esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 1 Introduction
### Main Contributions  
In this paper, we consider a battery-powered transmitter using on-off keying (OOK) modulation and study its battery energy efficiency in terms of BECPB. OOK is chosen because it features less complexity and cost than other modulation schemes such as MPPM, MFSK and MQAM. Moreover, OOK has an inherent mechanism to save energy, in the sense that the transmitter does not need to transmit signal when launching '0'. Due to the above reasons, OOK is suitable for portable devices and remains a strong candidate for realistic applications in WSNs. Moreover, the transmitter may use one of two distinct types of batteries. The first has an IDC-based BUF, while the second has a MDC-based BUF motivated by the claim made in [15]. Note that the above two types of BUF are applicable for Li-ion batteries as introduced in [15]. To be more specific, our contributions are summarized as follows:  
* For each type of battery, the instantaneous battery power consumption is derived as a nonlinear function of the instantaneous battery discharge power and a battery characteristic parameter \(\gamma\). It is also shown that this battery characteristic parameter depends on inherent properties of the battery. With the battery discharge power fixed, the instantaneous battery power consumption still increases as this battery characteristic parameter increases.
* For each type of battery, a closed-form BECPB expression is derived under Rayleigh channel when a prescribed symbol error rate (SER) is guaranteed. To the best of the authors' knowledge, the BECPB for MDC-based BUF has not been reported in previous works.
* Theoretical analysis is made to show the impact of the battery characteristic parameter \(\gamma\), communication distance and bandwidth on the BECPB. To the best of the authors' knowledge, the above has not been studied in previous works.  
* The analytical analysis is corroborated by numerical experimental results.  
For readers' convenience, symbols and their physical meanings frequently used later are summarized in Table 1. Acronyms used in the paper are summarized in Table 2.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 3 Mathematical Analysis
### Estimation of an Optimal Number of Almost Blank Subframes (ABSs)
[CrossRef]
* [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON] Internet Traffic and Capacity Evaluation in UMTS Downlink. In Proceedings of the Future Generation Communication and Networking (FGCN), Jeju-Island, Korea, 6-8 December 2007; pp. 547-552. * European Telecommunications Standards Institute (ETSI) (2019) European Telecommunications Standards Institute (ETSI); Universal mobile telecommunications system (UMTS). _Selection Procedures for the Choice of Radio Transmission Technologies of the UMTS_; TR 101 112, UMTS 30.03, ver. 3.2.0.; European Telecommunications Standards Institute (ETSI): Sophia Antipolis Valbonne, France, 1998-2004. * E-UTRA (2007) E-UTRA. _Radio Frequency (RF) System Scenarios_; Document 3 GPP TR 36.942, V.1.2.0; 3 rd Generation Partnership Project (3 GPP): Sophia Antipolis Valbonne, France, 2007. * Simulation Assumptions and Parameters for FDD HeNB RF Requirements (2009) Simulation Assumptions and Parameters for FDD HeNB RF Requirements; Document TSG RAN WG4 (Radio) Meeting #51, R4-092042; 3 rd Generation Partnership Project (3 GPP): Sophia Antipolis Valbonne, France, 2009. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Cellular Architecture and Key Technologies for 5G Wireless Communication Networks. _IEEE Commun. Mag._**2014**, _52_, 122-130. [CrossRef]
