esa-sceva/satcom-chunk-collection huggingface dataset:
# The TORC2-Dependent Signaling Network in the Yeast _Saccharomyces cerevisiae_
## 6 Structure, Function and Regulation of Pkc1
### Modulation by PtdSer and DAG
In this regard, it has been reported that, like Rho1 itself, the bulk of full-length Pkc1 localizes to sites of polarized growth [249], consistent with its known function in maintenance of cell wall integrity signaling. Moreover, using a fragment-based approach, it was found that a segment containing the tandem Hr1 domains localized to the bud tip, whereas a portion containing the tandem C1 domains localized to the PM all around the cell periphery [249]. However, certain fragments displayed prominent nuclear localization, which lead to the identification of two functional nuclear localization signal (NLS) elements [249]--one SV40 T-antigen-like (554-KKKKRTPV-560) and one bipartite nucleoplasmin-like (808-KHHKKRAAKRRKVSL-821) [250]. Yet, full-length Pkc1 does not localize to the nucleus, raising the possibility that Pkc1 undergoes nucleocytoplasmic shuttling in which its rate of nuclear export keeps pace with its rate of import; and, that hypothesis led to the identification in Pkc1 of a functional nuclear export signal (NES; 51-LEYLEDSLKKLRL-63) [249] of the Leu-rich Crm1/Xpo1-dependent class [251]. This shuttling may explain why certain reported Pkc1 substrates (Figure 7) are located in the nucleus (see further below); however, since the nucleus appears to lack Rho1 and its content of Ca2+, PtdSer, and DAG is uncertain, it raises the question of what factors, if any, allow Pkc1 to be active in this compartment. It is also of some potential concern that the conclusions of this fragment-based approach were not corroborated by installing in full-length Pkc1 substitution mutations that ablate the folding and/or the ligand-binding residues in each of the corresponding motifs (Hr1 domains, C2 domain, etc.) and examining the consequences. To further confound matters about the importance of localization and the role of modulatory factors, it has been reported that a cloned fragment that encodes only the C-terminal catalytic domain of Pkc1, which was isolated on the basis of its complementing the temperature-sensitive lysis defect of a _pkc1 ts_ allele (_cly15_), is also able, at least when overexpressed from a _GAL_ promoter on a plasmid, to rescue the growth and, quite surprisingly, the morphology defects of a _pkc1_ null mutant [210]. For these reasons, much is still uncertain about how, why, and where in the cell Pkc1 is active and carries out its multifarious functions.
## 3 Current Status of the Circuit Component in the MMW Regime
### Recent Works of SoC in MMW Regime
#### 3.7.2 SiGe MMW Transceivers
Figure 19: The system block diagram of the dual-polarization W-band phased array transceiver [214]. In [218], a fully differential 240 GHz sub-harmonic transceiver was implemented in 0.13-\(\upmu\)m SiGe technology. This transceiver has a measured EIRP of \(-1\) dBm and a measured tuning range of 61 GHz whereas the measured phase noise is \(-84\) dBc/Hz at 1 MHz offset at 240 GHz. Furthermore, the 240 GHz mixer has a simulated conversion gain of better than 5 dB, and an IP\({}_{\rm 1 dB}\) of \(-1.3\) dBm. In [103], a dual-frequency transceiver in 0.13-\(\upmu\)m SiGe technology was presented. The transceiver has a measured maximum output power of 3.5 dBm at 165 GHz. The conversion gain of it is 27 dB with the 3-dB bandwidth of 9 GHz. In addition, the temperature effect is also measured. It is observed that the receiver gain is reduced to 11 dB at 75 \({}^{\circ}\)C, and to 25 dB at 125 \({}^{\circ}\)C, but the transceiver is still functional. The receiver has a P\({}_{\rm 1 dB}\) of \(-20\) dBm, the linearity, like the receiver gain, degrades significantly above 100 \({}^{\circ}\)C. The transmitter has a measured output power from 23.5 dB to 3 dB, and from 10 dBm to 3.5 dBm, respectively. Moreover, the oscillator at 82.5 GHz drives the static frequency divider and the two amplifiers. Based on 0.12-\(\upmu\)m SiGe technology, the 16-elements phased array transmitter [212] and receiver [213] were reported. For the transmitter [212], the measured OP\({}_{\rm 1 dB}\) for the single-element is 9 dBm, corresponding to a 16-element total generated power of 21 dBm and