esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Precise Orbit Determination Strategy  
The Position and Navigation Data Analyst (PANDA) Software [14,15] developed at the GNSS Research Center in Wuhan University is adapted for BeiDou data analysis for this study. The processing strategy including observation modeling, parameterization and satellite dynamic models, and processing procedure are discussed in this section.
## 4 The 5G Use Cases and Spectrum Requirements
### The 5G Applications and the Recommended Frequency Bands  
The 5G technology provides several services across various fields of human endeavor. The research communities and other stakeholders have been looking into all of the options for a quick and seamless implementation of the enormous 5G services. Spectrum options, service needs, various access mechanisms, novel waveforms, software-defined networking, and the potential use of massive MIMO are key 5G issues that are still being investigated for efficient technology integration [56]. The spectrum for 5G must be carefully chosen to meet a variety of demands, including those for high data rates, high reliability, the Internet of Things (IoT), and low-latency communication [62].  
The three generic use cases supported by the 5G system will require different spectrum specifications to be delivered effectively. To fully serve these various UCs, 5G must use either the lower Frequency Range 1, often called the sub-6 GHz band, or the higher Frequency  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Key Performance Indicator** & **Enhanced Mobile Broadband (eMBB)** & \
\begin{tabular}{c} **Ultra-Reliable and Low-Latency** \\ **Communication (uRLLC)** \\ \end{tabular} &
\begin{tabular}{c} **Massive Machine-Type** \\ **Communication (mMTC)** \\ \end{tabular} \\ \hline Latency & Medium & High & Low \\ \hline Mobility & High & High & Low \\ \hline Connection Density & Medium & Low & High \\ \hline Spectrum Efficiency & High & Low & Low \\ \hline User Experienced Data Rate & High & Low & Low \\ \hline Peak Data Rate & High & Low & Low \\ \hline Network Energy Efficiency & High & Low & Medium \\ \hline Area Traffic Capacity & High & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 5: Important KPIs for 5G Generic Use Cases.  
Range 2, encompassing frequencies above 6 GHz. This specification was consistent with a report [63] on 5G spectrum allocation. Spectrum from the low-, mid-, and high-spectrum ranges will be required for 5G to meet the UCs' performance requirements [64]. Combining different spectrum bands will have several benefits and solve many transmission problems. In addition to high data rates, 5G must provide wide area and indoor-outdoor coverage. As a result, the 5 GY spectrum solution heavily emphasizes the band below 6 GHz. Over 1200 MHz of spectrum in the 694 MHz to 3800 MHz frequency range has been harmonized in Europe for mobile broadband [65]. However, lower frequencies provide better wireless communication coverage [66].  
Table 6 provides an appropriate frequency band for 5G applications based on their specific KPIs. More details on the 5G frequency ranges and the segmentation of the frequency bands into low, medium, and high bands are provided in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**ITU Usage** & **Main Service** & **Scenario-Specific KPI** & **Applications** & **Deployment Scenario** & **Proposed** \\
## 4 Experimentation and Results
### Experiments and Results
#### 4.2.2 Experimental Results  
Using two alternative assessment approaches, we split the data into training and testing sets and perform 10-fold cross-validation. This part concentrates on the SVM and TinyML algorithm findings.
## 4 Bayesian Estimation
### Loss Functions  
In this subsection, we will examine three loss functions utilized in Bayesian statistics to enhance our understanding of their mathematical properties. These include the Squared Error Loss function (SEL), the LINEX, and the Balanced Squared Error Loss function (BSEL).  
The function for SEL is as follows:  
\[L_{SEL}(\sigma,\theta)=(\sigma-\theta)^{2}, \tag{26}\]  
where \(\sigma\) is a Bayesian estimation of \(\theta\), and \(\sigma\) is calculated as \(\widehat{\sigma_{SEL}}=E(\theta|X)\).  
The SEL function is a conventional loss function; however, in many practical situations, its application without consideration for weighting or the differences between overestimation and underestimation can introduce bias into the results.  
