esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Bioinformatics Analysis  
The ORF of _Cp_GPPS/GGPPSs were identified using the online tool 'ORF finder' ([[http://www.ncbi.nlm.nih.gov/gorf/gorf.html](http://www.ncbi.nlm.nih.gov/gorf/gorf.html)]([http://www.ncbi.nlm.nih.gov/gorf/gorf.html](http://www.ncbi.nlm.nih.gov/gorf/gorf.html))). Molecular weight and pI values of the putative proteins were predicted with the ExPASy tool ([[http://web.expasy.org/compute_pi/](http://web.expasy.org/compute_pi/)]([http://web.expasy.org/compute_pi/](http://web.expasy.org/compute_pi/))). The NCBI conserved domain tool ([[https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi](https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi)]([https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi](https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi))) was used to identify the functional domains. TargetP 1.1 server ([[http://www.cbs.dtu.dk/services/TargetP/](http://www.cbs.dtu.dk/services/TargetP/)]([http://www.cbs.dtu.dk/services/TargetP/](http://www.cbs.dtu.dk/services/TargetP/))), ChloroP ([[http://www.cbs.dtu.dk/services/ChloroP/](http://www.cbs.dtu.dk/services/ChloroP/)]([http://www.cbs.dtu.dk/services/ChloroP/](http://www.cbs.dtu.dk/services/ChloroP/))) and PSORT ([[http://psort.hgc.jp/](http://psort.hgc.jp/)]([http://psort.hgc.jp/](http://psort.hgc.jp/))) systems were used to predict the signal peptides and subcellular localization. Multiple sequence alignment of _Cp_GPPS/GGPPS sequences, as well as the phylogenetic tree, was constructed using the MEGA 7 software, following the neighbor-joining method [35]. Gene structure was predicted using an online GSDS tool ([[http://gsds.cbi.pku.edu.cn/](http://gsds.cbi.pku.edu.cn/)]([http://gsds.cbi.pku.edu.cn/](http://gsds.cbi.pku.edu.cn/)); [PERSON], et al. [36]).
# Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks
## 3 Improved Road Segment-Based Geographical Routing Protocol
### Segment Formation  
The proposed routing protocol is based on the routing parameters of the distance, direction, link quality, and traffic density of the networks. When the source node finds the destination out of its direct communication range, the protocol divides the forwarding path into small road segments with the help of the location information of vehicles and the road map information of the nearby area. Each vehicle is considered to be equipped with an OBU and a digital road map of the nearby area. It comprises street-level maps and precise traffic statistics. In Figure 1, a trajectory model of the ISR protocol is presented, focusing on intersection and segment formation. Here, segments are identified  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**S/N**} & **Protocol** & **Year** & **Direction** & **Position** & **Link** & **Velocity** & **Distance** & **Road** & **Traffic Density** \\  & **Name** & & & & & & & & **Topology** & **Density** \\ \hline
1 & MLARI [17] & 2015 & \(\times\) & & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
2 & EBECM [18] & 2015 & & & \(\times\) & & \(\times\) & \(\times\) & \(\times\) \\
3 & FGRP [19] & 2016 & & \(\times\) & \(\times\) & \(\times\) & & \(\times\) & \(\times\) \\
4 & AODV-R [20] & 2016 & \(\times\) & & & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
5 & FGR [21] & 2016 & & & \(\times\) & & \(\times\) & \(\times\) & \(\times\) \\
6 & SRPE [22] & 2016 & & & \(\times\) & & \(\times\) & \(\times\) & \(\times\) \\
7 & AMGR [23] & 2017 & \(\times\) & & & \(\times\) & \(\times\) & \(\times\) & \\
9 & TASR [24] & 2018 & \(\times\) & & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \\
10 & VBRP [25] & 2018 & & & \(\times\) & & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for geographical routing protocols. PGRP: perception-based geographical routing protocol; AODV-R: ad hoc demand distance vector reliability; AMGR: analytical hierarchical process (AHP)-based multi-metric geographical routing; TASR: traffic-aware segment-based routing; and VBRP: virtual backbone routing protocol.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline \(s\), \(d\) & IDs of the source nodes and destination nodes \\ \(S_{net}\) & & Next Segment \\ \(HN_{i}\) & Head Nodes for segment \(i\) \\ \(NSH_{i}\) & Next segment Head Node where \(i\) = 1, 2, 3,..., \(n\) \\ \(S_{s}\) & Segment where source \(s\) is located \\ \(S_{d}\) & Segment where destination \(d\) is located \\ \(RD\) & Route Discovery packet \\ \(n_{i}\) & Intermediate node \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations with description.  
