esa-sceva/satcom-chunk-collection huggingface dataset:
# Introduction to Photonics: Principles and the Most Recent Applications of Microstructures
## 5 Classification of Optical Fiber Sensors  
There have been some approaches to the classification of optical fiber sensors. The increasing complexity of several types of optical fiber sensors is what prompted the development of adequate and appropriate classification systems. Factors such as physical quantity transduced by the sensors, detection systems, as well as sensor type have been considered in so many classifications. To develop the most suitable classification scheme for optical fiber sensors, an emphasis is given to the most important aspects and, hence, a classification method is adopted. Previous work that attempted to offer classification methods that cover the majority of the essential optical fiber sensors is cited in [61]. With the continuous increase in the development of optical fiber sensors, so many classification systems that were adopted previously became unsuitable. Other classification systems were given based on the modulation type chosen [62; 63]. Hence, factors like wavelength, intensity, phase,  
Figure 6: Total internal reflection phenomena  
and polarization were regarded as the primary classification standards. The disadvantage of this type of classification, however, is that the technique used is given emphasis rather than the sensor itself. This may be insignificant in applications where the most suitable technology is targeted for measuring a parameter of interest like pressure or temperature. This second method that considered variables like temperature, pressure, magnetic field, electric field vibration and flows in classifying sensors has also been adopted [64]. However, this approach is also associated with some disadvantages when applied in a similar way to the other methods of measuring various parameters like displacement. Other factors such as novelty and geographical location were also considered in the classification of sensors [65]. In the most extensively used system of classification, optical fiber sensors are classified as intrinsic or extrinsic sensors [66; 67]. Extrinsic sensors are those in which the fiber guides the light wave, and the interaction between the magnitude of the parameter measured and light occurs outside the fiber. These types of sensors have been used successfully for some applications. For intrinsic sensors, on the other hand, interactions between light and the measured parameter occur inside the fiber. Figure 7 shows a comparison between the intrinsic and the extrinsic optical fiber sensors [61].  
An important parameter to be considered in intrinsic sensors is the nature of the optical guidance of the fiber--that is, whether it is multimode, single, or otherwise. Another important sub-class of the intrinsic sensors is interferometric sensors [61].
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
###### Abstract
In order to analyze the performance of our new resource allocation protocol, we perform system level simulations (SLSs) under the environments consisting of one UBSC and multiple UBSs. We consider four performance measures: average packet drop rate, average resource utilization, average message overhead, and reserved backhaul capacity in the UBSC. In our SLSs, these four performance measures are investigated with respect to the average input traffic and the average burst traffic of UBSs by considering two scenarios:  
* Scenario 1: varying the upper limit (or maximum) of the backhaul capacity of a UBS,
* Scenario 2: varying the lower limit of the resource utilization threshold where a UBS returns unused backhaul capacity when its current resource-utilization becomes less than this lower limit. For each scenario, we provide a summary of our SLS results and analysis, and an operational guideline for the backhaul resource allocation in the UCNN using the proposed UBSI protocol. The remaining part of this paper is organized as follows. In Section 2, the structure of a considered UCWN is described. In Section 3, a new resource allocation protocol is specified in detail, including its queue model, the way to determine the backhaul capacity of a UBS, and the corresponding protocol messages and procedures. In Section 4, the performance is investigated via extensive system level simulations considering two scenarios. Finally, in Section 5, we conclude this paper.
## 8 Applications of UWSN Technology
### Natural Disasters  
The effects of natural disasters, including floods, can be efficiently avoided with the help of wireless sensor networks. Rivers requiring the real-time monitoring of water level changes successfully deployed wireless nodes. Machine health monitoring is one example of industrial monitoring. In order to significantly reduce costs and enable additional functionality, wireless sensor networks were created for the condition-based maintenance (CBM) of industrial machinery. In wired systems, the expense of wiring frequently prevents the installation of enough sensors. Wireless sensors can now access previously inaccessible places, rotating machinery, dangerous or restricted areas, and mobile assets.  