In such instances, it may be beneficial to employ an asymmetric loss function. As discussed by [13], among the various options available, the LINEX loss function is often preferred, which is as follows:  
\[L_{LINEX}(\sigma,\theta)=\zeta\Big{(}e^{k}(\sigma-\theta)-k(\sigma-\theta)-1 \Big{)} \tag{27}\]  
It is important to note that we assume \(\zeta=1\) here without loss of generality. The equation includes the Bayesian estimate of \(\theta\), which is as follows:  
\[\widehat{\sigma_{LINEX}}=-\frac{1}{k}\ln E(e^{-k\theta}|X) \tag{28}\]  
In their work, ref. [21] introduced a balanced loss function that is commonly employed to integrate both goodness of fit and estimation accuracy into the evaluation of estimators. Let \(\hat{x}=(x_{1},\ldots,x_{s},x_{s+1},\ldots,x_{m})\) represent the observed data values of \(X=(X_{1},\ldots,X_{s},X_{s+1},\ldots,X_{m})\). The formulation of the BSEL function is described as follows:  
\[L_{BSEL}(\sigma,\theta)=\gamma\rho(\sigma,\sigma^{*})+(1-\gamma)\rho(\sigma, \theta), \tag{29}\]  
where \(0\leq\gamma\leq 1\). We will select \(\sigma^{*}\) as the maximum likelihood estimation of \(\theta\), and \(\rho\) represents the SEL function as defined above. We will compute the Bayesian estimation in accordance with BSEL, which is expressed as follows:  
\[\widehat{\sigma_{BSEL}}=(1-\gamma)E(\theta|X)+\gamma\sigma^{*}. \tag{30}\]  
Due to the variability of \(\gamma\), we are able to consider additional scenarios. For instance, in the context of the BSEL function under \(\rho\), when \(\sigma^{*}\) represents the maximum likelihood estimator of the parameter, if \(\gamma=1\), the Bayesian estimate coincides with the MLE. Conversely, when \(\gamma=0\), the Bayesian estimate is reduced to the Bayesian estimation utilizing the SEL function.
# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 3 The Proposed Approach
### Online Pipeline Section  
On the other hand, the online section is responsible for performing all the operations that are applied to the specific input image. It is allocated to quicker tasks, such as using trained Machine Learning models and performing image processing Classical filters to the input image. We also propose the introduction of a _comparison phase_ that guarantees the best output possible out of the multiple alterations computed during the pipeline processing. The comparison phase introduces some overhead in the pipeline. Moreover, we also propose the introduction of a _threshold phase_ that filters images in which quality can presumably be even more enhanced, from those considered to have such quality that is attempting to improve them even further may degrade their quality.  
Regarding the Machine Learning component, we were inclined to explore adversarial and convolutional networks as they have continuously indicated to perform well on image manipulation tasks. We focused on a recent architecture that performs image-to-image translation [21]. This component would help to subtly improve the overall image quality; thus, it is placed at the beginning of the pipeline as a way to provide a _head start_ for the following methods. More details on this will be presented in Section 3.5.  
Pseudo-Code in Algorithm 1 sums up the pipeline's online decision process, where _IQA_ represents a IQA function that returns a score based on the input image's quality; _ML  
Figure 2: Schematic of the proposed end-to-end IE pipeline architecture. The pipeline is divided in two main sections, _online_ and _offline_, containing the components computed in real-time over the input image, and the components independent from the input sample, respectively.  
represents a Machine Learning component in the pipeline; and _EC_ represents the set of Classical functions applied to an image that is returned by the Evolutionary component.  
