esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation
## 5 Performance Analysis of Amplitude Comparison Monopulse
\begin{table}
\begin{tabular}{c c c} \hline
**Description** & **Value** & **Units** \\ \hline Monte Carlo simulation & 100,000 & times \\ The mean of the noise received at Antenna A, B, C, D & 0 & voltage/meter \\ The standard deviation of the noise received at Antenna A, B, C, D & \(10^{-0.6},10^{-0.5},\cdots,10^{0}\) & degrees \\ Antenna 3 dB beamwidth & 3 & degrees \\ Track axis gain & 1 & voltage \\ Antenna squint angle & 1 & degrees \\ Gain of received signal & 1 & voltage \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters for simulation. \(\text{'approxi}=2\)'. Therefore, the analytically derived expression 'Analytic \(E\left[\left(\tilde{\theta}^{\prime(\text{approxi}=1)}-\tilde{\theta}_{\text{T}} \right)^{2}\right]\)' can be used to see how 'Simulation \(E\left[\left(\tilde{\theta}^{\prime(\text{approxi}=1)}-\tilde{\theta}_{\text{T} }\right)^{2}\right]\)' is dependent on the standard deviation of an additive noise. Figure 4: MSEs of target elevation estimate for ACM radar. Figure 3: Mean square errors (MSEs) of target azimuth estimate for ACM radar. The results in Figures 3 and 4 show that the MSEs of the angle estimate increase with the increase of SNR in the four antennas. Also, it is clear that the results with the superscript '\(\text{approxi}=2\)' are much closer than the results with the superscript '\(\text{approxi}=1\)' to the estimates without approximations. Note that the proposed scheme is not a new DOA estimation algorithm with better MSE performance than previous existing DOA estimation algorithms. The proposed scheme is on how MSE of previously existing amplitude comparison monopulse DOA algorithm can be obtained analytically with much less computational complexity than the Monte Carlo simulation-based MSE. Therefore, we cannot quantitatively state how much improvement in estimation accuracy in terms of MSE can be achieved by adopting the proposed scheme in this paper. Instead, to quantify the improvement in the computational cost, computational complexity in execution time is illustrated both for analytically derived MSE and for the Monte Carlo simulation-based MSE. Note that the computational complexity is independent of the standard deviation. Therefore, the results for an arbitrary standard deviation of 0.1 degree is adopted for illustration. As, in getting the Monte Carlo simulation-based MSE, the computational complexity is nearly proportional to the number of repetitions, the results for number of repetitions of 10, 100, 1000, 100,000 and 100,000 are shown. It is clearly shown in Figure 5 that the computational complexity for analytically derived MSE is much less than that for the Monte Carlo simulation-based MSE with number of repetitions = 100,000. Note that the execution time for analytically derived MSE is independent of the number of repetitions, which is why the execution time of the analytically derived MSE is flat with respect to the number of repetitions.
## 4 Derive Assessment Templates
### Allocation Requirements Based on Technology Reliance
#### 4.2.2 Positioning  
Positioning is a capability required for high levels of automation. It involves determining the position of the ego vehicle (the vehicle under test) and estimating and trackingthe position of objects in its vicinity within the traffic system. Different applications within the scope of connected ADSs have varying positioning needs, with the main aspects being absolute and relative positioning. The accuracy, precision, refresh rate, and integrity are subattributes associated with these aspects.  
Global Navigation Satellite System (GNSS)-based positioning and High-Definition (HD) maps can be utilised for absolute positioning. HD maps provide relevant information, such as traffic signs, beams, or poles, which can be trust anchors to determine the vehicle's position without active connections. V2X communications can also improve positioning by transferring information, provided a mechanism exists to establish sufficient trust in the received data. In the realm of GNSS technologies, ongoing standardisation efforts are spearheaded by organisations like ETSI, with test-equipment vendors actively enhancing GNSS testing capabilities. Furthermore, the interrelation of cybersecurity with GNSS positioning in Intelligent Transportation Systems (ITSs) is detailed in EN 16803-1 [27]. The European COST Action SaPPART [28] established standardised methodologies, enhancing integrity metrics, PVT error models, and positioning solutions for future ITSs. This involved validating positioning terminals and analysing GNSS receivers under the guidance of EN 16803-1.