* Wireless sensor networks are also used to collect data for environmental information monitoring. This can be as basic as monitoring a refrigerator's temperature to as complex as monitoring the water level in a nuclear power plant's overflow tank. The performance of the systems can then be demonstrated using statistical data. The ability to receive \"live\" data feeds is what sets WSNs apart from traditional loggers.
* Monitoring the quality and level of water involves many different activities, including determining the quality of the surface or subsurface water and ensuring a country's water infrastructure for the benefit of humans and animals.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 2 Theoretical Background
### Spatial Consistency  
Spatial consistency is an essential characteristic of time-varying channels for outdoor scenes, specifically the characteristic that the channel parameters vary continuously over time and space, and the study of channels including spatial consistency can improve the simulation's realism.  
The 3 GPP TR38.901 file contains the channel correlation matrix information that is used to choose the correlation distance for various scenario contexts in the spatial consistency simulation. Correlation distance determines the length of a channel segment. Within the correlation distance range, large-scale parameters are considered spatially correlated during the UAV movement. Correlation distance for spatial consistency is shown in Table 1.
## 2 Experimental and Theoretical Tools
### Diagnostic Techniques
#### 2.3.2 High-Speed OH* Chemiluminescence (OH*) Imaging  
Excited hydroxyl radicals (OH*) are good tracers of high temperature combustion regions in a flame [28]; therefore, visualization of OH* chemiluminescence at the base of the flame allows to quantifying the lift-off length (LOL). Moreover, if an intensified high-speed camera is used, the ID can be measured too. In these experiments, a high-speed image intensifier (Hamamatsu C10880 by Hamamatsu, Hamamatsu city, Japan) was used, with gain set between 700 and 850 depending on fuel and operating condition. It was coupled to a Photron Fastcam SA5 camera with a 1:1 relay lens. The system was equipped with a UV f/4 100 mm focal length lens. An interference filter centered at 310 nm (10 nm FWHM) was placed in front of the camera to remove most of the radiation of the flame while keeping OH* chemiluminescence. As shown in Figure 1, a dichroic mirror, which reflects UV spectrum and transmit the visible light spectrum, is used to reflect the UV flame radiation to the intensified camera.  
Images were taken at 25 kfps. The resolution was 704 \(\times\) 416 pixels and the magnification was 5.43 pixel/ mm. The shutter time was adapted between 19.97 us and 39.75 us to accommodate the camera dynamic range to the flame radiation intensity as a function of the fuel and operating condition. Again, 20 injection cycles per test were recorded in any case. ID was obtained determining the first frame with detectable light intensity, and it was used as a check on the results obtained with the schlieren technique.  
The algorithm used to obtain the LOL is based on the procedure described in [29] and recommended by the engine combustion network (ECN). Thus, the LOL was as the distance between the injector tip and the first axial locations above and below the spray centerline with intensity greater than 50% of the intensity peak of that zone.
# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 2 VANETs Applications
### Non-Safety Applications  
Comfort and entertainment applications are called non-safety applications that aim to improve the comfort levels of drivers and passengers and make travel more pleasant. [5; 6; 9]. Communication usually takes place between vehicles or between vehicles and an RSU. Non-safety applications can tolerate delays because they do not have strict real-time requirements. Vehicles can use spatial- and time-validity information to determine PEL, where to cache and whether to participate in message dissemination.  
Some of the non-safety applications are as follows:  
* Traffic navigation map: The spatial validity of the traffic navigation map is 10 km, and the time validity is 30 min [5; 6; 9].
* Entertainment/Multimedia applications: Music downloads, file sharing, home control, and other interactive entertainment applications are examples of interactive entertainment applications. Information is not required by all vehicles, but rather on-demand based on user preferences.
* Commercial advertisement: The spatial validity of this application is 1.5 km, and the temporal validity is 1-10 days [5; 6; 9].