allowing for an EIRP of 33 dBm under ideal spatial power combining excluding unit antenna gain. With increased PA bias conditions the OP\({}_{\rm 1 dB}\) per element and total generated power increase to 13.5 dBm and 25.5 dBm, respectively. The OP\({}_{\rm 1 dB}\) has a measured variation of \(\pm 0.25\) dB across different phase settings and adjacent elements. For the receiver [213], it has an input P\({}_{\rm 1 dB}\) of 59 dBm at 47 dB gain and 37 dBm at 20 dB gain. An adjacent-channel IIP\({}_{3}\) of 26.5 dBm with the Rx gain is set to be 35 dB (single channel). The noise figure is 7.4 dB at the highest gain mode and 11.6 dB when the receiver gain is set to be 15 dB. The sensitivity at 30 dB Rx gain setting is 86 dBm under the assumption of 2 GHz noise bandwidth and a 12 dB improvement in SNR while the input power is \(-44\) dBm. Also, the measured phase noise is 90 dBc/Hz at 1 MHz offset.
## 1 Introduction  
The technological advancement of vehicle platforms--including electronic control units (ECUs) and their firmware, intra-platform communication on control buses between ECUs, and other advancements necessary for future platform automation--exposes opportunities for new attack paths and vulnerabilities. This research aims to explore concepts and technologies needed to enable autonomous, active cyber defense of vehicle platforms for survival and recovery from cyberattacks in contested cyberspace environments (CCEs).  
Vehicle platforms, being cyber-physical systems, are not currently resilient in a CCE [1]. Cyber resilience is the ability of systems to recover from cyber stress that causes a reduction in performance [2]. Essentially, how well can the platform continue to perform its mission in a CCE? As humans alone cannot defend all vehicle platforms in real-time, it is necessary to develop software able to defend against novel cyberattacks during operation in a CCE.  
We propose using reinforcement learning (RL) as an approach for cyber-resilient autonomous intelligent cyber-defense agents (AICAs) [3, 4] in vehicle platforms. The software agent is trained to defend a controller area network (CAN) bus from malicious messages. We evaluate the agent using traditional classification measurements such as accuracy, as well as the U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory's proposed quantitative measurement of cyber resilience (QMoCR) [5] (see Section 3.6). QMoCR is calculated using the difference in area under the curve of a measured key performance parameter (KPP); in our case, this performance measure is vehicle speed.  
Our contributions are as follows. While RL has been suggested and used in automotive applications, to the best of our knowledge, we are the first to use RL for detecting and preventing CAN bus injection attacks at the network layer. Our feature engineering approach for the RL agent reformulates the observation space from an action-independent multi-armed bandit task into an action-dependent Markov decision process. We argue that this impact on the CAN bus state justifies the use of RL methods over supervised machine learning (ML) methods such as classification, and our results show that RL is superior with and without this feature engineering. Additionally, we use QMoCR to evaluate cyber resilience in Toyota's Portable Automotive Security Testbed with Adaptability (PASTA), which gives a better picture of real-world effects vs. using simple receiver operating characteristic curves or loss metrics.
# Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies
## 3 Software Program Used for Determining Possible Structures
### Influence of the Number of Turns on the Gain Distribution  
Considering the fact that the number of turns can vary up to 20, the influence of the number of turns when considering the same exterior diameter is also an important matter.  
In this study, two circular spiral structures with exterior diameters of 15 mm, w and s values of 0.1 mm, and 7 (Figure 13a) and 12 (Figure 13b) turns, respectively, were considered.  
The conclusion is that when the number of turns was higher, the gain was more evenly distributed on the surface of the tag, but the peak value of the higher number of turns was lower.  
Figure 12: Gain representation of four coil shapes: (**a**) circular; (**b**) square; (**c**) hexagonal; (**d**) octagonal.  