## 3 Spectrum Management  
The emergence of massive numbers of IoRT devices will form part of the 5G networks ecosystem despite the limited spectrum. In a satellite network, it remains unclear what the best spectrum management solution for this scenario is. In this paper, two different spectrum management schemes are investigated in order to determine the best spectrum management scheme that will ensure good network performance for IoRT devices without also compromising human-to-human communications.
## 2 Materials and Methods
### Molecular Dynamics  
Molecular dynamics (MD) simulations were performed to investigate the dynamic of ligand interaction with the surface pocket at the protein-protein interface. The best scored binding poses calculated using docking simulations were used as inputs. MD simulations were performed using GROMACS (version 5.1.4) [30] with the CHARMM27 all-atom force field parameters support [31]. Ligands were processed with the CHARMM27 all-atom force field using the SwissParam web server ([[http://www.swissparam.ch](http://www.swissparam.ch)]([http://www.swissparam.ch](http://www.swissparam.ch))). The MD conditions previously described were used in each simulation (50 ns) [18]. Briefly, input structures were solvated with SPCE waters in a cubic periodic boundary condition, and counter ions (Na\({}^{+}\) and Cl\({}^{-}\)) were added to neutralize the system. Prior to MD simulation,the systems were energetically minimized to avoid steric clashes and to correct improper geometries using the steepest descent algorithm with a maximum of 5000 steps. Afterwards, all the systems underwent isothermal (300 K, coupling time 2 psec) and isobaric (1 bar, coupling time 2 psec) 100 psec simulations before running 50 nsec simulations (300 K with a coupling time of 0.1 psec and 1 bar with a coupling time of 2.0 psec). The number of long-lasting and short-range contacts between the viral protein and the human ACE2 was measured through g_contacts software (Version 1.0, CPC Program Library, Queen's University, Belfast, N. Ireland, 2013) [32] using the \"--resndx\" option to calculate the non-polar contributions to the inter-molecular interactions at an amino acid level, as previously shown [33; 34]. The cut-off frequency for long-lasting contacts was set at 0.25 to identify those that occurred, in at least, 25% of the simulations.
# Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
###### Abstract  
A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.  
indoor tracking; particle filter; channel state information; WiFi fingerprinting 2021
# Development of Traffic Scheduling Based on TSN in Smart Substation Devices
## 2 Smart Substation Delay Analysis and TSN Technology Analysis
### TSN QBV Technical Analysis
The IED receives commands from the station control layer, measures the process layer devices, makes control decisions, and transmits the collected data to other IEDs or sends the data to the supervisory control and data acquisition system configured in the station control layer for further processing. All the traffic converges in the IED's NIC, and the IED's TSN NIC must have the capability of traffic shaping, enabling it to send messages at specific hardware-timed intervals. This ensures an enhanced guarantee of the end-to-end delay and jitter of time-sensitive flow. In this paper, we propose a TSN design scheme for end-side devices of a smart substation to enable devices to have the capability to send messages at specific intervals.
## 2 Structural System
### Blind-Bolted Connection between Modular Units  
The details of the module-to-module connection proposed in this study are illustrated in Figure 2. In order to create an effective connection between the four modules, shown in the figure, a steel connection plate is added and connected by a number of blind and high-tension (H/T) bolts. The blind bolts are utilized in the portion of the connection plate attached to the HSS column members, which allows only limited access inside of the section. In contrast, the normal H/T bolts can be used in the portion attached to the C-shaped steel floor beam members. Totally, ten blind bolts and eight H/T bolts are required for this connection, as shown in Figure 2b.  
