esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Consideration of Dynamic Loads in Axle Load Spectra
### Dynamic Load Coefficients  
To investigate the impact of pavement surface conditions on dynamic loads, DLC was defined as a ratio by dividing the standard deviation of dynamic loads by the mean value [5]. It is well accepted that pavement roughness conditions and vehicle speed are influential factors to DLC. [PERSON] and [PERSON] [12] and [PERSON], [PERSON], and [PERSON] [4] related DLC to pavement roughness conditions, while [PERSON], [PERSON], and [PERSON] [8] developed regression models of IRI in terms of pavement roughness conditions and vehicle speeds. Few studies have considered the effect of static axle loads on DLC. [PERSON] et al. [7] and [PERSON] [14] predicted the DLC values in unladen and laden loading scenarios, respectively. Thus, in this study, DLCs of dynamic loads of single, tandem, and tridem axles estimated by the multi-body dynamic models in TrusKSim were calculated for each pavement profile considering a wide range of static axle loads. The two vehicle speeds of 80 km/h (50 mph) and 104 km/h (65 mph) were set for minor and major highways, respectively. Figure 3 shows the effects of IRI and static axle loads on DLCs on minor and major highways. It  
Figure 2: Selected pavement profiles: (**a**) major highways; (**b**) minor highways (legend: SHRP_ID_IRI value).  
indicates that DLCs tend to increase as pavement roughness levels increase, and decrease with an increase in static axle loads.  
Based on the calculated DLCs, a nonlinear regression model was fitted with respect to axle load and pavement roughness for each axle type, as expressed by Equation (1). The vehicle speed was not included as a continuous variable in the regression model since two different speeds were considered for major and minor highways, respectively.  
\[DLC_{i,k}=a\times\ln(Q_{k})+b\times IRI+c \tag{1}\]  
where, \(DLC_{i,k}\) is dynamic loading coefficient of axle type \(i\) (\(i\) = 1 for single, 2 for tandem, and for tridem); \(Q_{k}\) is static axle load (kips); \( IRI\) is international roughness index of pavement profile (m/km); and \(a\), \(b\), and \(c\) are fitting coefficients.  
Figure 3: Change of DLC with IRI and axle loads: (**a**) single axle (minor); (**b**) tandem axle (minor); (**c**) tridem axle (minor); (**d**) single axle (major); (**e**) tandem axle (major); and (**f**) tridem axle (major).  
Table 1 presents the parameters of regression model in Equation (1) for major and minor highways. Regression models were derived for single, tandem, and tridem axle loads with the acceptable R-squared values. Taking advantage of these models, the DLCs of specific static axle loads could be predicted for determination of dynamic axle load spectra, given the axle load interval.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 3 The Localization Method Using Bearing-Only Measurements
### Run the RPEKF Based on Bearing-Only Measurements of Passing Ships  
This subsection presents running the RPEKF based on bearing-only measurements of passing ships. We discuss how to update the EKF track in the RPEKF (See Section 2.3).  
Recall that the superscript \((j)\) in Section 2.3 denotes the index of each EKF track in the RPEKF (See Section 2.3). The EKF is composed of two steps: prediction step and measurement update step.
# Formation of a Colloidal CdSe and ZnSe Quantum Dots via a Gamma Radiolytic Technique
## 4 Results and Discussion
### Morphology and Particle Size  
Figure 5 shows the TEM images and corresponding size distribution histogram of CdSe and ZnSe nanoparticles. From the illustration, a uniform spherical morphology with a relatively narrow homogeneous distribution was achieved for both the CdSe and ZnSe nanoparticles. The average particle size of the CdSe nanoparticles was estimated to be 17.3 nm whereas the average particle size of the ZnSe nanoparticles was found to be 11.2 nm. The average particle size obtained from TEM images are in good agreement with the estimated crystallite size from the XRD profile results for both CdSe and ZnSe nanoparticles (Table 2). At 120 kGy, the gamma radiolytic method produced CdSe and ZnSe nanoparticles with larger particle sizes compared to those synthesized by microwave [20] or hydrothermal [21; 24] methods. The creation of hydrated electrons by gamma irradiation in the colloidal solution is considered constant for formation of both CdSe and ZnSe nanoparticles, but the particle size of CdSe nanoparticles is greater than that of ZnSe nanoparticles. The fundamental reason for this difference is that the atomic size of Cd (atomic mass of 112.40) is greater than that of the atomic size of Zn (atomic mass of 55.87). Another advantage of the gamma radiolytic method is that the formation of CdSe and ZnSe nanoparticles ceases immediately after removal of the gamma ray source. The formed nanoparticles remain stable (in our case for 7-10 days) prior to UV-Visible absorption and TEM measurements. Similar findings have been reported in previous investigations [31; 32].  