## 3 Underwater Routing Protocols
### Umdr
#### 3.2.5 Protocols Avoiding the Void Region  
EBPR  
Energy balanced pressure routing (EBPR) uses the feedback from sensor nodes for the beacon signals to avoid void zones [69]. It does not take into account the velocity of nodes to avoid void zones. In addition, residual energy is considered as a routing metric in selecting a forwarder node. Furthermore, the proposed protocol uses the redundant route information to help nodes that do not have this information to update route information. The EBPR increases the lifetime of the network. However, it suffers from poor performance in sparse networks when sending beacon signals is less effective.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### Challenges in Mitigating RFI
#### 2.4.1 Test Equipment Used to Investigate RFI  
A spectrum analyzer [7] is a critical tool for monitoring radio frequencies. It measures the power of electromagnetic signals over a range of frequencies, making it possible to detect unauthorized or unintended transmissions that could disrupt aviation communication or navigation systems. Spectrum analyzers are widely used to identify interference in both VHF and UHF bands. RF signal generators [8] are essential for producing controlled electromagnetic signals to test and calibrate aviation systems. These devices help simulate interference scenarios, allowing engineers to evaluate how avionics, such as radar altimeters and communication systems, respond to specific types of interference.
## 4 Conclusions  
Peanut microwave pretreatment prior to cold pressing was effective for improving the extraction yield, micronutrients content, oxidative stability, and flavor quality of oil. Although the acid value (AV) and peroxide value (PV) of extracted oil from MW-treated peanuts were increased, the values were both far below the limit in the Codex standard for cold-pressed and virgin oils. Comparing with the untreated sample, 5 min MW pretreatment on peanuts significantly increased the oil extraction yield, phytosterols content, tocopherols content, and the induction period of the oil extracted by cold pressing. MW pretreatment on the peanut also formed the pyrazine which contributed to improving the nutty and roasty flavor of the cold-pressed oil. In conclusion, MW pretreatment is a feasible method to improve the oil extraction yield and to obtain the cold pressed peanut oil with longer shelf life and better flavor. The economics and energy requirements for the industrial-scale continuous microwave-assisted system need to be further investigated.  
Performing experiments and writing-original draft preparation, H.H.; methodology, H.L.; software, A.S.; resources, L.L.; writing-review and editing, M.L.F.; conceptualization and supervision, Q.W.  
This research was funded by the Special National Key Research and Development Plan [2016 YED0400204]; and the Technology Innovation Program of the Chinese Academy of Agricultural Sciences [CAAS-ASTIP-201X-IAPPSI].  
The authors declare no conflict of interest.
## 1 Introduction
Particularly, the CDS selection algorithm provides a CDS while the CDS size reduction algorithm uses some rules and the weighted function to reduce the size of CDS which supports the cluster process more effective. * We next propose a CDS based clustering algorithm to formulate clusters by using the weighted function and a node's identify for the cluster head election process. Moreover, this algorithm also helps to select the gateways and cluster heads in the set intermediate nodes which makes the routing process more efficient. * We develop a sending channel based focus region selection (CFS) algorithm in routing phase to support the intermediate nodes to select dynamically a channel for sending a route request packet which can avoid the affected region of a primary user. A CRD protocol is then proposed to establish a good route from a source to a destination which utilizes the CDS based clustering and CFS algorithms to improve the network performances. * We finally implement the proposed CRD protocol in environmental conditions close to reality (OMNET++ platform) to show its superiority over the conventional AODV protocol in terms of PDR, control overhead, delay and energy consumption. The rest of paper is organized as follows--Section 2 describes system model, energy model and channel model. Section 3 presents the proposed CRD protocol consisting of four subsections, such as basic concepts of the system model, necessary function and definitions, packet structures, underlying technology and the proposed CRD protocol. In the underlying technology subsection, we present necessary algorithms such as the proposed CDS selection, the proposed CDS size reduction, the proposed CDS based clustering. Section 4 evaluates the performances of the proposed protocol in the considered system setup. Finally, Section 5 concludes the paper.