Figure 3 illustrates the details and fastening process of the blind bolts utilized in this study. The overall shape of the blind bolt and its main components are shown in Figure 4. The fastening process of the blind bolts is illustrated in Figure 3a,b. If a sufficient level of axial force is applied by a fastening tool after inserting the blind bolt into the hole, plastic deformation occurs in the sleeve, and the highly deformed portion of the sleeve forms a nut that can fix the main body to the connection  
Figure 1: Modular unit composition.  
Figure 2: Module-to-module connection details.  
plate. Then if the axial force reaches a limit value determined by the target torque, the pin-tail of the bolt is detached from its main body and its secure fastening can be guaranteed.
# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
### Extracellular Vesicles (EVs)
#### 1.3.2 EVs-Recipient Cell Interaction  
The particular compositions of EV surface markers of different natures and structures (protein, lipid, etc.) are closely related to their original cell, and this makes EVs capable of specific interactions with recipient cells. In the presence of surface receptors and/or internalization, with the release of their cargo contents, EVs can influence the recipient cell's physiological or pathological condition. Exosomes can interact with target cells through different mechanisms: interactions between proteins exposed by exosomes and the receptors of the target cell, activating them; modifications to exosomal membrane proteins  
Figure 2: Generic EV futures and cargo. The most common EV tetraspannins are Motility-Related Protein-1 (CD9), human leucocyte surface antigen (CD53), Cluster of Differentiation 36 (CD36), Cluster of Differentiation 81 (CD81), and Cluster of Differentiation 82 (CD82). Typical cytosolic proteins are tumor susceptibility gene 101 (TSG 101), ALG-2-interacting protein X (ALIX), annexins (ANXAS), C-X-C chemokine receptor type 4 (CXCR4), and epithelial cell adhesion molecule (EPCAM). Abbreviations: Major Histocompatibility Complex (MCH); heat shock protein 70 (Hsp70); heat shock protein 90 (Hsp90); Endosomal Sorting Complexes Required for Transport (ESCRT).  
whose fragments can act as ligands for cell surface receptors; and the fusion of exosomes with the target cell, resulting in the release of their contents.  
Transmembrane proteins or adhesion molecules (CAM), integrins (ITG), tetraspanins (CD9, CD63, CD81) [22], growth factor receptors, heterotrimeric G proteins (GNA), and phosphatidylinositol-serine-binding MFGE8/lactadherin are able to mediate the interaction between the recipient cell and the extracellular vesicle, resulting in cargo release. Furthermore, cytosolic binding membrane receptor proteins, such as membrane-binding proteins (TSG101, annexins, Rabs), proteins responsible for signal transduction or scaffold proteins (syntenin), and extracellular proteins binding specifically or nonspecifically to membranes, such as fibronectin, acetylcholinesterase, collagen, and soluble secreted proteins (metalloproteinases, cytokines, growth factors), are implicated in EV uptake [23; 24; 25; 26]. The mentioned EV uptake strategies are reported in Figure 3, such as EVs' important role as diagnostic and prognostic biomarkers and their implication in therapeutic approaches and clinical applications (Figure 3).
## 3 VVCM Evaluation
### Influence of VMA on the Mechanical Properties of LSAM-50 Asphalt Mixture and Design Criteria  
#### 4.3.1 VMA Influencing Factors  
The VMA is defined as the percentage of the volume, excluding the aggregate and the absorbed asphalt, in relation to the total volume of the specimen. During the design and construction phases, many factors can influence VMA, such as compaction processes, properties of aggregates, aggregate gradation, and the asphalt-aggregate ratio. The curve illustrating the impact of the asphalt-aggregate ratio on VMA can be seen in Figure 10.  
