We propose a new shadowed Rice (1948) model for land mobile satellite channels. In this model, the amplitude of the line-of-sight is characterized by the Nakagami distribution. The major advantage of the model is that it leads to closed-form and mathematically-tractable expressions for the fundamental channel statistics such as the envelope probability density function, moment generating function of the instantaneous power, and the level crossing rate. The model is very convenient for analytical and numerical performance prediction of complicated narrowband and wideband land mobile satellite systems, with different types of uncoded/coded modulations, with or without diversity. Comparison of the first- and the second-order statistics of the proposed model with different sets of published channel data demonstrates the flexibility of the new model in characterizing a variety of channel conditions and propagation mechanisms over satellite links. Interestingly, the proposed model provides a similar fit to the experimental data as the well-accepted Loo's (1985) model but with significantly less computational burden.

In wireless communications, the relative strength of the direct and scattered components of the received signal, as expressed by the Ricean K factor, provides an indication of link quality. Accordingly, efficient and accurate methods for estimating K are of considerable interest. In this paper, we propose a general class of moment-based estimators which use the signal envelope. This class of estimators unifies many of the previous estimators, and introduces new ones. We derive, for the first time, the asymptotic variance (AsV) of these estimators and compare them with the Cramer-Rao bound (CRB). We then tackle the problem of estimating K from the in-phase and quadrature-phase (I/Q) components of the received signal and illustrate the improvement in performance as compared with the envelope-based estimators. We derive the CRBs for the I/Q data model, which, unlike the envelope CRB, is tractable for correlated samples. Furthermore, we introduce a novel estimator that relies on the I/Q components, and derive its AsV even when the channel samples are correlated. We corroborate our analytical findings by simulations.

Gender-based violence (GBV) primary prevention programs seek to facilitate change by addressing the underlying causes and drivers of violence against women and girls at a population level. Social norms are contextually and socially derived collective expectations of appropriate behaviors. Harmful social norms that sustain GBV include women’s sexual purity, protecting family honor over women’s safety, and men’s authority to discipline women and children. To evaluate the impact of GBV prevention programs, our team sought to develop a brief, valid, and reliable measure to examine change over time in harmful social norms and personal beliefs that maintain and tolerate sexual violence and other forms of GBV against women and girls in low resource and complex humanitarian settings. The development and testing of the scale was conducted in two phases: 1) formative phase of qualitative inquiry to identify social norms and personal beliefs that sustain and justify GBV perpetration against women and girls; and 2) testing phase using quantitative methods to conduct a psychometric evaluation of the new scale in targeted areas of Somalia and South Sudan. The Social Norms and Beliefs about GBV Scale was administered to 602 randomly selected men (N = 301) and women (N = 301) community members age 15 years and older across Mogadishu, Somalia and Yei and Warrup, South Sudan. The psychometric properties of the 30-item scale are strong. Each of the three subscales, “Response to Sexual Violence,” “Protecting Family Honor,” and “Husband’s Right to Use Violence” within the two domains, personal beliefs and injunctive social norms, illustrate good factor structure, acceptable internal consistency, reliability, and are supported by the significance of the hypothesized group differences. We encourage and recommend that researchers and practitioners apply the Social Norms and Beliefs about GBV Scale in different humanitarian and global LMIC settings and collect parallel data on a range of GBV outcomes. This will allow us to further validate the scale by triangulating its findings with GBV experiences and perpetration and assess its generalizability across diverse settings.

In this paper we address the problem of identifying the modulation format of an incoming signal. We review many existing techniques for digital modulation recognition in a systematic way, which helps the reader to see the main features of each technique. The goal is to provide useful guidelines for choosing appropriate classification algorithms for different modulations, from the large pool of available techniques. Furthermore, the performance of a benchmark classifier is presented, as well as its sensitivity to several model mismatches. At the end, open problems and possible directions for further research are briefly discussed. I. INTRODUCTION Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, with application in both commercial and military communication systems. In civilian systems, MC is specially applied in software defined radio (SDR), to cope with the variety of communication systems. In military communication systems, advanced techniques are required for real-time signal interception and processing, which are vital for decisions involving electronic warfare operations and other tactical actions. This has emerged the need for smart receivers, which employ blind signal processing techniques. A major task of such systems is the blind recognition of the modulation of an incoming signal. MC is a challenging problem, particularly in a non-cooperative environment, since in addition to complex channels, there are many unknown parameters, such as carrier frequency, symbol timing, etc., that have to be extracted from the received signal. The design of a classifier essentially involves two steps: preprocessing of the incoming signal and proper selection of the classification algorithm. Preprocessing tasks can include noise removal, estimation of carrier frequency, symbol period, signal and noise powers, equalization, etc. The required preprocessing and its accuracy depend on the classification algorithm chosen in the second step. For the classification part there are two general approaches: likelihood-based (LB) (1)- (8) and feature-based (FB) methods (9)- (20). A good classifier has to provide a high correct classification rate for a large range of signal-to-noise ratio (SNR), in a short observation interval. In addition, it needs to fulfill these requirements: robustness with respect to (w.r.t.) preprocessing inaccuracy, capability to recognize a large number of modulations in environments with different propagation characteristics, real-time functionality, and low computational complexity. A classifier should either decide what modulation has been received, out of mod N equally likely candidates, or that the modulation cannot be recognized as one of the catalog. The former case is considered here, as being the only treated in the literature. Due to the lack of space, we focus on classification algorithms for phase shift keying (PSK), quadrature amplitude modulations (QAM), amplitude shift keying (ASK), and frequency shift keying (FSK). The rest of the paper is organized as follows. A general signal model is introduced in Section II. Algorithms derived within the LB and FB frameworks are presented in Section III and Section IV, respectively. Numerical results are provided in Section V, and conclusions are finally drawn in Section VI.

The relationship between side friction factor and longitudinal grade is investigated for horizontal curves considered in three dimensions, and new models for estimating side friction factors for different vehicle types are presented. The study consists of three parts: (1) a series of simulation tests using the CarSim and TruckSim multi-body simulation software packages; (2) multiplex regression analysis; and (3) presentation of recommended formulas for the side friction factor. Two different driving behaviors are considered in the simulation process: in one, the driver negotiates the curve at constant speed; in the other, the driver brakes while passing downgrades. Side friction factors are also studied separately for each axle of a vehicle. Greater side friction factors are found on downgrades for all vehicle types when cornering. From the viewpoint of skidding, the most critical situation is found for the rear axle of a sedan. In addition, braking is found to have a significant effect on side friction factors. The results and models from this study can be used in the geometric design phase by highway engineers to design safer roads.