esa-sceva/satcom-chunk-collection huggingface dataset:
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications
### Coherency of the GNSS-R Signal  
The first model of GNSS-R signals developed in 2000 [2] only included the incoherent scattering because of the Kirchhoff approximation method that only assumes strong diffuse (non-coherent) scattering of very rough surfaces. Later, in 2018, the same authors improved the model by adding a coherent scattered component of the GNSS signals [3] generalizing the bistatic radar equation based on the assumption that the roughness distribution is spatially homogeneous, and that this leads to a more natural transition from partially coherent scattering to incoherent and diffuse scattering. Other studies, such as [21], have developed methodologies to untangle the incoherent and coherent components of a GNSS-R signal, leading to novel applications. This is contrast to traditional monostatic radars, where the return is dominated by incoherent back-scattering, bistatic radars (e.g., GNSS-R) with forward-scattered signals present both coherent and incoherent components. The methodology presented in [21] was used to separate the direct signal leakage in the reflected signal channel in an airborne dual-band (L1/E1 and L5/E5a) experiment and successfully applied to different GNSS constellations (GPS and Galileo). The authors in [108] were capable of untangling the coherent and incoherent components, providing a better signal calibration accuracy and a higher resolution. In addition, the use of the coherent component allowed authors to establish a more precise location of the specular reflection point by determining the maximum peak of this coherent component rather than the point of maximum derivative of the incoherent one. The authors in [109] conducted an analysis of the development of the response of a GNSS reflected signal to a step function because of surface transition. The authors computed the spatial resolution of GNSS-R signals and then validated their assessments at two GPS bands, L1 and L5, from airborne measurements. The study proved that when a land-to-calm ocean or lake transition occurs, the reflectivity shows \"ripples\" during transition, which is the same as in the response of a step function in a common electrical circuit.
# Phase Separation Study on the Storage of Technically Specification Natural Rubber Modified Bitumen
###### Abstract  
Overloading and climate change are often problems in pavement structures. For this reason, hard asphalt binders have high softening points, are elastic, and have good adhesion, which is needed to improve pavement performance. Asphalt binder performance can be enhanced by adding additives such as natural rubber or natural-rubber-modified asphalt. However, natural-rubber-modified asphalt shows poor storage stability problems. This is due to differences in density and viscosity between the constituent components of natural-rubber-modified asphalt. This study examines the phase separation mechanism in technically specified natural rubber (TSNR) modified asphalt. Prediction of the optimum storage length of modified asphalt before phase separation occurs, using a combined incompressible Navier-Stokes and phase field model and carried out with COMSOL Multiphysics software version 5.5. Experimental validation was conducted at TSNR levels of 8, 10, and 12% at 160 \({}^{\circ}\)C for 48 h, with and without sulfur. The simulation showed that the asphalt modified with TSNR experienced phase separation after 12 h of storage at 160 \({}^{\circ}\)C under conditions without stirring. This aligns with the experimental results, which showed phase separation at 160 \({}^{\circ}\)C after 48 h. Adding sulfur additives did not have much effect on improving storage stability. The combined incompressible Navier-Stokes and phase field model accurately describes the phase separation in TSNR-modified asphalt. The results of this research recommend that the industry store natural-rubber-modified asphalt in a constantly stirred condition to prevent phase separation of modified asphalt. In addition, the results of this research help the industry predict or increase the homogeneity of polymer-modified asphalt production and save time and costs.  
Keywords:COMSOL multiphysics; natural rubber modified asphalt; phase separation; softening point; storage stability +
Footnote †: journal: Applied Physics
# Biogas Production Enhancement through Chicken Manure Co-Digestion with Pig Fat
## 2 Materials and Methods
### Laboratory Equipment and Instruments  
The biogas production experiment took place in the continuous feeding BTP-2 laboratory biogas pilot reactor (Umwelt- und Ingenieurtechnik GmbH, Dresden, Germany) (Figure 1). The airtight reactor construction fully ensured anaerobic conditions, and the heating controller ensured the reactor operation under the mesophilic temperature of 37.0 \(\pm\) 0.5 \({}^{\circ}\)C. Entire biogas production lasted 90 days, including a ten-day run-up of the digestion process. A similar run-up duration was observed in the study [29], with semi-continuous anaerobic co-digestion of food waste with chicken manure. The semi-automated laboratory bioreactor system consisted of a 15-L glass vertical reactor (a reactional volume of 14 L) with electrical heating, an electric mixer, a biogas volume meter, and a biogas storage tank. The mixing cycle and temperature of the biogas reactor substrate were controlled automatically by the heating controller. The temperature and pH of the substrate and biogas yield data were recorded by a reactor programmable logic controller and stored in the internal database. The produced biogas was continuously counted with the RITTER TG 0.5 PLASTIC (Dr.-Ing. RITTER Apparatebau GmbH & Co. KG, Bochum, Germany, measurement accuracy \(\pm\)0.5%) drum-type volumetric biogas flowmeter and continuously collected in a 25 L RESTEK (Restek Corporation, Bellefonte, PA, USA) bag of 25 L volume.  