As illustrated in Figure 10, the VMA of the asphalt mixture first decreases and then increases with the rising asphalt-aggregate ratio. This trend can be attributed to the following: At lower asphalt-aggregate ratios, the lubricating effect of the asphalt on minerals is limited, amplifying the friction between mineral particles and making the mixture challenging to compact, leading to a larger VMA. As this ratio escalates, the asphalt's filling and lubricating effects become more pronounced, causing a decline in VMA. Upon reaching the optimal asphalt-aggregate ratio, the VMA reaches its minimum. However, further increases in this ratio boost the proportion of free asphalt, impeding the closeness of mineral particles and subsequently elevating the VMA of the mixtures.
## 2 Modeling of Electro-Hydraulic Servo System for Valve-Controlled Cylinder
### Introduction of Electro-Hydraulic Servo Position System  
Hydraulic systems can be divided into two main categories: one is hydraulic transmission systems, and the other is hydraulic control systems [30]. Hydraulic transmission systems are mainly used to transmit power, and to transmit information as a supplement. The basic task is driving and speed regulation. Hydraulic control systems are mainly used to transmit information, and to transmit power as a supplement. The main task is to enable the controlled quantity to automatically, stably, quickly and accurately track the input command changes. This paper focuses on the hydraulic automatic gauge control (AGC) system for rolling mills whose main application is the electro-hydraulic position servo system, which is a hydraulic control system with servo components as the control core. The electro-hydraulic position servo system is the main application of AGC, and it has two major subsystems: energy subsystems and servo subsystems [31]. The customary term electro-hydraulic position servo system mainly denotes servo subsystems, which are generally composed of a displacement sensor and input command, controller, servo valve, hydraulic cylinder, load and other components, and its configuration is shown in Figure 1.  
A system of a four-way slide valve-controlled asymmetric cylinder is shown in Figure 2. We assume that the valve is matched symmetrically, the flow at the valve port is turbulent, the supply pressure is constant, and the temperature and density are constant; the dynamics and losses of the pipeline are not considered. At the same time, considering the nonlinearity of flow-pressure at the servo valve port, the nonlinearity of the saturation characteristics of the servo valve, the nonlinearity of the internal leakage of the hydraulic cylinder and the time-varying characteristics of the liquid volume of the two chambers, as well as the nonlinearity of the friction characteristics, the mathematical model of the system is established as follows [32; 33; 34; 35; 36].
## 4 Communication Architecture
### Modeling of Intra-Level Communication Architecture
#### 4.1.4 Sink  
The sink performs a second level of data aggregation (before the data leaves the WSN home automation network). As described in Figure 8. In state 1, the sink is waiting for a wake-up message from its predecessors on the lower levels (Coordinator or Beacon node). When it receives data, state 2 is activated. In this state, it sends the appropriate acknowledgments (_msg-ok_), and state 3 for alarm management takes place. At this stage, the sink node makes the decision whether or not to send the received data (e.g., alarms) via external communication systems to the monitoring center (hospital, doctors, etc.). Depending on the selection, either state 4 or state 1 takes place. An important detail must be mentioned for level 2 sensors and the sink: the _msg-ok_ expectation is not shown in the state-transition diagram. We assume that acknowledgments for data and wake-up messages are built into the model.