The ZnSe and CdSe nanoparticles were found to increase in size with increasing dose [31; 32]. However, increasing the molecular mass or concentration of PVP reduced the particle size of ZnSe and CdSe nanoparticles as a result of the efficient capping mechanism making the individual particles difficult to aggregate [10]. An increase in precursor concentration would increase the number of nucleation processes of ZnSe and CdSe nanoparticles because of the increasing number of Cd, Zn, and Se ions, thus enlarging the particle size [28].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Crystal Structure** & **Crystallite Size/nm** & **Particle Size/nm** & **Bandgap /eV** \\ \hline CdSe & Hexagonal & 16.3 & 17.3 & 2.87 \\ ZnSe & Hexagonal & 10.7 & 11.2 & 3.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Crystal structure, size and optical band gap of CdSe and ZnSe nanoparticles.  
Figure 4: EDX compositional analysis of ZnSe nanoparticles.
## 3 Single Phase Lattice Boltzmann Equation  
In LB, the fluid characteristics are described by sets of imaginary particles by propagation and relaxation in the d-dimensional lattice. For instance, in the \(\mathrm{D_{2}Q_{9}}\) lattice, which was used in this work, each node at position \(\mathbf{x}\) has a distribution function, \(\mathrm{f_{\alpha}}(\mathbf{x},\mathrm{t})\), in any of the nine discrete directions, \(\mathbf{e_{\alpha}}\). Each node in each iteration not only passes information to itself, but also communicates with its eight neighboring nodes. The LB equation with the Bhatnagar-Gross-Krook (BGK) collision model in each iteration is written as:  
\[\mathrm{f_{\alpha}}(\mathbf{x}+\mathbf{e_{\alpha}}\Delta\mathrm{t},\mathrm{t }+\Delta\mathrm{t})-\mathrm{f_{\alpha}}(\mathbf{x},\mathrm{t})=-\frac{1}{ \tau_{\mathrm{v}}}\left(\mathrm{f_{\alpha}}(\mathbf{x},\mathrm{t})-\mathrm{f_ {\alpha}^{eq}}(\mathbf{x},\mathrm{t})\right)+\mathrm{F_{\alpha}}(\mathbf{x},\mathrm{t}) \tag{6}\]  
where \(\mathrm{F_{\alpha}}(\The equilibrium distribution function in the D\({}_{2}\)Q\({}_{9}\) model is defined as [54]:  
\[\mathrm{f}_{\alpha}^{\mathrm{eq}}=\omega_{\alpha}\rho\bigg{[}1+\frac{3}{c^{2}}( \mathbf{e}_{\alpha}.\mathbf{u})+\frac{9}{2c^{4}}(\mathbf{e}_{\alpha}.\mathbf{u} )^{2}-\frac{2}{3c^{2}}\mathbf{u}^{2}\bigg{]} \tag{8}\]  
where \(\omega_{\alpha}\) is a weight coefficient defined as:  
\[\omega_{\alpha}=\left\{\begin{array}{cc}\frac{4}{9}&\alpha=0\\ \frac{1}{8}&\alpha=1,2,3,4\\ \frac{1}{36}&\alpha=5,6,7,8\end{array}\right. \tag{9}\]
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
## 3 The Calculation of the Posterior Probability
In the decoding process at this moment, \(CX^{i}\) cannot provide any information about \(\hat{X}_{i}\), and it is necessary to make two alternative decoding attempts at this position. Therefore, at this moment, \(P(\hat{X}_{i}|\hat{X}_{i-k}^{i-1},CX^{i})\
eq 1\), there should be \(P(\hat{X}_{i}|\hat{X}_{i-k}^{i-1},CX^{i})=P(\hat{X}_{i}|\hat{X}_{i-k}^{i-1})\). However, if \(X_{i}\) is an encoded symbol, regardless of the previous decoding process; it should be considered that both \(\hat{X}_{i-k}^{i-1}\) and \(CX^{i}\) are known at moment \(i\), and we have \(P(\hat{X}_{i}|\hat{X}_{i-k}^{i-1},CX^{i})=1\). In this way, the posterior probability calculation method of SPDCAC can be represented by:  
\[ \begin{split}\tilde{\Lambda}&=P(\hat{X}^{N}=\hat{ x}^{N},Y^{N}=y^{N},CX^{N})\\ &=\prod_{i=1}^{N}P(Y_{i}=y_{i}|\hat{X}_{i}=\hat{x}_{i})\cdot P( \hat{X}_{i}=\hat{x}_{i}|\hat{X}_{i-k}^{i-1}=x_{i-k}^{i-1},CX^{i})\end{split}  \tag{4}\]  
The logarithmic form of (4) is:  
\[ \begin{split} log\tilde{\Lambda}&=\log P(\hat{X}^{N} =x^{N},Y^{N}=y^{N},CX^{N})\\ &=\sum_{i=1}^{N}(logP(Y_{i}=y_{i}|\hat{X}_{i}=\hat{x}_{i})+logP( \hat{X}_{i}=\hat{x}_{i}|\hat{X}_{i-k}^{i-1}=\hat{x}_{i-k}^{i-1},CX^{i}))\end{split}  \tag{5}\]  
where  
\[P(\hat{X}_{i}=\hat{x}_{i}|\hat{X}_{i-k}^{i-1}=\hat{x}_{i-k}^{i-1},CX^{i})= \begin{cases}1&\text{$x_{i}$ is encoded}\\ P(\hat{X}_{i}=\hat{x}_{i}|\hat{X}_{i-k}^{i-1}=\hat{x}_{i-k}^{i-1})& \text{$x_{i}$ is purged}\end{cases} \]  