The collected biogas once a day was manually disconnected from the reactor and analysed with an AwirFlex biogas analyser (Awite Bioenergie GmbH, Langenbach, Germany, measurement: CH\({}_{4}\) 0-100%, \(\pm\)0.2%; CO\({}_{2}\) 0-100%, \(\pm\)0.2%; O\({}_{2}\) 0-25%, \(\pm\)0.2%; H\({}_{2}\)S 0-10,000 ppm, \(\pm\)5 ppm; H\({}_{2}\) 0-40,000 ppm, \(\pm\)5 ppm). The spare bag was instead connected. Samples of the reactor feeding materials were weighted manually on electronic scales KERN EG4200-2 NM (Kern & Shon GmbH, Balingen, Germany, accuracy \(\pm\)0.02%). The pH of the raw material and the digested substrate was determined during each loading with a Hanna PH213 m (Hanna Instruments Ltd., Woonsocket, RI, USA, measurement accuracy \(\pm\)0.01). Manure, fat,  
\begin{table}
\begin{tabular}{l l l l} \hline
**Experiment** & **FM Description** & **OLR, kg VS-(m\({}^{3}\cdot\)Day)\({}^{-1}\)** & **FM Mass, g** \\ \hline OLR3.0 & RM + water & 3.00 & 399.4 \\ OLR3.5 & RM + water + fat 7 g & 3.50 & 406.4 \\ OLR4.0 & RM + water + fat 14 g & 4.00 & 413.4 \\ OLR4.5 & RM + water + fat 21 g & 4.50 & 420.4 \\ \hline \end{tabular}
\end{table}
Table 1: Feeding material composition.  
and digestate samples were sent to the accredited Agrochemical Research Laboratory of the Lithuanian Research Centre for Agriculture and Forestry for detailed composition analysis.
# Evaluation of the Mid-Latitude Ionospheric trough Using GRACE Data
## 3 Results and Discussion
### Dependence on Solar Wind; Flow Pressure; and IMF Bx, Bz, and By Components  
We examined the correlation between the main trough parameters and solar wind plasma speed (see Figure 15); flow pressure (see Figure 16); and IMF Bx, Bz, and By components (see Figure 17). The dependence on solar wind and trough minimum location shows a negative correlation in the northern hemisphere. A negative correlation between the MIT latitude and solar wind speed was also reported by [PERSON] et al. (2011) [14]. The trough width decreases with increased solar wind speed, and the southern hemisphere shows no visible correlation with solar wind.  
Figure 16: Flow pressure dependence of the trough minimum position, trough width, and trough depth for the northern and southern hemispheres.  
Figure 14: Diurnal variation in trough width and depth for the northern and southern hemispheres as a function of the magnetic local time.  
Figure 15: Solar speed dependence of the trough minimum position, trough width, and trough depth for the northern and southern hemispheres.  
Correlation with flow pressure is presented in Figure 16. With increasing flow pressure, the trough moves slightly to lower latitudes and becomes narrower and deeper in both hemispheres.  
Figure 17 shows the variation in the trough minimum position relative to the Bx, By, and Bz components. The variation in the trough parameters is very low, and all the IMF components show a similar trend.
_Applied_  
_sciences_  
_Article_  
**Noise Analysis in Pre-Amplifier Circuits Associated to Highly Sensitive Optically-Pumped Magnetometers for Geomagnetic Applications**  
**[PERSON] 1,2,3,*, [PERSON] 1,2,3, [PERSON] 1,3, [PERSON] 1,3 and [PERSON] 1,3**  
1 Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (Q.Z.); [EMAIL_ADDRESS] (G.F.)  