* [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON] Carrier Aggregation for LTE-Advanced Mobile Communication Systems. _IEEE Commun. Mag._**2010**, _48_, 88-93. [CrossRef]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _How Much Energy is Needed to Run a Wireless Network? Green Radio Communication Networks_; Cambridge University Press: Cambridge, UK, 2012; p. 20. * [PERSON] (2019) [PERSON] Realization of Licensed/Unlicensed Spectrum Sharing Using eICIC in Indoor Small Cells for High Spectral and Energy Efficiencies of 5G Networks. _Energies_**2019**, _12_, 2828. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON], R.K.; [PERSON] A Novel Frequency Reuse Technique for In-Building Small Cells in Dense Heterogeneous Networks. _IEEJ Trans. Electr. Electron. Eng._**2018**, _13_, 98-111. [CrossRef]
* [PERSON] and [PERSON] (2017) [PERSON], R.K.; [PERSON] Incentive and Architecture of Multi-Band Enabled Small Cell and UE for Up-/Down-Link and Control-/User-Plane Splitting for 5G Mobile Networks. _Freq. J. RF Eng. Telecomm._**2017**, _71_, 95-118. [CrossRef]
## 1 Introduction
[22] utilized the Close Proximity method (CPX) to compare the noise data of EAC (maximum aggregate size: 8 mm) and SMA-11 asphalt pavements at various time intervals, revealing that both pavement types exhibited similar noise levels and followed a logarithmic variation pattern over their lifetime. In summary, current research on EAC has predominantly focused on the impacts of surface treatment techniques, texture characteristics, coarse aggregate composition, and exposure patterns on its SRNR performance, while studies concerning the formation mechanisms of surface texture characteristics and optimized mix design that comprehensively considers factors including water-binder ratio, sand ratio, coarse aggregate composition, and dosage have remained comparatively scarce in existing literature. To address this gap, the present study employed an orthogonal experimental method to prepare EAC samples with varying mix proportions and systematically investigated the effects of four mix design parameters--water-binder ratio, sand ratio, coarse aggregate volume ratio, and proportion of aggregates > 9.5 mm--on surface texture characteristics, SRNR performance,and mechanical properties. Based on these analyses, the optimal mix proportions for EAC with enhanced SRNR capabilities are proposed, thereby providing a scientific basis for the application of EAC with high SRNR performance in pavement engineering.
# Primary User Localization Algorithm Based on Compressive Sensing in Cognitive Radio Networks
###### Abstract  
In order to locate source signal more accurately in authorized frequency bands, a novel primary user localization algorithm based on compressive sensing (PU-CSL) in cognitive radio networks (CRNs) is proposed in this paper. In comparison to existing centroid locating algorithms, PU-CSL shows higher locating accuracy for integrally exploring correlation between source signal and secondary users (SUs). Energy detection is first adopted for collecting the energy fingerprint of source signal at each SU, then degree of correlation between source signal and SUs is reconstructed based on compressive sensing (CS), which determines weights of centroid coordinates. A weighted centroid scheme is finally utilized to estimate source position. Simulation results show that PU-CSL has smaller maximum error of positioning and root-mean-square error. Moreover, the proposed PU-CSL algorithm possess excellent location accuracy and strong anti-noise performance.  
c pacs: 22 January 2016; Accepted: 11 April 2016; Published: 14 April 2016
## 4 Verification of the Method
### Assessment of the Reliability of the Cargo Handling Process
\(SH_{2_{1}}\)= 0.92, \(SFH_{2_{2}}\)= 0.08, \(FH_{2_{3}}\)= 0.00  
\(SH_{3_{1}}\)= 0.31, \(SFH_{3_{2}}\)= 0.02, \(FH_{3_{3}}\)= 0.00, \(SH_{3_{4}}\)= 0.03, \(SFH_{3_{3}}\)= 0.07, \(FH_{3_{6}}\)= 0.00, \(FH_{3_{7}}\)= 0.00, \(SH_{3_{8}}\)= 0.02, \(SFH_{3_{9}}\)= 0.04, \(FH_{3_{10}}\)= 0.00, \(SH_{3_{11}}\)= 0.06, \(SFH_{3_{12}}\)= 0.00, \(FH_{3_{13}}\)= 0.00, \(FH_{3_{14}}\)= 0.00, \(FH_{3_{15}}\)= 0.01. Reliability for success is equal to:  
\(R_{pr(S)}=SH_{1_{1}}*SH_{2_{1}}*\left(SH_{3_{1}}+SH_{3_{4}}+SH_{3_{8}}+SH_{3_{ 11}}\right)=0.93*0.92*0.93=0.79\)  