## 5 DFIG Connected at Electrical Grid
### B2B Power Converter Analysis
#### 5.1.1 Loop Controller  
There are many papers in the literature on control strategies for controlled current three-phase power converters. Basically, the control problem is identical to synchronous-machine control. Since the B2B power converter control method is not the main point of this work, only a brief description of the control strategy implemented in this analysis will be mentioned. In this paper, the synchronous PI controllers are used. This is one of the most popular control methods. The idea is to transform currents and voltages into a rotating reference frame, where the controlled currents are constant in steady-state and use ordinary PI controller outputs back to the fixed reference frame. The drawbacks are the nonlinearities introduced in the transformation and the less than optimal control performance. The advantage is that it is simple to understand and simple to implement. Synchronous PI controllers work with averaged voltages and currents. The converter is controlled via a PWM modulator that generates a switching pattern that corresponds to the desired average voltage. In this paper, the line current controller is assumed to be a synchronous PI controller. Its properties are well known in the power electronics area. Furthermore, by assuming this type of controller, the three-phase converter  
Figure 6: B2B power converter configuration.  
can be modeled as two separate four quadrant converters: one for the active power and one for the reactive power. The reactive power is assumed to be zero, which means that this part can be removed. It is important to consider that the controlled system is nonlinear, but the large-signal stability is not examined. The stability of the closed-loop system should be examined with methods for non-linear systems. The load power is a factor in all coefficients of the characteristics equation of the closed-loop system, and the poles of the linearized system move around with the load of the B2B power converter. Consequently, the controller parameters must be chosen to keep all poles in the LHP of the complex plane under all load conditions.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 2 Vehicle Dynamics Modeling
### Rollover Stability Evaluation Index  
The indicators used to evaluate vehicle rollovers include lateral acceleration, roll angle, LTR, and rollover protection energy reserve (RPER) [50; 51]. Among these, LTR needs no change with changes in vehicle type, operating conditions, and road conditions. More specifically, the ratio of the difference between the vertical load of the left and right wheels of the vehicle and the sum of the vertical load of the left and right wheels is the lateral load transfer rate It is an accurate representation of the lateral load at the current moment in the vehicle journey and has the advantages of being simple to express, easy to calculate, and good portability. The calculation formula is as follows:  
\[LTR=\frac{F_{l}-F_{r}}{F_{l}+F_{r}}, \tag{11}\]  
where \(F_{l}\), and \(F_{r}\) denote the sums of the vertical loads on the left-hand side and the right-hand side of the wheels of the vehicle, respectively. \(F_{l}\) and \(F_{r}\) satisfy \(F_{l}+F_{r}=mg\). \(LTR\) takes values in the range of [-1,1]. When the value of \(LTR\) is zero, the car is stable, and all four wheels are in contact with the ground; the larger the value of \(\left.\right|LTR\), the greater the tendency of the car to roll over. When \(LTR=-1\) or \(LTR=1\), the left or right wheel of the car is completely off the ground, at which point the vehicle is considered to have rolled over. To establish a sufficient safety margin, the rollover threshold is generally set to 0.8, that is, when \(\left.\right|LTR\) \(\left.\right|\geq\) 0.8, the vehicle is considered to have the risk of rollover, and the vehicle should perform the corresponding anti-rollover intervention to be restored to a stable state.  
According to the established 3-DOF vehicle rollover dynamics model, the moments of the left and right wheels of the vehicle can be obtained from the following:  
\[ma_{y}h_{o}+mg\left(\frac{T}{2}-h\sin\phi\right)=F_{l}T, \tag{12}\]  
\[mg\left(\frac{T}{2}+h\sin\phi\right)-ma_{y}h_{o}=F_{r}T, \tag{13}\]  
where \(a_{y}\) denotes lateral acceleration at the center of mass; \(T\) refers to wheelbase of the vehicle; \(h\) denotes the distance between the center of mass and the ground.  
