esa-sceva/satcom-chunk-collection huggingface dataset:
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 1 Introduction  
The continuous advancements in wireless communication technology increase the need for efficient and dependable communication networks [1]. The networks need to have more data capacity and low bit errors at the receiver end [2]. Orthogonal frequency division multiplexing (OFDM) is a digital data transmission technique that encodes data onto numerous carrier frequencies for transmission over a common channel [3]. OFDM is used in applications such as wireless networks, digital television broadcasting, power line communication networks, and 4 th and 5 th generation mobile communications [4]. A communication signal has three basic characteristics that can be manipulated, i.e., phase, frequency, and amplitude. These can be varied depending on the transmission modulation scheme being used in the communication system [5].  
Quadrature phase-shift keying (QPSK) is a phase-altering modulation scheme used for modulating OFDM subcarriers. QPSK uses four phases with each phase being assigned a unique number of binary digits [6]. To ensure good data recovery, the demodulator is custom designed for the symbol sets used during modulation. Regardless of its popularity, the QPSK technique greatly suffers from phase ambiguity and carrier offset drift [7]. This automatically introduces inter-symbol interference (ISI) and inter-carrier interference (ICI) on the transmitted signal [8]. However, to further improve on the progress made in developing the QPSK technique, other enhancement methods need to be utilized [9].  
The genetic algorithm (GA) and the wind-driven optimization (WDO) are metaheuristic tools that give precise solutions to complex optimization and mathematical problems [10]. In their problem evaluation procedures, both algorithms begin with a multitude of solutions and use the Pareto sense in prioritizing the solutions [11].  
In this paper, the two algorithms were merged into a hybrid algorithm. The WDO was incorporated into the GA as its selection function. The hybrid genetic algorithm wind-driven optimization (GAWDO) was used in an analytical modeling technique for deducing and designating the optimum phases for a QPSK signal in an OFDM network. When compared to the conventional QPSK, the GAWDO-assisted QPSK showed better results in both noisy and noiseless OFDM networks. The states and operating conditions that were beyond the scope of the developed QPSK could also be noted.  
The rest of the paper is organized as follows: Section 2 gives the conducted literature survey which covers related work and the emanating research questions. Section 3 gives a detailed description of the proposed GAWDO for OFDM network optimization and the detailed QPSK enhancements. Section 4 presents the performance evaluation and Section 5 gives the results and their analysis. Finally, Section 6 gives the conclusion of the paper.
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
###### Abstract  
We present a global 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010-2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH\({}_{4}\) yr\({}^{-1}\) for Russia, 12.8 to 14.3 Tg CH\({}_{4}\) yr\({}^{-1}\) for temperate South America, and 43.2 to 44.9 Tg CH\({}_{4}\) yr\({}^{-1}\) for contiguous USA, and the values decrease from 54 to 51.3 Tg CH\({}_{4}\) yr\({}^{-1}\) for China, 26.2 to 25.5 Tg CH\({}_{4}\) yr\({}^{-1}\) for Europe, and by 12.4 Tg CH\({}_{4}\) yr\({}^{-1}\) for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.  
methane emissions; NTFVAR; GOSAT; EDGARv4.3.2; UNFCCC reports 20 September 2019; Accepted: 22 October 2019; Published: 24 October 2019
## 6 Security Analysis and Discussion
### Security Architecture Analysis  
SMART DShot implements a layered security architecture consistent with defense-in-depth principles through protocol layer enhancement with ML-enhanced DShot validation, signal layer real-time timing analysis and anomaly detection, algorithm layer multi-algorithm ensemble providing redundancy, and system layer cross-channel correlation and pattern analysis. This layered approach ensures that attack vector compromise in any single layer does not result in total system failure.  