This calculation method is only related to the BSC channel crossover probability and conditional probability distributions in the context model. It can not only use the correlation between the source sequence and the side information sequence, but can also selectively utilize the correlation among source symbols according to different decoding cases to ensure a better decoding BER (bit error rate) performance. It should be noted that DSC schemes based on expanding probability intervals such as DAC and DJSCAC cannot employ the proposed method, since, in these schemes, the time when the codeword falls into the overlapping area during the decoding process is not determined, the timing and number of occurrences of decoding ambiguity in different decoding paths are different. It is difficult to fairly compare the cumulative product of posterior probabilities in different paths, since the calculation of \(P(\hat{X}_{i}=\hat{x}_{i}|\hat{X}_{i-k}^{i-1}=\hat{x}_{i-k}^{i-1},Cx^{i})\) depends on the occurrence of decoding ambiguity. If the decoder of DAC use (5) to calculate the posterior probability, the cumulative product of posterior probabilities of a decoding path with many ambiguities will be much smaller than that of another decoding path with a few ambiguities. Apparently, such a difference between the cumulative product of posterior probabilities is not caused by the different correlations between the corresponding decoding sequences and the side information sequence.
## 3 Monitoring and Verification
### Comparative Analysis of Monitoring and Simulation
#### 3.2.3 Comparative Analysis of Bolt Dynamometer  
Based on the analysis of field monitoring test data and numerical simulation results (Figure 10), the stress characteristics of the monitoring section are as follows:  
1. The axial forces measured at various positions of the steel arch supports are positive, indicating a tensile state. At the same monitoring point, the sensors at deeper depths register smaller anchor rod axial forces. Compared to sensors at greater depths at the same monitoring point, those near the crown record the highest values. For instance, at a depth of 0.5 m from the crown, the axial force is 69.02 kN; at 1.5 m, it is 50.65 kN, and at 2.5 m, it is 34.87 kN. This suggests a trend in which the axial force at 0.5 m is greater than that at 1.5 m, which is greater than that at 2.5 m. In addition, as the monitoring point transitions from the crown to the arch's rib, the consistent values of the anchor rod axial forces on the same side exhibit a descending trend: the crown axial force surpasses the arch shoulder axial force, which exceeds the arch rib axial force.
2. A comparison of field monitoring data with numerical simulation results indicated that, at the same depth of 0.5 m, the observed axial forces predominantly range from 19.04 to 46.57 kN. The patterns in both datasets are broadly similar, though the actual monitoring outcomes are marginally lower than the simulated values. This suggests that the numerical simulation model and the chosen parameters for the surrounding rock lining are appropriate.  
Figure 9: Comparison of simulation and monitoring curves during the process from sensor installation to 20 m excavation (steel strain gauge).
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 2 Theoretical Background  
### Neural Networks  
As one of the most important members in the field of machine learning [26], neural network is considered an effective prognostic algorithm. Multilayer perceptron, radial basis function networks, and other neural networks have been widely used in anomaly detection, damage clustering, and fault diagnosis. Excitingly, remarkable success has been achieved [27].  
As for time series data, such as the samples in power forecasting problem, researchers have been searching for more reasonable models and algorithms to tap into their potential. Traditional static neural networks such as multi-layer perceptron (MLP) considered a sample point independently and create a prediction at each time step, and a majority of information in historical data was lost in the process. LSTM networks overcame the issue above and 'vanishing gradients' and 'exploding gradients' problems in RNNs.