by considering the road trajectory and the number of intersections from source to destination in the vehicular network.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 6 Turbulent Combustion Simulation Approach
### CFD with Different Combustion Models  
In the prediction of nitrogen oxides, in addition to direct numerical methods, other CFD approaches must introduce turbulence models, combustion models, and NOx generation models. The introduction of turbulence models is typically aimed at resolving the closure problem in the Navier-Stokes equations arising from the Reynolds stress term.  
The combustion model correlates the fuel reaction rate with the flow dynamics. Owing to the presence of turbulent fluctuation terms, the calculation of chemical reaction rates demonstrates non-linearity. Specifically, the average chemical reaction rate calculated using mean values (including temperature, reactant concentration, and density) does not equate to the average chemical reaction rate. Turbulent combustion models are required to provide closure for the mean reaction rates in the RANS species transport equations [45]. Hence, the inclusion of a combustion model is imperative for accurately determining the reaction rate of chemical reactions.  
Typical combustion models encompass a range of approaches, including the eddy breakup (EBU) model, eddy dissipation model (EDM), eddy dissipation concept (EDC) model, conditional moment closure (CMC) model, probability density function (PDF) model, flame-let model (FLM) model, etc.  
Various combustion models are based on specific assumptions and applicable ranges and could be categorized according to whether or not they can incorporate finite reaction rates. The EBU, EDM, steady laminar flame-let model, etc., were built as the fundamental assumption of the fast chemistry model. Because of the slow chemistry process of NOx generation, the NOx could not be calculated directly by these combustion models. Hence, the NOx approach of these combustion models would mainly have relied on the steady-state hypothesis calculation method. And the EDC, the composition PDF model, could calculate the finite-rate chemistry model. Therefore, they could approach the NOx with combustion models themselves. Based on the above content, whether these combustion models are compatible with finite reaction rates determines the approach of NOx models. The approach of NOx models was already discussed in Section 3.
## 2 Overview of SG Operations for Smart Manufacturing
### Demand Side Management through SGs  
With a limited power supply, the demand side uses SG technologies to calculate, manage, and allocate power usage for various manufacturing and operational needs. The previous works on demand side management strategies are reviewed below. [PERSON] et al. [16] combined the intelligent industrial power framework with manufacturing machines to collect their power consumption data, and carried out demand management according to the actual needs of consumers. [PERSON] et al. [17] proposed machine learning algorithms and visual sensor network approaches to forecast wind power generation in SGs to improve their performance and efficiency. When facing the demand of dynamic power changes, under the SG following the cyber-physical system model, [PERSON] et al. [18] proposed a multi-directional long-short-term memory to establish a stable predictive technology to predict the stability of the SG network, which is more effective than conventional machine learning models. For renewable energy incorporated into the SG, [PERSON] et al. [19] proposed a five-step method based on the energy Internet to collect big data for predicting the stability of SG. To manage and reduce the overload of the SG system, [PERSON] et al. [20] adopted AI and optimization strategies to establish an effective demand-side decision-making management system to effectively control energy costs.
## 3 Case Analysis
### Prediction Accuracy with Factor Ablation
#### 3.2.3 Test Site GPP without EVI  
In this experiment, all EVI data were deleted from the training data, which were then trained for each site flux. The final prediction results are shown in Figure 11. Compared to the prediction results without any feature ablation of the training data, the prediction accuracy of the AR site is still significantly improved and higher than that after removing Par but lower than that after removing Ta. The prediction accuracy of the DXST, DXSW, NMC, and ZF sites is similar to that after the removal of Ta and Par, with a sharp decrease.  
Figure 10: Predicted GPP vs. labeled GPP at a single site (no PAR).  
The decrease is larger for the NMC and ZF sites than for the others. The prediction accuracy of the GL site shows a continuous decline compared to that after the removal of Ta and Par. The prediction accuracy of the HBKO, HBSH, and HBSW sites does not change noticeably.