Figure 12: Event tree for activity (orders sent by the ship’s owner \(z_{1}\)). Figure 13: Event tree for activity (Sending the advice note list \(z_{2}\)). Reliability for success is equal to \(R_{pr}(S)=0.79\), which is lower than the one obtained using the event trees for the two modes, which was equal to \(R_{pr}=0.98\). This is due to the fact that the timely execution of the operations performed within the activities was considered. It also follows that the activities at the terminal are carried out, but a portion of them are delayed. The activity \(d_{3}\) and, more specifically, the operation of accepting the train at terminal \(b_{3}\) has the greatest impact on the reliability, i.e., the timely execution of the operating process. This activity determines whether the cargo handling process will be performed in a timely manner and whether the cargo will be delivered to the customer exactly on time. Train delays are up to 48 h, and the most critical delays are already above 6 h; therefore, it is of key importance for the proper operation of the terminal. In order to compare the value obtained with the developed method for the cargo handling process, the global value for delays in delivery to the customer from the container terminal was obtained. According to the data received from the terminal manager, 82% of haulage is performed on time in accordance with the customer's requirements. Based on the above, it can be determined that the reliability value of the cargo handling process for the three-state event tree amounting to 0.79 is a more accurate value, which suggests the validity of the developed method. It is also worth remembering that the method utilizes delay thresholds, which, following the identification of the bottlenecks within the process, will allow to improve the timeliness of the process or avoid penalties for delays.
## 4 Results and Discussion
### Farm-to-Retailer Environmental Profile of Dry Lentils
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**IC\({}_{\text{NWj}}\)** & \multicolumn{3}{c}{**ML**} & \multicolumn{3}{c}{**OL**} \\  & **(μPt/kg)** & **(\%)** & **(μP/kg)** & **(\%)** \\ \hline CC & 89.8 & \(\pm\) & 13.5 & 18.6 & 59.6 & \(\pm\) & 5.0 & 21.1 \\ OD & 0.08 & \(\pm\) & 0.01 & 0.02 & 0.04 & \(\pm\) & 0.00 & 0.01 \\ IR & 1.0 & \(\pm\) & 0.2 & 0.2 & 0.89 & \(\pm\) & 0.03 & 0.3 \\ PhOF & 17.8 & \(\pm\) & 2.7 & 3.7 & 14.9 & \(\pm\) & 1.4 & 5.3 \\ PM & 27.4 & \(\pm\) & 4.7 & 5.7 & 25.7 & \(\pm\) & 2.4 & 9.1 \\ A & 20.0 & \(\pm\) & 2.6 & 4.1 & 10.8 & \(\pm\) & 1.0 & 3.8 \\ FWE & 8.8 & \(\pm\) & 0.8 & 1.8 & 2.9 & \(\pm\) & 0.1 & 1.0 \\ ME & 6.7 & \(\pm\) & 1.1 & 1.4 & 5.7 & \(\pm\) & 0.6 & 2.0 \\ TE & 10.8 & \(\pm\) & 1.8 & 2.2 & 8.7 & \(\pm\) & 0.9 & 3.1 \\ ETFW & 43.3 & \(\pm\) & 10.0 & 8.9 & 24.3 & \(\pm\) & 3.4 & 8.6 \\ C-HT & 2.3 & \(\pm\) & 0.3 & 0.5 & 1.8 & \(\pm\) & 0.1 & 0.6 \\ NC-HT & 5.2 & \(\pm\) & 0.8 & 1.1 & 3.1 & \(\pm\) & 0.3 & 1.1 \\ LU & 227.2 & \(\pm\) & 53.6 & 46.9 & 105.7 & \(\pm\) & 13.6 & 37.5 \\ WU & 1.0 & \(\pm\) & 0.1 & 0.2 & 1.41 & \(\pm\) & 0.03 & 0.5 \\ RUF & 5.5 & \(\pm\) & 1.4 & 1.1 & 7.0 & \(\pm\) & 1.0 & 2.5 \\ RUMM & 17.3 & \(\pm\) & 2.8 & 3.6 & 9.4 & \(\pm\) & 0.5 & 3.3 \\ PEF & 484.2 & \(\pm\) & 95.3 & 100.0 & 282.1 & \(\pm\) & 29.2 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: End-point farm-to-retailer environmental characterization for 1 kg of dried ML and OL grains according to the PEF standard method: mean value and standard deviation of normalized and weighted impact categories (IC\({}_{\text{NWj}}\)) and corresponding percentage contribution to the Product Environmental Footprint score (PEF). the processing facility, which is significantly higher than the 15-40% range for OL grains (see Table 2). The higher volume of cleaning waste for ML grains is largely attributed to adverse growing conditions, such as high temperatures and drought, which can lead to a higher proportion of empty seeds and other defects.