When the angle of roll is small, \(sin\)\(\phi\approx\phi\), then:  
\[LTR=\frac{2a_{y}h+2 gh_{o}\phi}{gT} \tag{14}\]  
The relationship between the lateral load transfer rate and the body and driving parameters of the vehicle can be derived from Equation (14), where \(a_{y}\) and \(\phi\) can be obtained from onboard sensors and inertial guidance systems, respectively. In the subsequent simulation, the relevant parameters in Equation (11) can be directly output from the software and are more suitable for simulation control.
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 6 Phase-Based Sensors
A planar closed-loop ring resonator was shown to provide interesting sensitivity and the ability to address measurement of physiological concentrations in water-glucose solutions [88]. From the point of view of sensitivity normalization, phase-based sensors constitute a special case since the selection of the operating sensing parameter (\(SP_{0}\)) in (4) to compute the \(RS(G)\) is not trivial. Indeed, taking as reference the measured phase for the lowest \(\rho_{\mathrm{g}}\) case does not make much sense, since it gives no information about the performance of the sensor, and could lead to confusing percentage sensitivities. In ref. [155], a figure of merit was proposed for phase-based dielectric sensors, expressed as the ration between the maximum sensitivity in \({}^{\circ}\) and the size of the sensing region in terms of the squared guided wavelength. While consistent, this parameter seems not to provide the desired information about the sensor performance for the specific case of glucose sensing. An interesting value for \(SP_{0}\) could be the measured phase difference for the lowest \(\rho_{\mathrm{g}}\) value, which is, in general, unfortunately neither given in the references, nor possible to compute from the provided data. Due to these reasons, the review for glucose planar phase-based sensors is summarized in Table 4, ordered by operating frequency, without any computation of \(RS(G)\), providing the raw sensitivity in terms of \({}^{\circ}\) per wt%. The sensitivities in \({}^{\circ}\) per mg/dL are given only for the cases in which measurements of physiological concentrations were shown. The operating phase for the reflection or transmission parameter is given for the lowest \(\rho_{\mathrm{g}}\) case for informational purposes only. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Sensor Type**} & **Sample** & \multirow{2}{*}{\(\rho_{\mathrm{g}}\)**Range** **(Volume)**} & \multirow{2}{*}{\(\rho_{\mathrm{g}}\)**Range** **(wt%)**} & **Op. \(f\)** **(GHz)** & \multirow{2}{*}{\(\boldsymbol{\phi_{S1I}/\phi_{S2I}}\) (\({}^{\circ}\) per mg/dL)} & \multirow{2}{*}{**S\(\boldsymbol{\phi_{S1I}/\phi_{S2I}}\)**} & \multirow{2}{*}{**Remarks**} \\  & & & & & & & (\({}^{\circ}\) per wt\%) & \\ \hline
[88] & Closed-loop & AG (—) & 0–0.25 & 4.02 & \(-66.00\) & \(4.694\times 10^{-3}\) & 4.694 & — \\
[86] & MLIN res. & AG (7.5 mL) & 0.08–5 & 4.88 & \(-111.43\) & \(1.510\times 10^{-3}\) & 1.510 & — \\
[123] & Triple open & SRR & AG (MF) & 0–40 & 5.40 & 134.38 & — & 7.813 & MF channel \\
[154] & MLIN-based & & & & & & & Portable, \\ sensor & AG (—) & 0–5 & 7.81 & — & \(0.037\times 10^{-3}\) & 0.037 & PRN-driven \\
[153] & MLIN res. & AG (—) & 0–0.11 & 19.04 & \(-82.79\) & \(3.182\times 10^{-3}\) & 3.182 &
\begin{tabular}{c} Only \\ simulations \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Phase-based sensors. AG = aqueous glucose solutions; MLIN = microstrip line; PRN = Pseudo random noise.