## 2 Related Work
DSAODV uses geographical location to limit the area of RREQ's propagation. This article also introduces the method of obtaining the location of nodes. However, this protocol only limits the forwarding area, and the forwarding node will still adopt the flooding protocol. In addition, DSAODV takes more account of the node density of the source node. The reference [22] suggests an improved flooding algorithm applied to AODV routing protocol (NNRR) that makes use of the node's position to rebroadcast the RREQ packet. The area of route discovery is limited by having four nominated neighbors to rebroadcast RREQ using the concept of expected zone and requested zone. And the source node entirely specifies a node in the forwarding area to participate in forwarding. However, every node needs to share its information with its direct neighbors through the hello message mechanism. This will occupy more communication bandwidth and cause packet collisions. It is difficult to implement in underwater acoustic environment. [PERSON] proposed the GAODV that discovers a route in a unicast manner using the locations of the RREQ sender and the destination. The GAODV can significantly improve  
Figure 2: Route Discovery of AODV. the packet delivery ratio, the end-to-end latency, and the routing overhead of the AODV in a high-density MANET [23]. But GAODV only applies to uniform network. In addition, many routing protocols based on energy have been studied [24]. [PERSON] and [PERSON] in [25] proposed a method (PEAB) that trims down the flooding problem by considering nodes current remaining energy and threshold random delay to generate rebroadcast probability dynamically for efficient route discovery. [PERSON] et al. proposed AODV-SUARP protocol, which determines the energy level of nodes through energy stability parameters and effectively select the most reliable nodes to participate in packet transmission [26]. By comparing the related protocols, as shown in Table 1, we presents a routing discovery and maintenance method based on location and energy information, named the focused AODV (RFAODV) routing protocol, to address the broadcast storm problem in direct application of AODV in UWSNs, to achieve higher packet delivery ratio and lower energy consumption.
## 3 Quantum Modelling of FMO Quantum Excitation
### Methods, Extent of Analysis, and Source Data
#### 4.1.2 Ranges and Extent of the Parametric Analysis  
While the constant \(r_{\rm trap}=1\,\)ps\({}^{-1}\) is maintained fix in the analysis despite it is directly responsible from the trap efficiency for the BChls 3 and 4. Because we do not introduce the RC explicitly as a quantum system (as an instance in [47]), certain BChls (as the before mentioned) gather the final excitation upon the equilibrium.  
The parameter \(\lambda_{k}\) for the reorganization energies (5) reflecting the architecture of BChls inside FMO complex is responsible of the decoherence strength. Note there are differentiated \(\lambda_{k}\) values for each BChl, despite the analysis in the literature commonly uses a unique value for all of them in order to describe the dependence on their main working value for such elements. In addition, the experimental knowledge about their concrete values is still poor and not completely conclusive. The most accepted value experimentally for \(\lambda_{k}\) is approximately 35 cm\({}^{-1}\), then we will analyze the behavior in the interval \([35,75]\) cm\({}^{-1}\) to include other values discussed in the literature (particularly \(\lambda_{k}=65\,\)cm\({}^{-1}\)) [53; 54].  
The parameter \(\gamma_{k}\), as it was previously discussed, is the bath cutoff frequency (the inverse of the bath coherence time-scale, the memory time of the environment). \(\gamma_{k}\) has been experimentally determined around of 50 cm\({}^{-1}\). Here, we analyze it in the interval \([30,50]\) cm\({}^{-1}\)[50; 54]. For that reason, in the analysis sections we refer to them as \(\lambda\) and \(\gamma\) simply. As it was previously stated, note all of those constants are normally expressed in energy units, but in spectroscopy they are traditionally reported in cm\({}^{-1}\) through the converting factor 200\(\pi\hbar c\), while similarly, the switch between cm\({}^{-1}\) and s\({}^{-1}\) is reached through the factor 200\(\pi c\).  
Finally, the temperature \(T\) is an important factor in the equilibrium populations, the efficiency and in the coherence time of quantum coherence (a phenomenon expected to occur at very low temperatures avoiding that excitation not will be rapidly extinguished for the environment noise at high temperatures). Despite in the FMO the actual quantum phenomena occur at room temperature, experimental spectroscopy analysis are performed to relatively low temperatures around 77 \({}^{\circ}\)K.  
Notably, green sulfur bacteria have been discovered living near from a black smoker at the coast of Mexico in the Pacific Ocean at its limit operation temperature of \(60\,^{\circ}\mathrm{C}\) at a depth of \(2500\,\mathrm{m}\). At such a depth, no sunlight penetrates, but it still lives from the dim glow of the thermal vent [16]. In the current analysis, we will consider the range \([77,347]\,^{\circ}\mathrm{K}\).