## 2 Materials and Methods
### Skin Irritation Test  
To verify that topical application would not cause an irritation to the skin, an epidermal tissue was used to mimic a scenario where R-PE was applied on the tissue level. An epidermal irritation assessment is a key test for any medical device or cosmetic product, so that only products considered safe to the skin would be provided to consumers. Traditional animal tests have been reported to not only cause pain and discomfort for test animals, but such testing has also not always been representative of the effects observed in humans; thus, it is regarded that using a reconstructed bionic tissue is advantageous as they possess numerous cell types surrounded by a local microenvironment, simulating human skin in vivo [34]. Effects of R-PE on skin irritation were evaluated by using the bionic epidermal tissue EpiDerm(tm) (MatTek LIFE SCIENCES, Ashland, MA, USA) according to manufacturer's instructions and with experiments following the methodology modified from references [35; 36; 37]. The inserts containing the EpiDerm sample were inserted into a 6-well plate (Corning, New York, NY, USA) containing pre-warmed DMEM. An amount of 100 uL DMEM containing either 0 (negative control) or 1 mg/mL purified R-PE (final concentration 0.1 mg/mL) was added into the cell culture insert atop the EpiDerm sample. Tissue that was treated with 5% sodium dodecyl sulfate (SDS) served as a positive control. After 1-h incubation in a humidified environment of 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) incubator, the inserts were removed and were washed twice with PBS (pH 7.4), followed by placement into a 24-well plate containing 300 uL of a 1 mg/mL MTT solution (in DMEM). After 3-h incubation, isopropanol was applied to dissolve MTT formazan crystals. The supernatant was transferred to a 96-well plate, and the sample measured at an OD of 570 nm using spectrophotometry. Relative viability was calculated following the equation described in Section 2.4. Chemicals are considered irritants when they decrease cell viability to less than 50% (Category 2), and chemicals that result in cell viabilities of greater than 50% are considered to be non-irritants (No Category) [37].
# A Novel Ultrasonic Cavitation Peening Approach Assisted by Water Jet
## 4 Results and Discussion  
### Impact Force Caused by Cavitation Bubbles  
The effect of different standoff distances and different vibration amplitudes on impact force is investigated in the following section, as the impact force caused by the collapse of cavitation bubble is an evaluation factor for this novel ultrasonic cavitation peening approach. When the cavitation bubbles occur in the small gap, the workpiece surface is impacted by the impulse of cavitation collapse. Thus, the voltage signal by the force sensor can be recorded to obtain the load peaks which are presumably due to the impulse. The load peaks are corresponded to the impact amplitude. More violent cavitation collapses mean higher load peaks. In Figure 3 the frequency distribution of load peak are compared at different vibration amplitudes (20 \(\upmu\)m, 25 \(\upmu\)m, 30 \(\upmu\)m) and standoff distances (0.2 mm, 0.6 mm, 1.0 mm). The horizontal axis gives the amplitude in volts whereas the vertical axis gives the number of the voltage peaks. The higher vibration amplitude leads to higher impact loads and the counts of larger impact load are much less than that of small impact loads. Since the standoff distances are less than 1 mm, the affected areas of the specimens by the cavitation bubbles are slightly different. This phenomenon can be observed through the treated specimen. The large impacts have much more influence on the treated surface, as with smaller impacts it is difficult to generate the plastic deformation which is beneficial for the treated surfaces. Thus, we mainly focus on the effects of large impact in the following measurements. The vibration amplitude of sonotrode becomes larger, which results in the increase of sound pressure. Thus, the cavitation bubbles generated in the gap are larger at larger vibration amplitude than that at smaller vibration amplitude. At the same time, the impacts caused by cavitation bubbles become larger.  
