esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Moisture Susceptibility  
The percentage of bitumen coverage was noted after 6, 24, 48, and 72 h during the rolling bottle test. Figure 12 is the graphical representation of the relationship between the time of rolling and the percentage of bitumen coverage. Figure 13 shows the affinity between the bitumen and aggregate after 72 h of rolling. The rolling bottle test is a measure of the binder's moisture susceptibility. A higher percentage of bitumen coverage indicates a strong adhesive bond between the binder and the aggregate, as well as a high resistance of the binder to moisture damage. It is evident from the results that, after the completion of the test, the percentage of bitumen coverage for the base binder was 15%. It then increased to 60% when 2% of GNPs were added. For the binder modified with 4% of GNPs, it jumped to 70%. The extremely small size of the GNPs gives them a larger surface area compared to their parent material, which results in the increase of their affinity to form a strong bond. Thus, GNPs have the ability to absorb more free asphalt binder and maximize the quantity of the structural asphalt [26]. The results prove that the addition of GNPs leads to an increased adhesion between the binder and aggregate, making the asphalt highly resistant to moisture.
## 2 SIR-Based Performance Analysis
### The ABEP for Binary Differential Phase-Shift Keying Modulation  
We evaluate here the MGF-based ABEP for BDPSK modulation. The formula is given by [1]:  
\[\mathrm{P_{be}}(\Omega_{0})=0.5\mathrm{M_{z}}(1),\mathrm{forBDPSK}. \tag{26}\]  
Based on replacing (24) to form (26), the ABEP for BDPSK modulation is obtained and presented in the next figures versus \(w_{i}=\Omega_{i}/s_{i}\) for different sets of parameters.  
In the case of ABEP for BDPSK modulation presented in Figure 10, it can be noticed that ABEP decreases with increasing fading parameter \(\kappa_{x}\), and power ratio \(w_{i}\), but ABEP is bigger for larger values of parameter \(m\), and the system performance deteriorates.  
\begin{table}
\begin{tabular}{c c c c} \hline
**ABEP-BFSK** & \(\mathbf{w_{i}}\) = \(-10\) **dB** & \(\mathbf{w_{i}}\) = \(0\) **dB** & \(\mathbf{w_{i}}\) = \(10\) **dB** \\ \hline \(\kappa_{y}=1,\mu=1,L=2\) & 8 & 8 & 7 \\ \hline \(\kappa_{y}=1,5,\mu  
In the situation in Figure 11, the influence of CCI parameters \(\kappa_{y}\) and \(\mu\) and the number of input branches \(L\) are shown. When \(L\) is increasing, the ABEP is becoming smaller, thereby improving system performance. The decrease in ABEP is greatest when L increases from 1 to 2, then somewhat less from 2 to 3, and so on. Therefore, to improve the performance, it is quite sufficient to take an SC combiner with 2 or 3 branches. On the other hand, parameters \(\kappa_{y}\) and \(\mu\) do not significantly affect the size of the ABEP.  
Figure 11: MGF-based ABEP for BDPSK modulation: CCI parameters \(\kappa_{y}\) and \(\mu\) are varying, and number of branches \(L\), while constant are fading parameters \(\kappa_{x}=1\), \(m=1\), and powers \(\Omega=1\), \(s=1\).  
Figure 10: ABEP for BDPSK modulation when parameters \(\kappa_{x}\) and \(m\) are changing. Other parameters values are constant: \(\kappa_{y}=1\), \(\mu=1\), \(L=2\), and powers: \(\Omega=1\), \(s=1\).  
Afterwards, we provide two tables showing the number of additions required in the sum in expression (26) to achieve the required accuracy.  
From Table 7, we see that as the parameters \(\kappa_{x}\) and \(m\) increase, the number of elements that need to be added in order to achieve convergence to the fifth decimal increases, and the series converges more slowly. It is necessary to have in sum between 8 and 14 additions for all values of parameters participating in expressions.  
When the parameters \(\kappa_{y}\) and \(\mu\) increase, as shown in Table 8, the number of additions in the sum required to achieve convergence to the fifth decimal increases, and the series converges more slowly. When \(L\) is bigger than 2, the number of elements decreases significantly, so for z = \(-10\) dB it is 8, for z = 0 dB it is 7, and for z = 10 dB the number of elements is only 5 or 6, depending on other parameters values.