ML-based security systems face unique challenges from adversarial attacks targeting algorithm weaknesses, including adversarial inputs designed to fool ML classifiers, model extraction attempts to reverse engineer algorithm parameters, training data poisoning compromising algorithm adaptation mechanisms, and side-channel attacks exploiting timing or power consumption patterns. Mitigation strategies include ensemble voting where multiple algorithms reduce single-point-of-failure risk, bounded adaptation limiting parameter updates to prevent dramatic model drift, anomaly bounds providing hard limits on acceptable timing deviations regardless of ML output, and constant-time implementation with fixed execution latency preventing timing side channels.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction  
The essential difference between \"the Internet\" and \"the Internet of Things\" (IoT) [1] is that in the IoT, there is just \"less of everything\" available in a given device or network device: less memory, less processing power, less bandwidth, etc.; and of course, less available energy. This is either because \"things\" are battery driven and maximizing lifetime is a priority or because their number is expected to be massive (it is estimated that there will be 50 billion connected devices by 2020 [2]). This drive to \"do more with less\" leads to constraints that limit the applicability of traditional cellular networks, as well as of technologies, such as WiFi, due to energy and scalability requirements.  
Another range of protocols and technologies has emerged to fulfill the communication requirements of the IoT: Low-Power Wide Area Networks (LPWAN). Colloquially speaking, an LPWAN is supposed to be to the IoT what WiFi was to consumer networking: offering radio coverage over a (very) large area by way of base stations and adapting transmission rates, transmission power, modulation, duty cycles, etc., such that end-devices incur a very low energy consumption due to their being connected.  
LoRa (LoRa Alliance, [[https://lora-alliance.org](https://lora-alliance.org)]([https://lora-alliance.org](https://lora-alliance.org))) is one such LPWAN protocol and the subject of study for this paper. LoRa targets deployments where end-devices have limited energy (for example, battery-powered), where end-devices do not need to transmit more than a few bytes at a time [3] and where data traffic can be initiated either by the end-device (such as when the end-device is a sensor) or by an external entity wishing to communicate with the end-device (such as when the end-device is an actuator). The long-range and low-power nature of LoRa makes it an interesting candidate for smart sensing technology in civil infrastructures (such as health monitoring, smart metering, environment monitoring, etc.), as well as in industrial applications.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
## 2 Methodology
### Physical Modelling Overview
#### 2.1.2 Overburden and Seismic Waves  
The extent of seismic influence on the SIS system is greater when SV waves are incident vertically in the supercritical angle case. The maximum vertical acceleration of the seismic wave increases at this time. The motion of the foundation surface is essentially the same regardless of the presence or absence of the overlying fluid. The influence of sea waves on the seismic response results is very small [11]. Therefore, the effect of seawater on seismic waves is not considered in this study, and seawater is equivalent to the overlying pressure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modeling Type** & **Component** & **Properties** & **M1** & **M2** & **M3** \\ \hline \multirow{4}{*}{Numerical model} & \multirow{2}{*}{SCP} & Outer diameter [m] & 2.4 & 1.6 & 1 \\  & & Number \(\times\) Length [m] & \(14\times 15\) & \(23\times 15\) & \(25\times 15\) \\ \cline{2-6}  & & \multirow{2}{*}{Foundation} & Surface area [m\({}^{2}\)] & \multicolumn{2}{c}{52.91 \(\times\) 3 S (Pile spacing)} \\ \cline{3-6}  & & Deformation modulus \(E_{\mathrm{S}}\) [kPa] & \multicolumn{2}{c}{\(E_{\mathrm{S}}=E_{\mathrm{S}}\)*} \\ \hline \multirow{4}{*}{Physical model} & \multirow{2}{*}{SCP} & Outer diameter [mm] & 48 & 32 & 20 \\ \cline{3-6}  & & Number \(\times\)Length [m] & \(4\times 0.5\) & \(5\times 0.5\) & \(7\times 0.5\) \\ \cline{2-6}  & & \multirow{2}{*}{Foundation} & Surface area [m\({}^{2}\)] & \multicolumn{2}{c}{0.09} \\ \cline{3-6}  & & Deformation modulus \(E_{\mathrm{S}}\)* [kPa] & \multicolumn{2}{c}{\(E_{\mathrm{S}}\)* = \(E_{\mathrm{S}}\)} \\ \hline \hline \end{tabular} Note: M1, M2, and M3 represent the three SCP arrangements.  
\end{table}
Table 1: Dimensions of piled foundations in model experiments.  
Figure 2: (**a**) Experimental equipment and SCP installation process, (**b**) Model size (\(\theta\) is the angle of the pile arrangement).  