2 School of Electronic, Electrical and Communication Engineering, University of the Chinese Academy of Sciences, Beijing 100049, China  
3 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China  
*Correspondence: [EMAIL_ADDRESS]  
Received: 5 September 2020; Accepted: 12 October 2020; Published: 15 October 2020  
**Featured Application: This paper analyzes the noise characteristics of photoelectric detection circuit in frequency domain for a cesium optically pumped magnetometer. It can guide the low-noise circuit design. The influences of magnetometer due to the radio frequency (RF) power, the incident light power and the cell temperature are also investigated and improved. The optimization process is also applicable for the other types of optically pumped magnetometers.**  
**Abstract: This paper analyzes the noise sources in photoelectric detection circuits with several low-noise operational amplifiers cores. The fabricated circuits are low-noise pre-amplifiers that are used for optically pumped magnetometers. In the proposed circuits, the noise levels of equivalent output voltage are calculated, and the results are in accordance with measurements. With a cooperation of several operational amplifiers, we select LT1028 from linear technologies as the core for our detection circuit, which has an output signal-to-noise ratio of more than \(2\times 10^{5}\) up to the frequency of 100 kHz. By analyzing the individual noise sources in the detection circuit, the dominant noise source is confirmed as the photocurrent shot noise below 200 kHz. Beyond this frequency, the voltage noise source in the operational amplifier dominates. Besides, the lamp power, the radio frequency (RF) power, the temperature variations, and their influences on the sensitivity are studied and optimized. Finally, an optically pumped magnetometer with cesium head is established, showing an intrinsic sensitivity of 85 fT/\(\surd\)Hz. This sensitivity is realized under a geomagnetic magnetic field strength of 53 \(\mu\)T.**  
**Keywords: noise analysis; low-noise detection circuit; optically pumped magnetometer; optimization**
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 1 Introduction
### Contributions  
This article outlines several significant contributions in the field of cybersecurity and malware detection. These contributions are as follows:  
1. **Categorization and Comparison of Malware Families:** This paper provides a comprehensive categorization of different types of malware. It also offers a detailed comparison of major malware families, likely highlighting their characteristics, propagation methods, and impact on systems. This categorization and comparison aids in understanding the diverse nature of malware and its potential threats.
2. **Intruder's Perspective and Incident Response:** This article delves into the mindset of malicious actors when attempting to compromise a system. This understanding of the intruder's perspective is used to introduce a defense mechanism known as the incident response process. Incident response involves predefined strategies and actions to be taken when a security breach occurs, helping to minimize the impact of ransomware attacks or other security incidents.
3. **Analysis of Malware Detection Systems:** This article systematically reviews various malware detection systems. This involves an analysis of current intrusion detection techniques and tools. By studying these detection systems, this paper aims to provide insights into the effectiveness, limitations, and advancements in identifying and countering malware threats.
4. **AI-Based Solutions for Malware Detection:** This article presents detailed solutions for malware detection that leverage Artificial Intelligence (AI) technologies. These AI-based solutions likely utilize machine learning algorithms to identify patterns and anomalies in system behavior, aiding in the timely detection of malware. This approach is expected to enhance the accuracy and efficiency of malware detection.
5. **Case Studies of Notable Incidents:** This paper includes case studies of specific cybersecurity incidents to illustrate real-world implications and challenges. The examples mentioned, such as the Facebook-Cambridge Analytica data breach controversy and the Cisco system intrusion by the Yanlluowang Ransomware Gang, offer insights into the tactics used by threat actors and the consequences of such attacks. These case studies help contextualize the importance of effective cybersecurity measures.