## 2 Research Method and Process
### Research Process  
Problem identification and motivation was \"What kind of concept and system architecture enables real-time uninterrupted streaming data transfer from moving vehicles towards control center?\" The research entry point was \"Objective Centered Solution\". As the main objective, the solution should enable transfer of large amounts of delay and loss-sensitive data from moving mining vehicles to a control center. The goal is to achieve (near-) optimal multipath routing in an emulated underground mining environment. Particularly, routing of data from a moving vehicle towards the control center is the focus. Preprocessing or storing of data at the router is out of the scope for this paper.  
The architecture and system were designed and implemented for realizing multi-access routing in moving vehicles based on continuous ML. Continuous ML refers to the deployment and inference of models, which are continuously trained based on the most recently available data. In the context of this paper, inference refers to predicting OWD for data transfers when a specific access point is utilized for routing. Particularly, predictions are utilized for decision making regarding the AP to be selected for routing.  
Demonstration and evaluation were performed in an environment that could be used for emulating moving vehicles within an underground mining industry. In particular, data transfers from moving vehicles towards a control center (over multiple radio accesses) were emulated, and ML was utilized in decision making, regarding the most suitable path for  
Figure 1: The research process performed based on the DSR methodology (figure is adapted based on [18]).  
communication. Performance of two artifact iterations was evaluated in terms of ex-post criteria [20] (feasibility, fidelity with real world phenomenon, efficiency, and operationality).  
From a communications perspective, the results are described, published, and communicated in this article. The research process applied is illustrated in Figure 1.
## 4 Parametric Analysis and Parametric-Sensitivity Analysis
### Effect of Differential Order \(\alpha\)  
First, the order \(\alpha\) of the differential is increased by 20%, 40% and 60% and decreased by 20%, 40% and 60%, while other parameters are kept constant. Figure 3 shows the settlements at the centre of the rectangle with time for different \(\alpha\). The figure shows that the settlements contain two stages, instantaneous settlement and deceleration creep, and the change in order \(\alpha\) has no effect on the instantaneous settlement at \(t=0\) and the ultimate settlement at \(t\to+\infty\), which is consistent with the law described in the analysis of viscoelastic solutions above. Moreover, Figure 3B shows that there is a demarcation point for different \(\alpha\) that divides the creep curve into two stages: in the first stage, the settlement's value decreases at the same moment as \(\alpha\) increases; in the second stage, the settlement's value increases at the same moment as \(\alpha\) increases. The peculiar law is attributed to the Abel damping element, an intermediate between the ideal solid (\(\alpha=0\)) and the Newtonian fluid (\(\alpha=1\)), which allows the fractional-order model to describe a wider range of rheological properties of the soil or rock than the integer-order model (\(\alpha=1\)). This phenomenon is consistent with the results of the literature [22; 23; 24]. The smaller the value of \(\alpha\), the longer the time required to reach a steady state. For \(\alpha\) = 1, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3 and 0.2, the times required for the basic stabilisation of settlements are \(130,2\times 10^{4},\ 10^{5},\ 8\times 10^{5},\ 10^{7},\ 5\times 10^{8},\ 2 \times 10^{11}\) and \(4\times 10^{16}\) d, respectively.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 2 Structure of Filter Bank Multi-Carrier Systems
### Overview of Next Generation Networks (5G)  
The Cisco Internet Business Solutions Group (IBSG) in 2011 predicted that the number of Internet connected devices will rise to about 50 billion by 2020 [46]. To achieve higher data transmission rates and capacity enhancement due to the high demands of the sparsely available frequency spectrum of wireless communication networks, the downlink layer of the 4G/LTE based system is well designed using OFDMA technology. The use of CP-OFDM in the design of 4G networks offer several benefits, such as improvement in bandwidth/spectrum utilisation (spectral efficiency), robustness to the detrimental effects of multipath fading and efficient frequency domain allocation of available resources/spectrum for enhancement in multiple user (MU) transmissions. However, the 4G CP-OFDM scheme is known to have certain limitations such as introduction of system overhead, sensitivity carrier frequency/time offsets (CFTO) and very high peak-to-average-power ratio (PAPR) [47]. The added system overhead arises from the inclusion of CP in other to combat the effects of multipath propagation as the signal propagates over the wireless frequency selective fading channel. This resulting overhead (from the added CP) reduces system spectral efficiency while CFTO introduces unpleasant ICI due to loss in sub-carrier orthogonality. PAPR [48; 49] is the ratio between the maximum power of a sample in an OFDM transmit symbol to the average power of that OFDM symbol. A high PAPR (as characterised by OFDM systems) decreases the efficiency of high power amplifiers and consequently leads to further degradation of system performances. To improve the limitations of the 4G/LTE CP-OFDM system, the xG (5G and beyond) wireless networks are designed to meet fundamental requirements such as very high reliability and robustness, low out-of-band (OOB) emission, ultra-low latency and immensely high data rate capacity [50; 51; 52]. We discuss the potential candidates that constitute the air interface configuration for the physical (PHY) and medium access control (MAC) layer of 5G networks in Section 2.4.1. Subsequently, we classify these candidate waveforms in Section 2.4.2.