Figure 13: Gain representation of two coil shapes with different numbers of turns: (**a**) \(N=7\); (**b**) \(N=12\).  
It can be determined that the aforementioned conclusion is verified. Namely, when the number of turns increased, the maximum gain decreased for all the coil shapes considered in this study. Also, with the increases in the exterior diameter, the value of the gain also increased. There were two points at which the maximum gain was reached; therefore, the positioning of the tag is very important.  
Considering all the coil shapes analyzed, the maximum gain values were obtained for the square coil planar structures.  
The directivity was not considerably influenced by the number of turns or the exterior diameter, as can be seen in Figure 15a,b.
# Comparison of Tracking Techniques on 360-Degree Videos
## 3 Experiments
### Experiment Process  
Our implementation of eight tracking algorithms was based on the open-sourced OPENCV library with Version 3.4.2 (Intel Corporation, Santa Clara, CA, USA). All eight trackers were initialized with default parameters. Among these trackers, the GOTURN tracker was the only one based on the Convolutional Neural Network (CNN) and utilized a standard Caffe model for online tracking. For each frame in a 360-degree video, the centroid of the tracking target was marked up manually in advance as the ground truth of tracking. Figure 3 shows the flowchart of the proposed experiment to evaluate eight trackers on nine 360-degree videos. Each tracker was executed on individual 360-degree video sequences in turn to measure the tracking speed in terms of Frames Per Second (FPS). A spatial displacement (in pixels) was computed as the absolute distance between the tracker's output position and the ground truth. If the displacement was smaller than a predefined tolerated threshold, the frame was counted as a correct tracking frame. The accuracy was defined as the ratio of the number of correct tracking frames to the number of all frames. Because only the GOTURN, TLD, CSRT, and MedianFlow trackers could adjust and update target window size dynamically, adaptable window size was implemented in these four trackers for qualitative evaluation. To make a fair comparison of eight trackers, the size of the target window was not considered for quantitative evaluations. For the same reason, the Kalman filter was not applied for all trackers in our experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sequences** & **Characteristics** & **Viewpoint** & **Occlusion** & **Deformation** & **Lighting** & **Scale** & **Shakiness** \\ \hline  & v & & v & & & \\  & & & v & & & \\  & & & v & & & \\  & & & v & & & \\  & & & v & & & \\  & &
## 19 Comparison of Traveling Wave and Non-Traveling Wave Based Methods  
There is no doubt that traveling wave methods for fault location and classification are found accurate and rapid as compared to non-traveling wave methods. However, detection of first traveling wavefront and handling large length of data window are the challenging tasks associated with the traveling wave methods. Table 2 summarizes the comparison of traveling and non-traveling wave-based methods for fault location estimation in MT-HVdc transmission system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Parameters** & \
\begin{tabular}{c} **Traveling Wave Based** \\ **Method** \\ \end{tabular} &
\begin{tabular}{c} **Non-Traveling Wave Based** \\ **Method** \\ \end{tabular} \\ \hline \multirow{3}{*}{1} & Wavehead/wavefront detection & First traveling wavefront is required & \multirow{3}{*}{Not Required} \\  & & Difficult to detect & \\ \hline \multirow{3}{*}{2} & Post and pre fault voltage values & Post and pre fault voltage values are required. & Only post fault voltage values are required. \\ \hline \multirow{3}{*}{3} & Post and pre fault current values & Post and pre fault voltage values are required. & Only post fault current values are required. \\ \hline \multirow{3}{*}{4} & Length of data window for fault estimation & Large length of data window is employed for an accurate fault estimation. & Smaller data window is sufficient for an accurate fault estimation. \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of traveling and non-traveling wave-based methods for multi-terminal high-voltage direct current (MT–HVdc) transmission systems.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 3 Preliminaries  
### Sensor Node Model  
Each sensor node \(u_{i}\) has a distinguishable identifier \(I_{i}\). Suppose node \(u_{i}\) is powered by the battery it carries, we can denote the maximum power as \(P_{max}\) and the remaining energy at time \(t\) as \(P_{i}(t)\). The node dies at time \(t\) if \(P_{i}(t)=0\) (rechargeable battery is a possibility but not considered here).  