## 1 Introduction
[3] uses an exponential equation to describe the rising temperature caused by incident irradiance and the decrease in temperature caused by the on-site wind factor. Other researchers refer to using nominal operating cell temperature (NOCT), which overlaps between implicit and explicit methods. The advantage of using NOCT is that this parameter is usually supplied by module manufacturers, and the implementation is simple. The disadvantage of this method is that the NOCT temperature is defined under specific meteorological conditions, which are difficult to meet under real conditions. Moreover, some studies showed that NOCT is not constant and varies by month, season, and location [9; 17]. All those models listed above are able to predict the temperature values of the module back surface of the PV cells for both instant values and hourly time steps. However, [PERSON] et al. [9] found that using hourly input parameters gained a higher accuracy of the prediction compared to using instant values of those parameters. Moreover, previous studies indicated that it is difficult to find a model, which can satisfy all PV module technologies [9; 18; 19], while [PERSON] et al. [13] showed that the accuracy of a thermal model is also affected by the on-site conditions. Thus, one of the objects of this study is to improve the possibility of calculating PV module temperature by taking into account the effect of its own thermal inertia, which strongly impacts the changing of the module temperature, especially for locations where the solar irradiance and wind speed fluctuate strongly. In this paper, two new module temperature models were proposed to predict the back-surface temperature of a PV module under outdoor operating conditions. The assessment considers parameters associated with the installation site, such as solar irradiance, ambient temperature, wind speed, mounting configuration, and interval recording, along with the PV cell material. The first model was achieved by modifying the method described by [PERSON], while the second model was based on the idea of the relationship between module temperature and solar irradiation intensity on the PV surface. A comparison between the proposed models and previous models existing in the literature was implemented to determine their reliability.
# Open-Pit Map: An HD Map Data Model for Open-Pit Mines
## 5 Application
### Intelligent Scheduling System  
The Intelligent scheduling system is primarily used in mobile devices, automotive industrial panels, vehicles, scheduling centers, and the service backend, in accordance with mining scenarios. The system's functional components are shown in Figure 9.  
The intelligent scheduling system optimizes the task scheduling and travel paths of shipping equipment based on mining grade requirements and ore blending results. This optimization is aimed at enhancing shipping efficiency, thereby maximizing production efficiency. The system achieves the autonomous driving of haul trucks to designated loading and unloading points, ensuring proper loading and unloading postures. During the coexistence of both human-driven and autonomous driving phases, it supports the coordination of human and autonomous operations and ensures safety in operations. The scheduling visual interface is shown in Figure 10.  
Figure 9: Intelligent scheduling system interface display.
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion
### Analysis A
#### 4.1.4 Results of Analysis A  
After training the model, the accuracy of the model was measured separately for the training datasets (total 207), and validation datasets (total 20) to check for overfitting of the model. The results showed that the accuracies for coal mines for training datasets and validation datasets were 97% and 83%, respectively, and were 97% and 75% for other type mines, respectively, as presented in Table 2. Logically, because the training was performed using the training datasets, their accuracy was higher than the accuracy for the validation datasets. It is expected that, once additional training datasets are added, in the future, the accuracy for validation will also improve, reducing the overfitting on the training dataset.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 3 Improved Gray Wolf Optimization Algorithm
### Evolutionary Population Dynamics  
Evolutionary population dynamics (EPD) is inspired by self-organizing critical theory (SOC), which posits that dynamic systems can adaptively adjust to maintain an efficient and stable dynamic equilibrium. EPD takes advantage of this idea by eliminating less-adapted individuals and repositioning them to enhance population diversity [35].  
During iterations of the gray wolf algorithm, the population gradually converges toward the top three individuals, which aids local exploration but also reduces overall population diversity, increasing the risk of becoming trapped in local optima. In later stages, a decline in population diversity may negatively impact the global optimization performance. In this paper, EPD is used to enhance the diversity of the population.  
The specific implementation of EPD is divided into the following two steps. Firstly, in each iteration, the bottom 20% of individuals with the lowest fitness values are removed. Then, the removed individuals are regenerated in four ways. The first three methods involve generating new individuals near the elite individuals, while the fourth method generates individuals randomly; each of these four methods is applied with a probability of 25%. The mathematical formula is as follows:  
\[X(t+1)=\begin{cases}X_{\alpha}-r_{4}(X_{\alpha}-l_{b}),&r_{3}\leq 0.5\\ X_{\alpha}+r_{4}(u_{b}-X_{\alpha}),&r_{3}>0.5\end{cases} \tag{14}\]  
\[X(t+1)=\begin{cases}X_{\beta}-r_{4}(X_{\beta}-l_{b}),&r_{3}\leq 0.5\\ X_{\beta}+r_{4}(u_{b}-X_{\beta}),&r_{3}>0.5\end{cases} \tag{15}\]  
\[X(t+1)=\begin{cases}X_{\delta}-r_{4}(X_{\beta}-l_{b}),&r_{3}\leq 0.5\\ X_{\delta}+r_{4}(u_{b}-X_{\beta}),&r_{3}>0.5\end{cases} \tag{16}\]  
\[X(t+1)=l_{b}+r_{5}(u_{b}-l_{b}) \tag{17}\]  
where \(r_{3}\) and \(r_{5}\) are random numbers between \([0,1]\), \(r_{4}\) is a random number between \([0,0.5]\), and \(u_{b}\) and \(l_{b}\) are the upper and lower bounds of the search space, respectively.  