It is assumed that the tunnel is in a straight line when it is subjected to upward movement by buoyancy, and the frictional \(f_{\rm s}\) resisting the uplift of the overburden and the tunnel is provided by the lateral earth pressure. The safety coefficient of the immersed tunnel \(\omega=(W+f+G)/F_{\rm float}\geq 1.1\) meets the design requirements, then the minimum thickness of the backfill layer is \(h=f\) (\(F_{\rm float}\), \(G\), \(W,f_{\rm s}\)), where \(W\) is the gravity in the overburden layer, \(G\) is the gravity of the immersed tunnel, and \(F_{\rm float}\) is the buoyancy of the structure.  
The physical model seismic waves were selected from PEER Ground Motion Database-NGA west2, and data from the Keslin Seismic Observatory in Greece were analyzed in the time history. The seismic intensity of 7.5, characteristic period of 0.40 s, damping ratio of 0.05, and peak acceleration of 0.05 g are shown in Figure 3.
## Materials and Methods
### Analytical Methods
#### 2.4.4 Volatiles  
The volatile profile of vinegar made by free and immobilized cells of both types of cultures, was analysed by gas chromatography/mass spectrometry (GC/MS) with headspace sampling by solid-phase micro-extraction (SPME; fibre DVB/CAR/PDMS, 2 cm; Sigma-Aldrich, part of Merck KGaA, Darmstadt, Germany), as described by [10] with minor modifications. Specifically, 1 mL of vinegar, 8.5 mL of pre-boiled deionized water, 1 g of (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), and 500 \(\mu\)L of a 1000 mg/L 1,4-dioxane solution as IS, were added in a 20 mL glass vial. The vial was then sealed with a silicone septum and was immersed in a water bath (40 \({}^{\circ}\)C) for 5 min with stirring (250 rpm, stir speed 5, Thermodyne-Nuova II). The SPME fibre was then exposed to the headspace above the liquid in the vial for 30 min. For desorption and chromatographic separation of the volatiles, the fibre was exposed in the injection port of the gas chromatograph (GC), in split mode (split ratio 1/10), at 240 \({}^{\circ}\)C for 5 min. The injection port carried an SPME liner (Sigma Aldrich) with 0.7 mm i.d. Before each analysis, the fibre was exposed in the injection port of another GC for 5 min at 250 \({}^{\circ}\)C, to remove any volatile residues. The GC instrument (GCMS-QP2010 Ultra, Shimadzu Inc., Japan) was equipped with a DB-Wax capillary column (30 m, 0.25 mm i.d., 0.25 \(\mu\)m film thickness, Agilent, Santa Clara, CA, USA), incubated at the following temperature program: 40 \({}^{\circ}\)C for 5 min, increased to 180 \({}^{\circ}\)C at a 5 \({}^{\circ}\)C/min rate, then increased to 240 \({}^{\circ}\)C at a 30 \({}^{\circ}\)C/min rate, and held at 240 \({}^{\circ}\)C for 5 min. The carrier gas was He at a linear velocity of 36 cm/sec. The mass spectrometer (MS) was operated in electron ionization mode, at an ionization energy of 70 eV and 4 at 0-300 m/z mass scan range. The source and interface temperatures were set at 200 \({}^{\circ}\)C and 240 \({}^{\circ}\)C, respectively.  
For the identification of volatiles, the following were compared: (i) Retention indices (RI) based on the homologous series of C8-C24 n-alkanes with those of available authentic compounds and those available in the NIST14 library (NIST, Gaithersburg, MD, USA), and (ii) MS data with those of reference compounds and data available in the NIST14 library. The identification was effected using the software GCMS Solution (ver. 4.30; Shimadzu), Amdis (ver. 2.72; NIST), and NIST MS Search (ver. 2.2; NIST), and the reliability of identification (RID) was set at 3 levels A, B, and C (A: RI and MS data agreement with those of authentic compounds assayed under the same conditions; B: agreement of RI (\(\Delta\)RI < 20) and MS data (similarity match > 900); C: at least \(\Delta\)RI < 20 or MS data similarity match > 800. The content of volatile compounds is presented as normalized peak areas %, estimated by determining the percentage of the area corresponding to an Amdis component relative to the sum of the areas of all components [10].