## 4 Conclusions  
Emerging pollutants pose a severe risk for the ecosystems and the human health, since they are continuously released into the environment. Among them, phenolic compounds play an important role being widely used in industrial applications. In this study, three different PACs were used to remove phenolic compounds after preliminary analyses on organic dyes. All the samples displayed very high surface area values, ranging from about 820 m\({}^{2}\)g\({}^{-1}\) to 1200 m\({}^{2}\)g\({}^{-1}\). In particular, the sample SCGs-AC, prepared by spent coffee grounds treated with KOH and then pyrolyzed at high temperature, showed a 30% higher surface area value with respect the commercial active carbons used for comparison purposes. Moreover, the mean pore diameter was between micro- and mesopore regions for this sample and typical of mesoporous materials for the commercial ones. Adsorption occurred via multiple mechanisms, including pore filling and adsorbent-adsorbate \(\pi\)-\(\pi\) and H-bonding interactions. SCGs-AC proved to have similar adsorption capacities than commercial PACs for the removal of organic dyes and phenolic compounds. In particular, PACs showed higher affinity with ionic substances, as MB and EB, as confirmed by the high partition coefficients and in agreement with their surface properties. Regarding Ph and CP, the removal rate was around 70% and 95% for all PACs. BPA was harder to remove from solution in comparison to the other phenolic compounds, with the lowest value of PC for SCGs-AC. Future studies will be aimed at improving the chemical properties of the produced SCGs-AC, via surface functionalization, to obtain a higher removal efficiency for phenolic compounds and other endocrine-disrupting agents. Moreover, LCA and LCC studies will be carried out to evaluate whether this application could be feasible from both the environmental and economical point of view at larger scale.  
Conceptualization, R.B. and P.S.; methodology, R.B., P.S., E.M., and A.T.; investigation, E.R., F.G., M.M. and G.M.; resources, R.B., M.C.L., E.M., and A.T.; data curation, E.R., F.G., G.M., E.M., and A.T.; writing--original draft preparation, E.R. and F.G.; writing--review and editing, E.R., P.S., R.B., E.M., and A.T.; supervision, R.B., P.S., and M.C.L.; project administration, R.B.; funding acquisition, R.B. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Industrial Engineering (DII), University of Padova, TWINNING-2017 research project \"Supramolecular transition-metal architectures based on non-covalent interactions for selective molecular sorption and non-linear optics: Rational design and preparation.\"  
Authors wish to thank CEASC (University of Padova) for ESEM analyses.  
The authors declare no conflict of interest.
# Context-Aware Data Dissemination for ICN-Based Vehicular Ad Hoc Networks
## 4 Test and Evaluation of the Results
### Simulation Environment and Evaluation Metrics
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline WLAN Protocol & IEEE 802.11p \\ Data Link Types & DLT\_IEEE802\_11\_RADIO \\ Wireless transmission model & ConstantSpeedPropagationDelayModel \\ Signal propagation loss model & RangePropagationLossModel \\ Receiving gain & 0 db \\ Data packet length & 1200 bytes \\ Remote station manager & ConstantRateWifiManager \\ Vehicle speed & 30–80 km/h \\ Time & 60 s and 300 s \\ Radio transmission range R & 300 m \\ \(D_{m}\) & 250 m \\ \(m\) & 8 ms \\ \(c\) & 10 \\ \(d_{s}\) & \(10/\pi R^{2}\) (\(R=300\) m) \\ \(\lambda\) & 5 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for the simulations. Figure 11: Data processing at vehicular nodes. network in the TCP/IP paradigm, whereas Basic-NDN is the original work combining NDN with VANET; V-NDN is an earlier work combining NDN and VANET, which also forwards packets based on geographical position information. In our tests, 10% of the nodes sent Interests with different names (i.e., asking for different content). Another 10% of the nodes served as content providers, holding the requested content and are randomly distributed in the network. Altogether 500 different types of contents (i.e., Data Packet) were used. The requested content follows Zipf distribution from the 500 different types. In our simulation, we set the parameters \(k=0.5\) for the Zipf distribution \(P(r)=c/r^{k}\). In order to analyze the effects of the distance-assisted routing and broadcast suppression, the same caching strategy was used. We selected the map of Solna in Stockholm, Sweden in our tests, which has an area of about 8.05 km\({}^{2}\) and where the roads and traffic are complex, as shown in Figure 12.