## 3 Results and Discussion
### Color Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method of Heat Treatment** & **L*** & **a*a** & **b*a** & **C*a** & **\(\Delta\)E** \\ \hline Raw sample & 53.35 \(\pm\) 1.41 \({}^{\text{c}}\) & 24.80 \(\pm\) 2.46 \({}^{\text{d}}\) & 29.28 \(\pm\) 1.05 \({}^{\text{c}}\) & 38.39 \(\pm\) 2.25 \({}^{\text{c}}\) & - \\ Boiling & 40.74 \(\pm\) 2.75 \({}^{\text{a}}\) & 10.66 \(\pm\) 1.59 \({}^{\text{bc}}\) & 22.40 \(\pm\) 2.60 \({}^{\text{a}}\) & 24.81 \(\pm\) 3.02 \({}^{\text{a}}\) & 20.16 \\ Steaming & 41.58 \(\pm\) 1.03 \({}^{\text{ab}}\) & 10.09 \(\pm\) 1.62 \({}^{\text{abc}}\) & 23.40 \(\pm\) 0.84 \({}^{\text{ab}}\) & 25.54 \(\pm\) 0.74 \({}^{\text{ab}}\) & 19.74 \\ He steaming & 43.17 \(\pm\) 1.44 \({}^{\text{b}}\) & 8.99 \(\pm\) 0.90 \({}^{\text{ab}}\) & 24.92 \(\pm\) 1.29 \({}^{\text{b}}\) & 26.50 \(\pm\) 1.44 \({}^{\text{b}}\) & 19.30 \\ Baking & 42.72 \(\pm\) 0.50 \({}^{\text{ab}}\) & 11.99 \(\pm\) 2.22 \({}^{\text{c}}\) & 24.23 \(\pm\) 0.34 \({}^{\text{b}}\) & 27.10 \(\pm\) 1.04 \({}^{\text{b}}\) & 17.40 \\ Sous-vide & 40.83 \(\pm\) 1.93 \({}^{\text{a}}\) & 8.12 \(\pm\) 1.41 \({}^{\text{a}}\) & 22.00 \(\pm\) 1.12 \({}^{\text{a}}\) & 23.47 \(\pm\) 1.50 \({}^{\text{a}}\) & 22.09 \\ \hline \hline \end{tabular}  
* Data are expressed as mean value \(\pm\) standard deviation (\(n=6\)). Values in the same column with different superscript letters are significantly different (\(p\leq 0.05\)).  
\end{table} 
Table 1: Color parameters of carrot before and after different heat treatments. Similarly, [PERSON] et al. [40] emphasized that the color of carrots can change during cooking as a result of the _cis_-isomerization of both \(\alpha\)- and \(\beta\)-carotenoids. Furthermore, [PERSON] and [PERSON] [29] showed differences in color changes during boiling, steaming, and frying both in the internal and external surfaces of carrots. Interestingly, although the color parameters decreased due to heat treatment, C* decreased on the internal surface, while the opposite relationship was found on the external surface. In contrast, [PERSON] et al. [41] found no significant color differences observed in carrot slices subjected to long periods (3-12 h) of moderate heating (75-95 \({}^{\circ}\)C), with or without low hydrostatic pressure.
## 2 Materials and Methods
### Data Processing and Statistics  
Dialysis data were used for the calculation of relative retention as a ratio between platinum concentration in the initial cell and total platinum concentration (sum of retained and dialysed platinum), expressed as a percentage. To compare retention between 50% plasma and the control buffer, a paired t-test was used. The Shapiro-Wilk test was used to test for normality of the distributions. A statistical level of \(p<0.05\) was considered as significant.  