It can also be seen that the variations of the standoff distance are very sensitive to the distribution of the peaks. In Figure 3, at the vibration amplitude of 20 \(\upmu\)m, the impact loads at the standoff distance of 0.6 mm is the highest while that at the standoff distance of 0.1 mm is the lowest. The highest value of peak height reaches to about 1.8 V while the lowest value is about 0.5 V. It can be deduced that the impact loads increase to a peak with the increase of standoff distance and then decrease again. There is the same tendency at the vibration amplitudes of 25 \(\upmu\)m and 30 \(\upmu\)m. This is because the highest impact load occurs at the standoff distance which equals the maximum diameter of the cavitation bubble. At the same vibration amplitude, the dynamics of cavitation bubbles are limited by the smaller gap. However, with increase the standoff distance, the bubble size near the treated surface becomes smaller due to the attenuation.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 4 The Proposed Protocol  
In this section, we describe our MEC protocol for efficient data dissemination in vehicular networks. Our protocol supports the two major classes of vehicular networking applications, which are traffic safety and travel convenience. Traffic safety applications require high data coverage with low latencies, whereas travel convenience applications require efficient data delivery and retrieval. These applications are often referred to as delay-tolerant applications. Examples include travel time estimation, speed expectation and congestion detection.
## 2 System Model  
The aim of this study is to determine the closed-form equation of the electromagnetic field exposure in a 5G massive MIMO cell emitting in the mmWave band. The base stations are modeled as points of a stationary and isotropic Poisson point process \(\Psi_{BS}\) with density \(\lambda_{BS}\). The locations of said points are denoted by \(x\in\Psi_{BS}\subseteq\mathbb{R}^{2}\). The MTs are distributed uniformly and independently in a bounded region of \(\mathbb{R}^{2}\) and thus, in addition to the assumption on the BS distribution, we can generalize the distribution of the exposure over the whole cell area and the framework can be developed for a typical MT denoted by MT\({}_{0}\) located at the origin without losing generality. To investigate the effect of the network load on the exposure, we assume that the BSs emit with probability \(\alpha\), i.e., at a certain time-frame the number of active MTs is less than the number of base stations \(\lambda_{MT}^{active}\leq\lambda_{BS}\), which means that the BSs will have an effective density of \(\lambda_{eff}=\alpha\ \lambda_{BS}\). Since line-of-sight (LOS) paths are the highest contributors to the exposure, we assume that all the paths between the BS and the MT are LOS paths. The BSs will emit with a constant power denoted by \(P_{tx}\). We assume a 5G massive MIMO network where the BSs are composed of antenna arrays with identical structure, and thus identical radiation patterns. The BS is assumed as single user MIMO, and it emits towards one MT in a single time slot. We assume no downlink power control exist in the network, which seems to be the case for 5G networks [26], so every MT is being allocated the whole power resource for each transmission slot. We implement the channel model NYUSIM, developed by New York University Wireless Group [11] which divides the channel power into clusters and paths between the BS and the MT. The received power \(P_{rx}^{BS_{i}}\) at each MT is dependent on the transmitted power, the propagation distance, the channel gain, and the antenna gain. The global exposure \(P_{rx}^{t}\) can then be defined as \(P_{rx}^{t}=\sum_{BS_{i}\in\Psi_{BS}}P_{rx}^{BS_{i}}\) and it can be rewritten as  
\[P_{rx}^{t}=\sum_{BS_{i}\in\Psi_{BS}}P_{tx}H_{i}G_{i}L_{i}, \tag{1}\]  
where \(H_{i}\), \(G_{i}\), and \(\mathrm{L}_{i}\) are the link's channel gain, antenna gain, and path loss, for the link between the MT to the \(i\)th BS, respectively. To better analyze the distribution of total received power \(P_{rx}^{t}\), we focus on studying the CDF of total received power, which is defined as  
\[F_{P_{rx}^{t}}(x)=\Pr\{P_{rx}^{t}\leq x\},x\in[0,+\infty) \tag{2}\]  
In the following subsections, we describe the system model, including the antenna array gain, the channel gain, and derive the expression to compute the CDF of the exposure of the assumed network.