Driven by different applications, each node can carry out many operations, such as sensing nearby information, transmitting data, etc. Among these operations, communications through the wireless channel dominate the energy consumption [12]. Therefore, we assume only two states \(\{ON,OFF\}\) in this paper; \(OFF\) means the node turns its radio off to save energy, while \(ON\) means the node turns the radio on for communication. We focus on the communications during neighbor discovery process. Before each node tries to communicate with others, it has to identify the neighboring nodes first. Therefore, we suppose the nodes can communicate only when the discovery is successful.  
Suppose time is divided into slots of equal length \(t_{0}\) which is sufficient for the nodes to establish a communication link on the channel. Denote the consumed energy in each time slot as \(p_{n}\) if the node's radio is turned on, and \(p_{f}\) if the radio is turned off. Notice that a node may send a beacon message for discovering neighbors, listen on channels, exchange information, etc. Different operations may consume different energy when the radio is \(ON\); we assume \(p_{n}\) is the average energy in a time slot for simplicity. In practical systems, switching the radio states also consumes energy, such as \(p_{nf}\) (switching the radio from on to off) and \(p_{fn}\) (switching the radio from off to on). As shown in Figure 2, a node has two states \(\{ON,OFF\}\) and it switches states in each time slot. When a node switches from \(OFF\) to \(ON\) as \(F\to N\), the consumed energy is \(p_{fn}\); when it switches from \(ON\) to \(OFF\) as \(N\to F\), the consumed energy is \(p_{nf}\); when it keeps state \(ON\) as \(N\to N\), the (average) consumed energy in the slot is \(p_{n}\); when it keeps state \(OFF\) as \(F\to F\), the consumed energy is \(p_{f}\). In this paper, we adopt the common assumption \(p_{f}=p_{nf}=p_{fn}=0\), and prolonging the lifetime of the node is equivalent to reducing its percentage of time slots when the radio is on. The notations are also given in Table 1.  
Figure 2: The finite state machine (FSM) of a sensor node’s states.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 2 Unmanned Ship-Wind and Wave Coupling CFD Model
### Unmanned Ship Structure and Computational Domain  
The 3D CAD model of the unmanned ship is shown in Figure 1, and the parameters are shown in Table 1. In order to consider the influence of multiple factors, such as navigation parameters, water flow, wind, and quay wall effects during the berthing process of the unmanned ship, a berthing model of the unmanned ship was established based on CFD for calculation simulation. In the actual calculation, it was assumed that the hull was relatively stationary, and the speed was determined on the basis of the relative speeds of water and wind; the calculation domain was selected by the method of simulating a numerical tank, with water below the flowing medium and air above. The size of the calculation domain affects the accuracy of the simulation results to a certain extent. If the calculation domain is too small, the flow field area is too small, which will not meet the boundary conditions at infinity, and the water flow will hit the wall early causing a backflow, which will distort the simulation result. The computational domain is shown in Figure 2, the specific size was L 5.15 m \(\times\) W 1.65 m \(\times\) H 0.8 m.  
If the flow field in an infinite space is simulated, it is theoretically closer to the actual situation, but the computational domain expands, and the grid increases accordingly, which will increase the amount of calculations. In practice, when the computational domain expands to a certain extent, the improvement of calculation accuracy is not obvious. When considering the motion of unmanned ships at berth, the speed of the ship is small, and the impact on the surrounding fluid is small. In these conditions, the calculation domain can be reduced appropriately, and the calculation amount can be reduced to improve the calculation efficiency.