## 1 Introduction  
Rock mass contains a large number of discontinuities, such as joints, faults, bedding planes, or any other planar defects. These discontinuities play a weakening role in the rock mass. Under low normal stress, rock mass behavior is controlled by sliding along existing discontinuities rather than failure of the intact rock material [1]. Thus, the shear behavior of rock joints plays a key role in the safety and stability of underground mining [2, 3, 4, 5, 6]. The roughness of rock joints is recognized as one of the most important parameters affecting mechanical behavior [7, 8, 9, 10, 11]. Thus, quantitative characterization of discontinuity roughness has recently become a research hotspot in the field of rock mechanics. In 1973, [PERSON] [12] first proposed a concept called the joint roughness coefficient (JRC) to describe the joint surface morphology. In 1977, [PERSON] and [PERSON] [13] proposeda method whereby the roughness of the joint specimen could be determined by visual comparison. This method was suggested by the International Society for Rock Mechanics (ISRM) [14] and is currently used in engineering applications. However, this method was proven to be subjective [15; 16] and affected by the experience of the engineer [17]. In order to quantify joint surface roughness objectively, a series of objective quantification methods by statistical parameters were gradually proposed. [PERSON] [18] first calculated the type-profiles' statistical parameters and correlated them with the JRC. Then, a series of statistical parameters (\(Z_{2}\), \(R_{P}\), \(SF\), \(Z_{2s}\), \(\theta_{\max}^{*}/(C+1)\)[19]) were proposed to describe joint roughness in two and three dimensions, and characterization formulas were established based on these statistical parameters. The statistical parameters and the formulas are shown in Table 1.  
With the development of scanning technology, three-dimensional (3D) statistical parameters were proposed. Based on the \"local apparent inclination asperity dip\", a 3D roughness parameter was proposed [32; 34; 35; 36]. In addition, the joint roughness was studied from the perspective of a power spectrum [37; 38]. Although increasing attention has been given to 3D characterization of fracture surface roughness in recent years, the characterization of linear profiles remains important for applications. The morphology of the joint specimen is complex and multi-scale [39; 40; 41; 42; 43]. The ISRM has divided the morphology into primary asperity (waviness) and secondary asperity (uneve
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 2 Background  
### IoT + AI = AloT  
IoT is a network of physical devices, actuators, interfaces, and connectivity. The basic components of the network are briefly described in the following:  
* _Sensors_ gather real-time data from the environment. They convert a physical phenomenon into a digital signal that a machine can elaborate on. The term IoT device is  
Figure 1: Organization of this SLR. The numbers refer to the sections.  
commonly used to denote a real-life device (e.g., a smart Light Emitting Diode (LED) bulb) which embeds a certain number of sensors.
* _Microcontroller units_ (MCU) process the data collected by the sensors. An MCU is an integrated circuit composed of memory, a processor, and input/output units.
* _Communication_ modules transmit (either in wireless or wired mode) the data over the network. The following are some wireless communication protocols that are widely used: Bluetooth, BLE (Bluetooth Low Energy), ZigBee, 6 LoWPAN, WiFi, 802.11 ax, radio-frequency identification (RFID), NFC (Near-Field Communication), LoRa (Long Range) (a Low-Power Wide-Area Network (LPWAN) technology), SigFox (a LPWAN solution), LTE (Long-Term Evolution, also called \"4G LTE\"), and NarrowBand IoT (NB-IoT). In ref. [10], they are described in some detail. A new entry in the protocol family is matter: an industry-unifying standard based on IP that facilitates communication across smart home appliances (e.g., lights, TV, and washing machines) and an application framework for home automation (www.buildwhmatter.com and [[https://csa-iot.org/all-solutions/matter/](https://csa-iot.org/all-solutions/matter/)]([https://csa-iot.org/all-solutions/matter/](https://csa-iot.org/all-solutions/matter/)) (accessed on 20 November 2024)). Matter is the output of a Connectivity Standards Alliance project started in 2019 together with relevant international firms (e.g., Amazon, Apple, Google, and Zigbee Alliances).
* _Cloud_ includes storage and servers needed for the processing of the sensed data.  
IoT devices sense huge amounts of data that need to be complemented with context. AIoT is the context [11; 12]. AI is beneficial for both real-time processing and post-event processing. The advantages that come from the adoption of AIoT technology have been reported for all IoT application domains, such as industry [13], retail [14], smart cities [15; 16], and healthcare [17; 18].