# Balance of Antioxidants vs. Oxidants in Perinatal Asphyxia
## 7 Therapeutic Strategies
### Nanomaterials  
The application of nanotechnology or techniques based on nanocarriers is rapidly growing as a promising tool for the detection and treatment of cerebral illnesses [157; 158]. Nanoparticles are carriers or delivery tools that enhance the bioavailability of therapeutics because of their enhanced solubility, reduced degradation by enzymes, decreased protein binding, and extended and targeted delivery at the injury site [159; 160]. Therapeutic medicines can be more effectively created to counteract excitotoxicity by utilizing the advantages of nanoparticle drug delivery and comprehending the biomolecular mechanisms behind neurological disease processes. Polymeric nanoparticles, liposomes, hydrogels, and dendrimers are just a few of the many nanoparticle platforms that are utilized to deliver medications to the brain [161]. Polymeric nanoparticles have demonstrated the ability to traverse both an intact and compromised blood-brain barrier, and they can offer controlled drug release, targeting capabilities, and sustained drug action by protecting against proteases [162]. To avoid steric clearance and non-specific binding, nanoparticles can be modified in terms of size, shape, flexibility, and surface charge to change pharmacokinetics and enhance brain accumulation [163; 164]. Densely polyethylene glycol-coatednanoparticles can move through the brain parenchyma by diffusion [165]. Among the drug-incorporating techniques are covalent conjugation to surface end groups and loading into lipophilic bilayers, cores, or nanoparticle matrices [166]. Nanoparticles can also be adorned with surface ligands or surfactants to enhance pharmacokinetics or cellular absorption [162].  
According to current evidence, the endogenous antioxidant enzyme SOD, which breaks down SOX into H\({}_{2}\)O\({}_{2}\) and water, is one of the most promising enzyme therapy possibilities. Reducing SOX levels at the expense of increasing H\({}_{2}\)O\({}_{2}\) levels has a clear therapeutic effect by lowering mitochondrial oxidative stress and fragmentation, even if SOD transforms SOX into H\({}_{2}\)O\({}_{2}\)[167]. Several of the following processes leading to neuronal death are mitigated by antioxidant intervention through the scavenging of ROS and injury-associated SOX. Numerous investigations have already demonstrated the therapeutic potential of different antioxidants as well as SOD-loaded nanoparticles or SOD-mimetic platforms. Though the suppression of excitotoxic damage appears promising, more research is necessary to determine the optimal therapeutic intervention point [157, 161].
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## Appendix A Proof of Cubic Convergence of the Developed Solvers  
In this section, we prove that the developed PF solution techniques present a cubic convergence rate. To this end, we use the Taylor series technique (e.g., see [27]). **Theorem A1**. : _Let \(\mathbf{g}\) be sufficiently differentiable at each point of an open neighborhood \(D\) of \(\mathbf{r}\in\mathbb{R}^{n}\); this is a solution of the system \(\mathbf{g}(\mathbf{x})\ =\ 0\). Let us suppose that \(\mathbf{g}(\mathbf{x})\) is continuous and nonsingular in \(\mathbf{x}\). Then, the developed method (23) converges to \(\mathbf{r}\) with order three._  