### Contributions and the Paper Structure
* Propose to use the SCADA system to update the operation status of DGs and the opened/closed status of circuit breakers (CBs), and to remotely control digital relays for the ASPC approach. * The ASPC system of OCRs can be effectively operated regarding different fault scenarios in the DG-based distribution system as well as the 'on-grid' or 'off-grid' operation modes for DG units in the DN. * A real 22 kV DG-integrated distribution network which is simulated by ETAP software considered to be a reliable test-bed to validate the proposed ASPC system of OCRs. * Coordination results of the OCRs are based on three optimization algorithms, Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA), as referred in Section 2.4. The remaining sections of the paper are organized as follows. Section 2 presents the proposed ASPC approach for OCRs in the DG-based DN. This section presents an objective function of OCR protection coordination, coordination constraints of the OCRs in the DN, the reliable and accurate calculation method of minimum and maximum fault currents for each fault type in the DN having DGs, selection of pick-up and tripping currents for OCRs, and three common optimization algorithms, GSA, hybrid PSO-GSA and GA. Section 3 describes a case study of the practical 22 kV DG-integrated distribution network. Section 4 indicates the optimal protection coordination results of OCRs in the practical 22 kV DN, analysis and discussion on these relay coordination results. Last but not least, Section 5 contains the conclusions of the study.
## 1 Introduction
### Laser and Electron Rapid Prototyping Methods  
Two main AM techniques dedicated to metallic alloys are Direct Metal Laser Fabrication (DMLF) [17] and Selective Electron Beam Melting (SEBM) [18; 19]. DMLF techniques are then divided further based on the power application methods. Some variants use powder provided beforehand in special beds. Those methods, called \"powder in bed\", include Selective Laser Sintering (SLS) [20], also known as DMLS (Direct Metal Laser Sintering) [21], and Selective Laser Melting (SLM) [22]. Other DMLF methods deliver the powder by injection nozzles just above the sample. These include Laser Engineered Net Shaping (LENS) [23] and similar methods, such as Direct Metal Deposition (DMD) [24] and Direct Light Forming (DLF) [25]. Other DMLF methods that are not used for the production of metal implants include Laminated Object Manufacturing (LOM), where a laser beam is used for cutting metal sheets [26]. Direct metal fabrication methods that employ an electron beam are Electron Beam Free Form Fabrication (EBF\({}^{3}\)) [27] and Electron Beam Melting (EBM) [28].  
The great advantage of using the AM methods for medical applications is the possibility of producing custom-made implants based entirely on the Computer Tomography (CT) scan data of the patient's disease [29]. Although there are many AM methods suitable for producing functional metallic implants, only the \"powder in bed\" methods are suitable for fabricating scaffolds needed for bone tissue engineering, i.e., which have complex, custom designed shapes and controlled porosity [30; 31].
# Additive Manufacturing in the Clothing Industry: Towards Sustainable New Business Models
## 2 Literature Review
### Clothing Lifecycle  
In general, a product lifecycle has five phases as follows: imagine, define, realize, use/support, and retire/dispose. According to [PERSON] [16], in each phase, a product is in a different state. Product lifecycle management (PLM) is an effort to manage each phase effectively to increase product revenue and reduce costs [53].  
To study the clothing industry's produced waste, the lifecycle of the products needs to be explored. [PERSON] et al. [54] presented a detailed view of the production-consumption path of a T-shirt. In their illustration, energy, water, and minerals are the inputs of the production process; while the outputs, such as gases, consumed water, and discarded clothing impact air, water, and soil. The production steps in a study by [PERSON] et al. [54] began with cotton fiber production, which includes farming and ginning, followed by transportation to yarn production through the process of spinning, fabric formation by the knitting process, wet processing for bleaching or dyeing, and T-shirt production through cutting and sewing processes, followed by transportation to the consumption markets. In their illustration, after the retail and use phase, the disposal takes place, which involves incineration, landfills, and water-treatment systems.  
In the clothing lifecycle, various production decisions such as the selection of raw materials, quality of clothing confections, and later consumption decisions related to the method of washing and drying are important in the disposal or reusability of the product [6]. [PERSON] [55] illustrated the three possible types of clothing lifecycle management. In the simplest type, the clothing is produced, and after consumption, goes directly to the disposal phase. In the second type, the product is sold/given away for reuse before being sent to the landfill. The first two consumption types follow a cradle-to-grave ideology. However, in the third type, which is the ideal one and illustrates a sustainable lifecycle (cradle to cradle), the product is fully recycled and starts over in the product lifecycle [55].  
In another study, Levi Strauss and Co. investigated the total impact of a single pair of Levi's 501 trousers over its lifecycle on the environment. The results showed that 33.4 kg of CO\({}_{2}\) and 48.9 g of PO\({}_{4}\) were released to the environment, while 3781 liters of water was consumed [56].