```
Input: Input Image Output: Output Enhanced Image I = Input Image ifIQA(I) > IQA(ML(I))then ifIQA(I) > Quality Thresholdthen returnI ifIQA(I) > IQA(EC(I))then returnI else return EC(I) end else I = ML(I) ifIQA(I) > Quality Thresholdthen returnI end ifIQA(I) > IQA(EC(I))then returnI else return EC(I) end if
```  
**Algorithm 1**Overview of the pipeline's online decision process
# Extending the Coverage of IEEE 802.15.4z HRP UWB Ranging
## 4 Experimental Results and Discussion  
In Section 3, the link performance required in the preamble and DP was theoretically analyzed. In this section, we verify the theoretical results through experiments using an actual HRP UWB module. In the experiments, we verify the performance changes according to the changes in PHY parameters such as the PSR, PAC, and data rate (850 kbps versus 6.8 Mbps) and the possibility of longer ranging. In particular, we verified the following results examined in the theoretical analysis through this experiment. First, since the link margin of the preamble is theoretically higher than that of the DP, errors may occur in the DP of the packet at a long distance, but the preamble-based synchronization signal will still be able to be received. Second, low-speed data (850 kbps) will show about 9 dB of performance gain compared with high-speed data (6.8 Mbps), as analyzed in the theory. Third, the processing gain will be increased according to the increase in the PAC size and PSR length, which will improve the distance measurement accuracy and stability. To verify these three points, the experiment was conducted as follows.
## 3 Results and Discussion
### The Effect on the Biofilm Formation
The data shown represent a single biological replicate per strain. zinc nitrate microneedles successfully destroyed mature biofilms but also caused transient biofilm formation at low zinc concentrations [25]. Zinc at sub-inhibitory levels appears to control bacterial biofilm formation by disrupting quorum sensing and EPS synthesis. Zinc was found to suppress quorum sensing at high concentrations but trigger biofilm signaling at low concentrations [3]. Sub-lethal exposure to Zn\({}^{2+}\) may initiate a stress response that results in the overproduction of the EPS, leading to biofilm development. Similar effects were observed in _Proteus mirabilis_, where zinc oxide nanoparticles suppressed biofilm gene expression at inhibitory concentrations but stimulated protective biofilm responses at sub-inhibitory doses [26].
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### Recombinant Live Vaccines
#### 3.2.1 Tb/FLU-01l  
A phase I clinical trial, completed in 2015, investigated TB/FLU-01L, a vaccine comprising a replication-deficient recombinant influenza virus A expressing ESAT-6 antigen (ClinicalTrial.gov: NCT03017378). In this 42-day trial, consisting of 36 healthy male or female volunteers, aged 18-50 years old, the safety and systemic immune response elicited by the vaccine were assessed after intranasal (\(n=18\)), or sublingual (\(n=18\)), administration at day 1 and day 21 (two doses total) [21]. In both groups, seven days following the first dose, only two participants reported AEs, namely, nasal congestion and sneezing [21]. In the sublingually administered group, a further two participants reported pharynx hyperaemia [21]. Similarly, only one participant in each intervention group reported systemic AEs, being fever (sublingual vaccine group), and sore throat (nasal vaccine group) [21]. After the second dose (seven days), only one participant reported pharynx hyperaemia in the sublingual vaccine group [21]. In no case was shedding of the vaccine virus detected [21]. After analyzing the immunogenicity, we found that 72.2% of the subjects from the sublingual group, and 77.8% subjects from the immunized intranasal group, demonstrated detectable responses (defined as any cytokine response at any time point) [21].
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 3 Research Method
### Network Analysis for Item Classification
#### 3.3.2 Item Classification  
As mentioned in Section 1, not all items are suitable for rearrangement since established shopping behaviors might be disrupted if major attractive items are relocated. To determine item suitability for relocation, this study classifies each product item as an attraction item, an opportunity item, or a trivial item based on its centrality measure.  