Average retention of Pt was calculated as follows: Equation (1) for both 1:2 diluted plasma and the control buffer:  
\[\text{Retained Pt\%}=C_{\text{retained}}/(C_{\text{retained}}+C_{\text{ dialysed}})\times 100 \tag{1}\]'Free' platinum unbound to high-molecular-weight species (including the highest-molecular-weight fraction of BP-Cx-1 ligand) was calculated (as a percentage) as the difference between 100 and average retention:  
\[\text{Unbound Pt control buffer}^{\text{ }}\%=(100-\text{Retained Pt}\%) \tag{2}\]  
In the case of plasma, the obtained value was divided by 2 to correct for the initial plasma dilution:  
\[\text{Unbound Pt plasma}\%=(100-\text{Retained Pt}\%)/2 \tag{3}\]  
The quantity of platinum bound to the macromolecular fraction (BP-Cx-1\({}_{\text{HMW}}\)) of polymeric ligand BP-Cx-1 was calculated as follows:  
\[\text{BP-Cx-1}_{\text{HMW}}\text{ Pt}\%=100-2\times\text{Unbound Pt control buffer}\% \tag{4}\]  
The fraction of macromolecular BP-Cx-1 bound platinum was taken into account when calculating the actual binding to proteins:  
\[\text{Pt bound to proteins, }\%=100-2\times\text{Unbound Pt plasma}\%-\text{BP-Cx-1}_{\text{HMW}}\text{ Pt}\% \tag{5}\]  
Microsoft Excel 2011 for MacOS (Microsoft, Redmond, WA, USA) and Prism 6 (Graph-Pad Software, San Diego, CA, USA) packages were used for calculations.
## 1 Introduction  
Plants of the _Boswellia_ genus (Burseraceae) are widely spread in northeast Africa, the Arabian Peninsula and India, and their gum resins have been used since ancient times for spiritual and medical purposes [1]. The genus includes 25 different species, including _Boswellia sacra_ (_B. sacra_), typical of Arabia and northeast Africa, _Boswellia papyrifera_ (_B. papyrifera_) from Somalia, Eritrea, Sudan and Ethiopia and _Boswellia serrata_ (_B. serrata_), typical of India [1]. However, the classification of the species within this genus is hard to define. For example, until a few years ago, _B. sacra_ and _Boswellia carteri_ (_B. carteri_) were considered two different species, while recently they have become recognized as a single species [2]. Gum resins from plants of the genus _Boswellia_ are widely used for the extraction of several bioactive compounds useful for cosmetics and nutraceuticalapplications. _Boswellia_ gum resins contain up to 30% pentacyclic triterpene acids (PTA), such as boswellic acids, their acetylated derivates and lupeolic acid [3], as well as volatile compounds that can make up to 15% by weight, including a di-terpenic fraction made from inconsole, inconsole acetate and cembrenol [4]. As a food supplement, the most used species are _B. carteri_ Birdw. (syn. _sacra_ Fluck.) and _B. serrata_ Roxb. which contain a large amount of boswellic acids. The content of boswellic acids is different among the species and therefore among the treatment indications also: _B. serrata_ resin is used for the treatment of oxidative and inflammatory damage [5], rhinitis [6], asthma [7], age-related disorders [8], neurorecovery [9], arthritis [10], skin disorders and cancer [11] and against several human pathogenic and plant pathogenic fungi. _B. carteri_ is otherwise used in the treatment of gastric and hepatic disorders [12] and skin disorders [13] and for its hepatoprotective activity [14], analgesic effect [15], antiglycation and antioxidant activities [16], tumor suppression [17], anticoagulation effects [18], anti-inflammatory activity [19] and cardioprotective effects [20]. In the market, it is often not possible to trace the origin of the gum resin; therefore, it is important to validate an analytical method to support the identification of the plant species from gum resin samples. The aim of this work is to provide an analytical method able to quantify the boswellic acids within the gum resin by a simple HPLC-DAD analysis and to develop a multi-variate model able to perform the clusterization of the observations reflecting their species. The application of the method presented in this study and the related multivariate analysis approach is focused on samples of resins from _B. carteri_ and _B. serrata_ due to their wide use as dietary supplements. Furthermore, we decided to augment the multivariate model with the introduction of literature data from another scientific publication.