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks  
[PERSON]  
1  
Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy; [EMAIL_ADDRESS]  
1  
[PERSON]  
1  
Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy; [EMAIL_ADDRESS]  
1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/1009/100](https://www.mdpi.com/journals/pdf/1009/100)]([https://www.mdpi.com/journals/pdf/1009/100](https://www.mdpi.com/journals/pdf/1009/100))  
cases/documents that could exist in the literature) in order to stop before the resources are finished [17]. [PERSON] et al. [13] apply several state-of-the-art Query Performance Predictors to select the best query in the Systematic Reviews domain. They show how current Query Performance Prediction approaches perform poorly on this specific task. International evaluation campaigns have organized labs in order to study this problem in terms of the evaluation, through controlled simulation, of methods designed to achieve very high recall [18; 19]. The CLEF initiative ([[http://www.clef-initiative.eu](http://www.clef-initiative.eu)]([http://www.clef-initiative.eu](http://www.clef-initiative.eu)), accessed on 15 February 2021) has promoted the eHealth track since 2013 and, the CLEF 2018 eHealth Evaluation Lab Consumer Health Search (CHS) task [20] investigated the problem of building search engines that are robust to query variations to support information needs of health consumers.  
In this paper, we study an alternative formulation of the intent-aware metric proposed by [PERSON] et al. [21], in which the authors analyze a metric to estimate the amount of missing information for each query reformulation during a search session. Note that in [21] the authors do not propose an approach capable of predicting the recall of different formulations. Nevertheless, our perception is that, their approach can be easily adapted with good results also to the predictive task. In our case, our research goal is to understand whether a gain based approach can be used to predict the relative importance of each reformulation in terms of recall performance, in the context of Consumer Health Search where users need support for medical information needs.  
In this sense, with respect to [21], our contribution is two-fold:  
* we show that it is possible to apply the GAIN measure proposed in [21] to obtain a recall predictor over a set of formulations for the same topic;
* we furthermore show how to improve the results of such predictor by exploiting also the information obtained through the various formulations.  
The paper is organized as follows: in Section 2, we present the original gain metric, while in Section 3 we define our alternative version to predict the performance in a recall-oriented fashion. In Section 4, we discuss the experimental analysis and results; while in Section 5 we give our final remarks.
## 4 Applications of AR and VR in Dentistry
### AR and VR in Dental Implantology  
Many advances have been made in dentistry in regard to the replacement of missing teeth or teeth in the mandibular and maxillary arches of patients such as removable dentures, fixed dental prostheses, and dental implants. Dental implants have emerged as a suitable and preferable choice for many patients because of their high success rate and the long-term benefits associated with them [27; 32]. The introduction of AR technology in dental implants has significantly improved many procedures associated with the placement of implants. Initially, the AR surgical navigation technology was used to place implants using the retinal imaging display as the surgeon keeps his visual on the operative area, avoiding the need for the surgeon to turn away [33].  
In a study by [PERSON] et al. [12], it was found that the use of augmented reality resulted in higher accuracy and applicability for guided placement of dental implants as compared to the traditional two-dimensional navigational method. Such AR navigation systems help the surgeon to concentrate only on the site of the implant placement, thereby providing only useful information to the surgeon, which eventually reduces the cost and time of the procedure [34]. It is of prime importance that the location of the implants should be as accurate as possible because negligence in this step of implant placement could be one of the factors responsible for future implant failure.  
Moreover, one study used augmented reality-based dental implant placement to evaluate the virtual placement of the implant as compared to the actual prepared implant site created [27]. In this study, it was found that the use of augmented reality resulted in a significantly decreased deviation in implant placement from the actual planned site. Since AR navigation-based systems help the surgeon to accurately locate and place the dental implants, the accuracy of such procedures thereby increases as compared to the traditional methods.  
The placement of the dental implant is a surgical procedure, so with the help of VR technology, patients have an opportunity to get a detailed explanation of the treatment and what they will experience while the treatment is being performed. Complete information given to the patients with the help of VR makes them better prepared mentally.
## 2 Modern Physical Layer
### Acoustic Triggered Wake-Up Systems  
In normal operation, UWSN nodes alternate data communication and IDLEmodes. In an ideal scenario, nodes remain switched off, as long as no communication is requested, so minimal energy is consumed. However, synchronization based on clock sharing strategies presents an additional energy waste to the minimum required to transmit or receive [45]. Moreover, in an event-driven communication, which starts when some specific event is recognized, the wireless interface tends to remain active longer than is necessary to complete a packet transmission.  