**Definition 1**.: _An attraction item is an item whose centrality value is higher than an upper bound threshold value \(\alpha\) in PRN. Attraction items should not be relocated because doing so would disrupt their connection to other products. In the following discussion, the set of attraction items is denoted as \(AI=\left\{i_{j}\middle|C(j)>\alpha\right|\)where C(j) is the centrality for item \(i_{j}\)._  
**Definition 2**.: _An opportunity item is an item whose centrality value is no greater than \(\alpha\) but higher than a lower bound threshold value \(\beta\) in PRN. Opportunity items are commodities affiliated with attraction items and might sell better if moved to a more appropriate section/shelf. Therefore, opportunity items are considered to be commodities which should be re-allocated. The set of opportunity items is denoted as \(OI=\left\{i_{j}\middle|\alpha\geq C(j)>\beta\right\}\)._  
**Definition 3**.: _A trivial item is an item whose centrality value is no greater than \(\beta\). Trivial items are items which do not show a strong association with other items in PRN. Thus reallocating trivial items might not generate additional sales. The set of trivial items is denoted as \(TI=\left\{i_{j}\middle|\beta\geq C(j)\right\}\)._
_Algorithms_**2015**, \(8\), 621-631; doi:10.3390/a8030621  
_algorithmics_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**One-Bit Quantization and Distributed Detection with an Unknown Scale Parameter**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 3**  
\({}^{1}\) College of Information and Communication Engineering, Harbin Engineering University,  
No.145 Nangang District, Harbin 150001, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken,  
NJ 07030, USA; E-Mail: [PERSON]RESS]  
\({}^{3}\) National Key Laboratory on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China; E-Mail: [PERSON]ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-186-8481-6268; Fax: +86-451-8251-9800.  
Academic Editor: [PERSON]  
_Received: 23 June 2015 / Accepted: 6 August 2015 / Published: 11 August 2015_  
**Abstract:** We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, _i.e._, a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs).  
**Keywords:** decentralized detection; one-bit scalar quantization; wireless sensor networks (WSNs)
## Materials and Methods  
### Sample Preparation  
In the experiment, NiCo\({}_{2}\)O\({}_{4}\) nanostructures were synthesized by simple solvothermal and subsequent heat treatment. Nickel acetate tetrahydrate (C\({}_{4}\)H\({}_{6}\)NiO\({}_{4}\).4H\({}_{2}\)O) and cobalt acetate tetrahydrate (C\({}_{4}\)H\({}_{6}\)CoO\({}_{4}\).4H\({}_{2}\)O) were used as Ni source and Co source, respectively. Urea (CO(NH\({}_{2}\))\({}_{2}\)) was used as the mineralizer. Deionized water (H\({}_{2}\)O), ethanol, glycerol, and tetraethylene glycol (TEG) were used as reaction solvents. The typical synthesis process can be described as follows: 1 mmol C\({}_{4}\)H\({}_{6}\)NiO\({}_{4}\).4H\({}_{2}\)O and 2 mmol C\({}_{4}\)H\({}_{6}\)CoO\({}_{4}\).4H\({}_{2}\)O were dissolved in 40 mL prefabricated reaction solvents to form mixed solution of metal ions, then 7 mmol CO(NH\({}_{2}\))\({}_{2}\) was added to the mixed solution under vigorous stirring to form suspension. The suspension was sealed and transferred to a 60 mL Teflon-lined stainless-steel autoclave. The autoclave was then heated at 180 \({}^{\circ}\)C for 4 h. The resultant products were collected, washed by ethanol and water, and then dried in 80 \({}^{\circ}\)C for 8 h in air. Finally, the dried products were put into a furnace and heat-treated in air at 500 \({}^{\circ}\)C for 1 h, and then mesoporous NiCo\({}_{2}\)O\({}_{4}\) nanostructures were obtained. In the experiment, the prefabricated reaction solvents are the mixture of ethanol and deionized water (volume ration = 3:1), deionized water, the mixture of glycerol and deionized water (volume ration = 3:1). By adjusting the type of prefabricated reaction solvents, NiCo\({}_{2}\)O\({}_{4}\) nanoparticles, nanorods, nanowires, and microspheres could be prepared. The synthesis process and experimental details are shown in Figure 1.