## 2 Materials and Methods
### Quantitative Analysis of Representative Metabolites Using HPLC  
An HPLC (Agilent Technologies 1260 infinity, Santa Clara, CA, USA) was equipped with an autosampler, binary pump, and diode array detector for quantitative analysis. _E. globulus_ extract was subjected to an analytical column (XBridge C18, 4.6 \(\times\) 150 mm, 5 \(\upmu\)m, Waters, Milford, MA, USA). The mobile phase procedures for the gradient solvent system using solvent A (water containing 0.1% acetic acid) and solvent B (acetonitrile) were carried out as follows: 0-5 min, 0% B; 6-20 min, 20% B; 20-40 min, 50% B; 40-60 min, 100% B with a flow rate of 1 mL/min. The detector wavelength was recorded at 254 nm. For the quantitative analysis of the principal metabolites (salicylic acid \(\beta\)-D-O-glucuronide, chlorogenic acid, epicatechin, 2''-O-galloylhyperin, isoquercitrin, isorhapontin, quercetin, and quercetin-3-O-glucuronide), a calibration curve (R\({}^{2}\) > 0.998) was derived from the concentrations (0-100 \(\upmu\)g/mL) of all the individual standards by comparing the peak areas in the _E. globulus_ extracts.
## Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem  
**[PERSON] 1,2, [PERSON] 1,2,* and [PERSON] 1,2**  
1 School of Mechanical Engineering, Shandong University, Jinan 250061, China;  
E-Mail: [EMAIL_ADDRESS]  
2 Key Laboratory of High-efficiency and Clean Mechanical Manufacture (Shandong University),  
Ministry of Education, Jinan 250061, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-531-8839-2539.  
_Received: 9 March 2014; in revised form: 5 May 2014 / Accepted: 5 May 2014 / Published: 13 May 2014_  
**Abstract:** The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons.  
**Keywords:** discrete imperialist competitive algorithm; traveling salesman problem; 2-opt algorithm; numerical experiments
## 1 Introduction
From previous experience, the lower the PPS is, the worse the decoding performance becomes. For the present proposed air-to-water OCC system, due to the diminution by distance of the image of the light source, it will be subject to more stringent limitations, and image blurring and distortion will be more severe, presenting more challenges for OCC decoding. In the case of water-air interface transmission, a wide FOV is required to eliminate the need for active alignment between the optical transmitter (Tx) and optical receiver (Rx). OCC can make use of the large photo-sensitive area of complementary-metal-oxide-semiconductor (CMOS) image sensors for receiving optical signals. Hence, it can provide a wide FOV for detection [26]. In order to increase the data rate of OCC systems to above the frame rate, the rolling shutter (RS) effect in CMOS can be employed [27, 28]. Recently, artificial intelligence/machine learning (AI/ML) has been utilized to enhance the decoding performance of the RS pattern, such as convolutional neural networks (CNN) [29], pixel-per-symbol labeling neural networks (PPSNN) [30], and long short term memory neural networks (LSTMNN) [23]. In this work, we propose and experimentally demonstrate a wide FOV air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNL-STM). Due to the effects of water turbulence and air turbulence on AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. Here, OCC can provide large FOV for transmission and detection without the need for precise alignment. The CUDA Deep-Neural-Network (CuDNN) is a library of graphics processing unit (GPU)-accelerated primitives designed for deep neural networks. It can speed up the training and inference procedures for deep learning problems. It can also support many neural network designs, such as LSTMNN and CNN. Similar to LSTMNN [31], the CuDNNLSTM processes memory characteristics to enhance the decoding of OCC signals. Experimental results show that the proposed air-to-water OCC system can support 7.2 kbit/s transmission through a still water surface, and 6.6 kbit/s transmission through a wavy water surface; this performance satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER) of 3.8 \(\times\) 10\({}^{-3}\). Here, we also compare the proposed CuDNNLSTM algorithm with the PPSNN, illustrating that the CuDNNLSTM algorithm outperforms the PPSNN. Here, we also discuss in detail the implementation of the CuDNNLSTM, as well as the loss and accuracy over time. In addition, we also compare the model training times needed when using CuDNNLSTM and LSTMNN running in Python (version: 3.11.7) on the same computer. The time required for CuDNNLSTM is about eight times less than that required by LSTMNN. The paper is organized as follows: An introduction and background are provided in Section 1. The proof-of-concept water-to-air OCC experiment and the CuDNNLSTM algorithm are presented in Section 2. Then, the results and discussion are given in Section 3. Finally, a conclusion to summarize this work is provided in Section 4.