The UWSN can save significant power when an acoustic-triggered wake-up (AT-WU) system is used. Using this technique, a node is able to react to certain acoustic signals, reactivating the node if needed. However, it requires specific hardware to perform this task. The results of simulations of simple link [46] and carrier sense multiple access (CSMA) MAC networks [47] are close to the optimal scenario. It is crucial that this new hardware dissipates ultra-low power, as it always remains active.  
Very little work has been done on asynchronous AT-WU, though there are various state-of-the-art commercial modems that implement this technology. The main references so far to the research of acoustic modems enhanced with AT-WU are the Wills modem [30] and ITACAmodem [48]. The power consumptions of these modems when waiting for an acoustic wake-up signal are \(500\)\(\upmu\)W and \(10\)\(\upmu\)W, respectively.
# PAM-\(N\)--Fundamental Limits in Chromatic Dispersive-Uncompensated Channels
## 6 Applications to Noisy Channels  
In the presence of noise, the \(\text{BER}>0\) even for \(\zeta<\zeta_{max}\). However, since Equation (20) is a measure of the eye opening, it can be used to calculate the channel's BER. For a dispersion-less PAM-\(N\) channel:  
\[BER\cong\frac{1}{\log_{2}M}\text{erfc}\left(\frac{\overset{-}{i}}{(N-1)\sqrt{2 \langle i^{2}\rangle}}\right) \tag{30}\]  
where \(\overset{-}{i}\) is the mean current and \(\langle i^{2}\rangle\) is the current noise variance (thermic, shot-noise, RIN, etc.). Now, due to dispersion, the relative eye-opening \((N-1)^{-1}\) is to worsen to (20), which is when \(G(\pi)=\pi/4\) receives a simpler form:  
\[\frac{\Delta I(\zeta)}{I_{0}}=\frac{1}{N-1}\bigg{[}1-N\sin^{2}\bigg{(}\frac{ \pi^{2}\zeta}{4}\bigg{)}\bigg{]} \tag{31}\]  
Consequently, since after detection the current is proportional to the optical intensity, the channel's BER can be estimated by:  
\[BER\cong\frac{1}{\log_{2}M}\text{erfc}\Bigg{(}\bigg{[}1-N\sin^{2}\bigg{(}\frac {\pi^{2}\zeta}{4}\bigg{)}\bigg{]}\frac{\overset{-}{i}}{(N-1)\sqrt{2\langle i^{ 2}\rangle}}\Bigg{)} \tag{32}\]  
Therefore, for a given noise level and a given fixed BER, the power penalty \(\eta\) (notice again that after detection the current is proportional to the optical power) is proportional to:  
\[\eta(0,\zeta)=\frac{\overset{-}{i}(\zeta)}{\overset{-}{i}(0)}=\bigg{[}1-N\sin ^{2}\bigg{(}\frac{\pi^{2}\zeta}{4}\bigg{)}\bigg{]}^{-1} \tag{33}\]  
Clearly, when \(\zeta\rightarrow\zeta_{max}\), Equation (33) diverges. More generally, the power penalty from the distance \(\zeta_{0}\) to \(\zeta\) is:  
\[\eta(\zeta_{0},\zeta)=\frac{\overset{-}{i}(\zeta_{2})}{\overset{-}{i}(\zeta_ {1})}=\frac{1-N\sin^{2}(\pi^{2}\zeta_{0}/4)}{1-N\sin^{2}(\pi^{2}\zeta/4)} \tag{34}\]and since the penalty is measured in dB, then for short distances it can be approximated by:  
\[10\log_{10}\eta(\zeta_{0},\zeta)\cong\frac{10}{\ln 10}N\frac{\pi^{4}}{16}\left( \zeta^{2}-\zeta_{0}^{2}\right)\cong 26.44N\left(\zeta^{2}-\zeta_{0}^{2}\right) \tag{35}\]  
In Figure 7, Equations (34) and (35) are plotted as a function of the fiber's length \(z\). The divergence at \(\zeta\rightarrow\zeta_{\max}\) is clearly shown. These simple expressions can be applied for any channel regardless of its noise level.