# An Improved Hybrid Routing Protocol Combining MANET and DTN
###### Abstract  
Hybrid protocols combining a mobile ad hoc network (MANET) and a delay tolerant network (DTN) have recently been proposed. In these works, a whole network is fragmented, and MANET is generally used for intra-fragment communication, while DTN is used for inter-fragment communication. In this paper, an improved hybrid routing protocol was proposed, wherein virtual source nodes are selected based on the delivery predictability to the destination node if routing path to the destination node is not successfully established using MANET protocol. Then, messages are delivered to the destination node from the original source node and selected virtual source nodes. Performance evaluation results show that the proposed protocol with appropriate selection of delivery predictability threshold values has a better delivery ratio than conventional protocol, at the expense of overhead ratio in the considered parameter setting.  
mobile ad hoc network (MANET); delay tolerant network (DTN); hybrid routing protocol 27 January 2020; Accepted: 3 March 2020; Published: 5 March 2020
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
###### Abstract  
Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets.  
computation offloading; WBANs; C-MEC; resource management; QoS +
Footnote †: journal: Applied Sciences
## 4 Nonlinear Static Analysis  
The collapse mechanism equilibrium curve [27] can be obtained by a rigid-plastic analysis extended to second-order effects.  
First, it is necessary to evaluate, for each possible collapse mechanism, the first-order collapse multiplier \(\alpha_{0}\) and the corresponding slope of the mechanism equilibrium curve \(\gamma_{s}\). The mechanism that will be activated in a field of displacements compatible with the local ductility supplies will be the one characterised by the equilibrium curve located below the others.  
Tables 1 and 2 show the results of the first kinematic multiplier \(\alpha_{0,im}^{(t)}\) and the slope of the equilibrium curve \(\gamma_{im}^{(t)}\) for each failure mechanism according to the TPMC method [27; 28; 29; 30] and for case 1 and 2, respectively. In particular, in case study 1, the collapse mechanism occurs according to the partial failure mechanism type-2, according to Figure 2, while the soft-storey mechanism, i.e., type-3 mechanism, occurs in case study 2.  
Starting from the trilinear approximation of the push-over curve, the four characteristic points of the structural behaviour curve have been identified, and each of these points is associated with a specific limit state. They have been determined according to Equations (6)-(9), and their values are reported in Table 3 with reference to both case studies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{_Failure Mechanisms According to TPMC_} \\ \cline{2-7}  & \multicolumn{2}{c}{**Type-1**} & \multicolumn{2}{c}{**Type-2**} & \multicolumn{2}{c}{**Type-3**} \\ \cline{2-7} _Storey_\(i_{m}\) & \(\mathbf{\alpha_{0,im}^{(1)}}\) & \(\mathbf{\gamma_{im}^{(1)}}\) & \(\mathbf{\alpha_{0,im}^{(2)}}\) & \(\mathbf{\gamma_{im}^{(2)}}\) & \(\mathbf{\alpha_{0,im}^{(3)}}\) & \(\mathbf{\gamma_{im}^{(3)}}\) \\  & [-] & [m-1] & [-] & [m-1] & [-] & [-] & [m-1] \\ \hline
1 & 10.96 & 3.02 & **5.04** & **0.49** & 10.96 & 3.02 \\
2 & 6.81 & 1.41 & 7.41 & 0.59 & 11.74 & 2.59 \\
3 & 5.62 & 0.88 & 10.13 & 0.72 & 13.70 & 2.26 \\
4 & 5.27 & 0.63 & 16.46 & 0.99 & 18.26 & 2.01 \\
5 & 5.39 & 0.49 & 32.87 & 1.81 & 32.87 & 1.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: First-order collapse multiplier and slopes of the mechanism equilibrium curves for case study 1.  
Figure 10: Comparison between the Pushover curve and those obtained by the trilinear model for case study 1.  
In order to evaluate the accuracy of the trilinear model obtained, a static nonlinear analysis, or push-over, was carried out using the SAP2000 computer program [33].  
In Figures 10 and 11, the non-dimensional pushover curves obtained by SAP2000 are compared with those obtained by the trilinear model described in Section 3.