**Proof of Theorem A1. **_Taylor expansion of \(\mathbf{g}(\mathbf{x})\) and \(\mathbf{g}^{{}^{\prime}}(\mathbf{x})\) about \(\mathbf{r}\) yields:_  
\[\mathbf{g}\Big{(}\mathbf{x}^{(k)}\Big{)}\ =\ \mathbf{g}^{{}^{\prime}}(\mathbf{r})\Big{[} \mathbf{e}^{(k)}+\mathbf{C}_{2}\mathbf{e}^{(k)^{2}}+\mathbf{C}_{3}\mathbf{e}^{(k)^{3}}\Big{]}+\mathbf{ O}\Big{(}\mathbf{e}^{(k)^{4}}\Big{)}\] (24) \[\mathbf{g}^{{}^{\prime}}\Big{(}\mathbf{x}^{(k)}\Big{)}\ =\ \mathbf{g}^{{}^{\prime}}(\mathbf{r})\Big{[} \mathbf{I}+2\mathbf{C}_{2}\mathbf{e}^{(k)}+3\mathbf{C}_{3}\mathbf{e}^{(k)^{2}}\Big{]}+\mathbf{O}\Big{(} \mathbf{e}^{(k)^{3}}\Big{)}\] (25) _where \(\mathbf{e}^{(k)}\ =\ \mathbf{x}^{(k)}-\mathbf{r}\in\mathbb{R}^{n}\), \(\mathbf{e}^{i}\ =\ \underbrace{(\mathbf{e},\mathbf{e},\ldots,\mathbf{e})}_{i-times}\), \(\mathbf{C}_{j}\ =\ (1/j! )\Big{[}\mathbf{g}^{{}^{\prime}}(\mathbf{r})\Big{]}^{-1}\mathbf{g}^{(j)}(\mathbf{r})\in L _{i}(\mathbb{R}^{n},\mathbb{R}^{n})\),_  
\[\mathbf{g}^{(j)}\in L(\mathbb{R}^{n}\times\ldots\times\mathbb{R}^{n},\mathbb{R}^{n})\ \text{and}\ \Big{[}\mathbf{g}^{{}^{\prime}}(\mathbf{r})\Big{]}^{-1}\in L(\mathbb{R}^{n})\text{. Now, let us assume that:}\]  
\[\Big{[}\mathbf{g}^{{}^{\prime}}\Big{(}\mathbf{x}^{(k)}\Big{)}\Big{]}^{-1}\ =\ \Big{[}c_{1}\mathbf{I}+c_{2}\mathbf{e}^{(k)}+c_{3}\mathbf{e}^{(k)^{2}} \Big{]}\Big{[}\mathbf{g}^{{}^{\prime}}(\mathbf{r})\Big{]}^{-1}+\mathbf{O}\Big{(}\mathbf{e}^{( k)^{3}}\Big{)} \tag{26}\]  
_where \(\mathbf{c}^{\prime}\mathbf{s}\in\mathbb{R}\).
# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Use Cases of the TinyML
#### 2.7.2 Hand Gesture Recognition  
In the TinyML realm, gesture recognition is a particularly promising subject. Various approaches for applying gesture prediction to resource-constrained devices are being investigated. For example, hyper-dimensional computing is being examined for the application of electromyogram pattern recognition [129]. The prospect of gesture prediction with limb position awareness utilizing sensor fusion technology is studied in this work. To replicate dual-stage categorization, the accelerometer and EMG data are combined. Multiple models keep position-specific properties in superposition. When evaluated on a dataset comprising 8 limbs and 12 motions, it achieved 93.34% accuracy during model validation. The findings suggest that this activity uses 8 times less RAM. The combination of EMG sensors and accelerometer sensors for hand signal collection is shown in Figure 7. In [108], an ANN model was used to detect and categorize a time series of a sensor readings for a collection of motions (Forward, Backward, Select and Stop). To conduct the experiment, they used an ATMega4809 CPU, a composite eye camera, and a hand gesture sensor. The microcontroller featured clock processing speed of up to 20 M, a 48 KB of flash memory, and 6 KB of RAM. They produced synthetic data after recording different actions made by a complete hand, arm, or fingers at different distances. Models included Feed Forward Neural Networks (FFNN) and RNN. FFNN outperformed RNN, according to the results of the model. With the ReLu activation function, FFNN had an accuracy of 84% whereas RNN had an accuracy of 83%. The 32 KB flash memory and 2 KB RAM of the ATMega328P microcontroller were verified to be adequate for achieving a frame rate of 40 Hz using both methods.  
Capacitive sensing has been addressed for gesture prediction for TinyML configurations [105]. The embedded prototype created for this study contains four capacitive sensing electrodes and may be worn on the wrist. It has a classifier accuracy of 96.4% and uses just one hidden layer. An ARM Cortex-M4 MCU with 256 KB of RAM and 1 MB of flash is used in the design. Without quantization, the released embedded prototype in float32 mode is just 29.6 KB in size. The inference process requires 26.4 mW of electricity and takes only 12 ms. In a different study, it was discovered that the first finger was equipped with an NRF52 CPU and a 32 Hz accelerometer. From the TensorFlow Lite Micro library, it selects a multilevel long short-term memory (LSTM) model with 2.8 MB of on-disk capacity. With a measurement accuracy of 95.5% over 10 distinct movements, it is trustworthy.