## 4 Hardware Circuit Design  
### Thermoelectric Module Specifications  
The thermoelectric generator (TG), namedTG12-8, is manufactured by Marlow Industries, Inc. Since this converter has two inputs, the first input is composed of four pieces of TGs connected in series, and the second input is composed of two pieces of TGs connected in series. Table 2 shows the associated information on the first thermoelectric module and Table 3 shows the associated information on the second thermoelectric module. Figures 19 and 20 show the measured curves for the first and second thermoelectric modules, respectively. These measured curves for the two thermoelectric modules are based on the Prodigit 9842-MPPT function under constant voltage mode, sweeping so as to obtain the data relevant to power vs. voltage (PV) and voltage vs. voltage (IV), and then plotting these data by using Microsoft Excel.  
As can be seen from Figure 19, the maximum power point of the first TG has 18.52 W at half of the open-circuit voltage and short-circuit current of the first thermoelectric module, with values of \(V_{mpp1}\) = 16.22 V and \(I_{mpp1}\) = 1.145 A. As can be seen from Figure 20, the maximum power point of the second thermoelectric module has 9W at half of the open-circuit voltage and short-circuit current of the second thermoelectric module, with values of \(V_{mpp2}\) = 7.67 V and \(I_{mpp2}\) = 1.177 A.  
\begin{table}
\begin{tabular}{c c} \hline
**Second Thermoelectric Module Specification** & **Name or Value** \\ \hline TG product name & TG12-8 \\ \hline TG size & \(40\times 40\times 3.2\) mm \\ \hline Number of TGs & 2 in series \\ \hline Max. Power @ MPP (\(P_{mpp2}\)) & 9 W \\ \hline Voltage @ MPP (\(V_{mpp2}\)) & 7.67 V \\ \hline Current @ MPP (\(I_{mpp2}\)) & 1.177 A \\ \hline Open-circuit voltage (\(V_{\alpha 2}\)) & 14.07 V \\ \hline Short-circuit current (\(I_{sc2}\)) & 2.46 A \\ \hline Cold-side temperature & 80 \({}^{\circ}\)C \\ \hline Hot-side temperature & 250 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 3: Specifications of the second thermoelectric module.  
Figure 19: Curves for the first thermoelectric module: (1) blue line for P-V; (2) orange line for I-V.  
Figure 20: Curves for the second thermoelectric module: (1) blue line for P-V; (2) orange line for I-V.
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 4 Numerical Results
It is noted that we achieve the performance gaps from (11a) and (11b). In particular, the total achievable rate of \(x_{1}\) is considered as the achievable rate transmitted in the fixed DF relaying scheme and the one directed transmitted from \(S\) to \(D\). It is clear that the achievable rate in our considered system is enhanced thanks to the deployment of PS WSN-NOMA protocol compared to the existing PS-OMA respectively discussed in [30] and CRS-NOMA in [31]. Unlike PS-OMA and CRS-NOMA which only have the relay transmitting the decoded symbol to the destination in the second time slot, our system deploys SIC technique in which \(S\) can transmit symbols to \(D\), and \(D\) can use SIC technique to decode the data symbols.