\({}_{600}\) of 0.4. The culture was split into sub-samples consisting of uninduced samples and samples induced with nisin at concentrations ranging from 10-60 ng/mL for 2 and 4 h. For subsequent experiments, cultures were induced at 40 ng/mL for 2 h after determining the optimum induction condition. After induction, the cells were harvested in an assay buffer (15 mM 3-morpholino-2-hydroxypropanesulfonic acid (MOPSO), pH 7, 10% v/v glycerol, 10 mM MgCl\({}_{2}\), 1 mM MnCl\({}_{2}\), 1 mM sodium ascorbate and 2 mM dithiothreitol) [20] or (20 mM Tris-HCl, pH 8.0, 5 mM MgCl\({}_{2}\) and 2 mM dithiothreitol), modified from [PERSON] _et al_. [46], by centrifugation at 1438 \(\times\) g for 10 min at 4 \({}^{\circ}\)C. Crude extracts were prepared by subjecting the cells to sonication treatment using the Omni Ruptor 4000 (Omni International, GA, USA) set to 10% power and pulsed for 2 min per sample. Then, the samples were centrifuged at 16,000 \(\times\) g, for 10 min, at 4 \({}^{\circ}\)C with the final aqueous phase subjected to SDS-PAGE and Western Blot analyses.  
For recombinant protein purification, His SpinTrap (GE Healthcare, WI, USA) columns equilibrated with ten column volumes of binding buffer containing 20 mM imidazole were used according to the manufacturer's protocol to trap recombinant protein via the N-terminal His-tag fusion. Subsequently, the column was washed with another ten column volumes of binding buffer and the final elution was performed in the presence of 300 mM imidazole. After purification, the proteins were desalted using HiTrap Column (GE Healthcare, WI, USA) according to the manufacturer's protocol. At each step, the protein was analyzed by SDS-PAGE and Western Blot. SDS-PAGE analysis was done according to Laemmli [47] while Western Blot was performed using the chromogenic Western MAX\({}^{\text{TM}}\) horse radish peroxidase (HRP) kit (Amresco, OH, USA) according to the manufacturer's protocol with slight modifications. The primary antibody used was mouse anti-his IgG (Novagen, NJ, USA) while the secondary antibody, HRP conjugated goat anti-mouse IgG, was supplied in the kit. Briefly, the samples were run on 12% SDS-PAGE Tris/glycine gels and transferred to polyvinylidene fluoride (PVDF) membrane using a semi-dry blotter (Biorad, CA, USA). Blocking was done using 1% (w/v) bovine serum albumin. PageRank\({}^{\text{TM}}\) Unstained Protein Ladder (Fermentas, Ontario, Canada) was used as SDS-PAGE marker while for Western Blot, PageRank\({}^{\text{TM}}\) Prestained Plus Protein Ladder, Fermentas, Ontario, Canada was used.