###### Abstract
Indeed, LoRaWAN has proven to be effective for deploying underground to above ground transmission channels [4], as well as in the presence of metallic housings [5] and in severe industrial contexts [6]. Tests were performed also to analyze the performance for transmissions from under to above water in fresh water [7]: feasibility was demonstrated for transmitter depths up to more than 1 m, suggesting the usability of this technology for a number of applications [8]. The aim of this work is to test the performance of LoRaWAN for underwater to above water transmissions with the transmitter positioned in salt water at first by means of a theoretical analysis, then by means of preliminary laboratory measurements for different salinity levels and at different depths. Finally, a set of qualitative tests was carried out in sea water. The rest of the paper is structured as follows: Section 2 reviews the scientific contributions concerning radio transmission from salt water; Section 3 describes the theoretical analysis of EM propagation through a lossy medium, focusing on the estimation of the complex permittivity of salt water and on the mathematical modeling of link budget and path loss in underwater to above water transmissions; Section 4 illustrates the experimental setup adopted during the tests; Section 5 presents and discusses the experimental outcomes of the performed measurement campaigns; finally, in Section 6 the conclusions are provided.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 4 Routing Path Formation
### Adaptation for Hexagonal and Grid Topologies  
This protocol may also work for a hexagonal topology. When the gateway receives the SGN_RSP packet (see Section 4.1) from the neighbors, it can easily deduce that the neighbors are located in a ring around it, because all gateway neighbors indicate that they have two neighbors each. When the gateway sends the RR packet (see Section 4.2) to the neighbors, it will set the ring field in the header to true. This will allow a node to form multiple paths with multiple nodes. Each node in a layer may form paths with 1, 2 or 3 nodes in the next layer as shown in Figure 7.  
The neighbor nodes send the RR packet at randomly selected timeslots. The first RR packet will be received by three next layer nodes and they will form the path with the RR-sending neighbor (shown in Figure 7). The rest of the neighbor nodes form two paths with the next layer nodes, except the last node, which will form only one path. Thus, the resulting paths may be unbalanced, but this can be solved either by choosing one of the possible paths or by using all paths in a round-robin fashion, which would balance the energy consumption among the forwarding nodes.  
In the case of a grid topology, the network can be split in several sections as shown in Figure 8, each having its own sink (gateway). Each section then can be considered to have the topology shown in Section 3.1 (Figures 2 and 3).
# Performance of Tilting Pad Journal Bearings with the Same Sommerfeld Number
###### Abstract  
This paper presents the change of non-dimensional characteristics and thermal behavior of different sized tilting pad journal bearings (TPJBs) with the same Sommerfeld number. A three-dimensional (3D) TPJB numerical model is provided considering the thermo-elastic hydro-dynamic (TEHD) lubrication model with pad thermal-elastic deformation. The pivot stiffness is assumed to be the combination of linear and cubic stiffness based on the Hertzian contact theory. The TPJBs in a configuration of load between pad (LBP) with the same Sommerfeld number having seven different sizes are simulated, and their non-dimensional dynamic and static characteristics and thermal behavior are compared. Pad thermal and elastic deformation are both taken into account. If the changes in lubricant viscosity, thermal deformation, and elastic deformation of journal/pads due to viscous shearing are ignored, the bearings with identical Sommerfeld numbers should show the same performance characteristics. However, the heat generation at the bearing clearance during operation (a) induces a decrease in viscosity and heat transfer to journal/pads and (b) results in a thermal deformation. Furthermore, the elastic deformation of the tilting pads and pivots also affects the bearing dynamic performance. For the same Sommerfeld number, the numerical analyses provide how the viscous shearing and elastic deformation lead to a change in bearing performance. For the small bearings with the same Sommerfeld number, the non-dimensional characteristics did not change significantly, where the heat generation was small being compared to the large sized bearing. The largest change in non-dimensional characteristics occurred when the maximum temperature of the oil film increased by 30 \({}^{\circ}\)C or more compared to the lubricant supply temperature. The root cause of the change in the non-dimensional characteristics is the viscous shearing in the oil film, and the thermal deformation of the structures surrounding the oil film acts in combination. These results provide insight into the Sommerfeld number, which can be used for the early stage of bearing design.  
title: **Performance of Tilting Pad Journal Bearings with the Same Sommerfeld Number**  
**[PERSON]**, 1 Compressor Technology & Development, Ingersoll Rand, La Crosse, WI 54601, USA; [PERSON]RESS]  
2 School of Mechanical Engineering, Pusan National University, Busan 46241, Korea  
3 Department of Naval Architecture and Ocean Engineering, Pusan National University, Busan 46241, Korea; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-510-2332