EPD updates the less-fit wolves in various ways, including generating new individuals around elite wolves to improve local search ability and employing random generation to improve population diversity. This mechanism enhances the algorithm's ability to escape local optima while maintaining its optimization capability.  
Figure 1: The improved nonlinear convergence factor \(a\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
1G First-Generation  
1x EV-DO 1x Evolution Data Optimized  
2G Second-Generation  
3D Three-Dimensional  
3G Third-Generation  
3 GPP Third-Generation Partnership Project  
4D Four-Dimensional  
4G Fourth-Generation  
5G Fifth-Generation  
AAS Active Antenna Systems  
AAU Active Antenna Unit  
ABF Analog BF  
ADC Analog-to-Digital Converter  
ADSL Asymmetric DSL  
AI Artificial Intelligence  
AMPS Advanced Mobile Phone System  
ANSI American National Standards Institute  
AP Access Point  
APON ATM PON  
AT&T American Telephone and Telegraph  
ATM Asynchronous Transfer Mode  
B5G Beyond-5G  
BB Broadband  
BBU Baseband Unit  
BF Beamforming  
BFWA Broadband FWA  
BPL Broadband-over Power Lines  
BPON Broadband PON  
BS Base Station  
BT Beam Tracing  
C&M Control and Management  
CA Carrier Aggregation  
CableLabs  
Cable Television Laboratories  
CATV Community Antenna TV  
CBR Constant Bit Rate  
CBRS Citizens Broadband Radio Service  
CO Central Office  
COTS Commercial off-the-Shelf  
CP Cyclic Prefix  
CPE Customer Premises Equipment  
CPRI Common Public Radio Interface  
C-RAN Cloud RAN  
CSPR CW-to-signal power ratio  
CU Central Unit  
CW Continuous-Wave  
CWDM Coarse WDM  
DAC Digital-to-Analog Converter  
D-AMPS Digital AMPS  
DBA Dynamic Bandwidth Allocation  
DBF Digital BF  
DBS Direct Broadcast Satellite  
DD Direct Detection  
DEMUX De-multiplexing  
DL Downlink  
DOCSIS Data Over Cable Service Interface SpecificationHSDPA High-Speed Downlink Packet Access HSPA High-Speed Packet Access HSPA+ HSPA evolution HSUPA High-Speed Uplink Packet Access I/Q In-Phase and Quadrature Components IEEE Institute of Electrical and Electronics Engineers IoT Internet of Things IP Internet Protocol IPTV Internet Protocol TV IPv6 IP version 6 ISD Inter-Site Distance ITU International Telecommunication Union ITU-T ITU Telecommunication Standardization Sector JTACS Japanese TACS L1 Layer 1--physical layer L2 Layer 2--data link layer L3 Layer 3--network layer LAN Local Area Network LEO Low Earth Orbit LLS Low Layer Split LMDS Local Multipoint Delivery Service LoS Line-of-Sight Low-e Low-emissivity LTE Long-Term Evolution LTE-A LTE-Advanced M2M Machine-to-Machine MAC Media Access Control MBB Mobile Broadband MBH Mobile Backhaul MCNS Multimedia Cable Network System Partners Limited MFH Mobile Fronthaul MIMO Multiple-Input Multiple-Output mloT massive IoT MMDS Multichannel Multipoint Distribution Service mMTC massive Machine-Type Communications mm-wave Millimeter Wave MNO Mobile Network Operator MU Multi-User MU-MIMO multi-user MIMO MUX Multiplexing MVNO Mobile Virtual Network Operator NB Narrowband Netsli Network Slicing Netz-C C450-20 Network NEXT Near-End-Crosstalk NFV Network Functions Virtualization NGFI Next-Generation Fronthaul Interface NG-PON2 Next-Generation PON2 nLoS near LoS non-LoS NM Nordic Mobile Telephony NR New Radio NRZ Non-Return-to-Zero OAM Operations, Administration and Maintenance OAN Optical Access Network OBSAI Open Base Station Architecture Initiative OCDM-PON Optical Code Division Multiplexed PON