esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Techniques
#### 2.2.5 X-ray Fluorescence Spectroscopy  
Two different spectrometers were used for the XRF in situ analyses: one for the paintings on the walls of the apsis, and the other was a handheld model, which allowed working on the scaffolding for the vault.  
The elementary analysis of the apsis walls was performed using a portable XRF Asing LITHOS 3000 spectrometer equipped with a Mo anode X-ray tube and a Peltier-cooled Si-PIN detector. The radiation is quasi-monochromatic at the energy of Mo K\(\alpha\) energy by means of a Zr transmission filter; the X-ray tube mounts a 4 mm diameter collimator so that the irradiated area on the sample is about 25 mm\({}^{2}\). Data were collected using 25 kV high voltage and 0.3 mA tube current, with a 100 s acquisition time. The distance between the head of the device and the wall was about 10 mm.  
The XRF analyses on the vault were instead performed using a Tracer III SD Bruker portable device equipped with a rhodium X-ray tube, a palladium anticathode, and a solid-state silicon detector energy dispersion system. The set-up was as follows: 40 keV and 12 \(\mu\)A for 60 s; the instrument head, covered in soft plastic, was gently applied on the vault. The measuring area was an elliptical spot of 4 mm \(\times\) 7 mm. For data visualization and fitting, ARTAX software was used.  
For both spectrometers, we based our considerations on the raw spectra fitting: we assigned each characteristic energy to the related chemical element and performed the relative intensity evaluation to acquire information about the chemical composition of the materials. From a methodological point of view, a comparison between the results obtained by two different commercial portable XRF spectrometers mounting X-ray tubes with different anode materials allows us to obtain the most from this technique. Indeed, experience suggests that spectrometers should be selected according to the specific requirements and type of analysis to perform, always keeping in mind the difficulty of also working on hard-to-access surfaces, such as the Tormiparte vault frescoes for which scaffolds were required [19].
## 3 Results
Figure 6: The Pmax values observed in the dentin tissue; ECIR: no ferrule, endocrown; ECER: 2 mm circumferential ferrule, endocrown; PC4: 2 mm circumferential ferrule, post–core; PC3: 2 mm mesial, palatal, and distal ferrule, post–core; PC2: 2 mm mesial and distal ferrule, post–core; PC1: 2 mm distal ferrule, post–core; PC0: no ferrule, post–core; Pmax: maximum principal stress values; LS: lithium disilicate; PICN: polymer-infiltrated ceramic network. Figure 7: The Pmax values observed in the periodontal ligament; ECIR: no ferrule, endocrown; ECER: 2 mm circumferential ferrule, endocrown; PC4: 2 mm circumferential ferrule, post–core; PC3: 2 mm mesial, palatal, and distal ferrule, post–core; PC2: 2 mm mesial and distal ferrule, post–core; PC1: 2 mm distal ferrule, post–core; PC0: no ferrule, post–core; Pmax: maximum principal stress values; LS: lithium disilicate; PICN: polymer-infiltrated ceramic network. When the Pmax values found in the restoration were examined, LS exhibited values greater than the PICN for each group (Figure 2). For the Pmax values observed in dentin (Figure 6), the fiber post (Figure 5), and the resin composite cores (Figure 4), the PICN revealed higher values in each group. In terms of the Pmax values found in the resin cement (Figure 3), the Pmax values were higher in the PICN for the endocrown groups, which led to an opposite result in the post-core groups, resulting in higher values for LS. For the PDL, this was identified as being the exact opposite of resin cement. The fatigue performance was estimated by applying the Pmax values of restorations to Equation (1) and the Pmax values of dentin to Equation (2) (Table 2). When the fatigue performances of restoration and dentin were compared, LS demonstrated better results each time. The dentin tissue was found to be less durable than the restorative material in the estimated fatigue performance values calculated for the restorative material (Figure 8) and the dentin tissue (Figure 9). Of the materials, configurations prepared with LS showed significantly better fatigue performance than those prepared with the PICN. It can be said that the survival of dentin tissue will be longer in all configurations prepared with LS. ECER showed the most unsuccessful fatigue performance in relation to dentin, while the post-core groups generally showed successful results. The ECIR group with internal retention showed a result close to PC1, while the post-core group prepared with two or more walls showed better fatigue performance. The PC4 group with a circumferential ferrule demonstrated the best fatigue performance.
# The Fast Discrete Tchebichef Transform Algorithms for Short-Length Input Sequences
## 5 Discussion of Computational Complexity  
In Table 2, we present the number of additions, shifts, and multiplications for the 1D DTT algorithms known from the literature. It is important to note that in references [1,41], the number of additions and shifts for integer DTT was evaluated. Therefore, we added to the corresponding rows of Table 2 the minimal number of multiplications needed to implement real DTT. As a consequence, the results in Table 2 show that the number of multiplications for the proposed algorithm is the same for \(N=4\) and \(N=8\) as compared with [1,41]. At the same time, we reduced the multiplication by 67-72% compared with [8]. However, the number of additions increased by 12-66%, and additional shifts are required.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{\begin{tabular}{c} **Reference, Year** \\ **of Publication** \\ \end{tabular} } & \multicolumn{3}{c}{\(N\)=4**} & \multicolumn{3}{c}{\(N\)=8**} \\ \cline{3-8}  & & **Mults.** & **Adds.** & **Shifts** & **Mults.** & **Adds.** & **Shifts** \\ \hline \begin{tabular}{c} [PERSON], \\ [PERSON] \\ \end{tabular} & [8], 2019 & 6 (\(-\)67\%) & 8 (+12\%) & 2 (+150\%) & 29 (\(-\)72\%) & 32 (+66\%) & 12 (+125\%) \\ \hline \begin{tabular}{c} [PERSON], \\ [PERSON], \\ [PERSON], \\ [PERSON] \\ \end{tabular} & [1], 2013 & – & – & – & 8 (0\%) & 44 (+20\%) & 29 (\(-\)7\%) \\ \hline \
\begin{tabular}{c} [PERSON], \\ [PERSON], \\ [PERSON] \\ \end{tabular} & [41], 2008 & 2 (0\%) & 10 (\(-\)10\%) & 4 (+25\%) & – & – & – \\ \hline
\begin{tabular}{c} Proposed \\ algorithm \\ \end{tabular} & – & 2 & 9 & 5 & 8 & 53 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of operations for the existing algorithms.  
Figure 7: Diagram of the memory consumption for real 1D DTT, which is implemented via a direct matrix-vector product (black bars) and the proposed algorithms (gray bars).  
The advantage of the proposed solutions is that the length of the input data sequence for the proposed DTT fast algorithms is not limited by 4 and 8. The obtained algorithms are multiplier-free for integer DTT. They can calculate integer DTT and real DTT. The data flow graphs constructed for the proposed algorithms have a modular structure that is well-suited for hardware implementation. A carefully selected heuristic for certain values of \(N\) can greatly decrease the computational complexity of the DTT algorithm, as in [1]. However, extending the proposed algorithms to cases where \(N\) is greater than 8 enables the design of a fast DTT algorithm for input sequences of any length.
## 2 Methods and Data Sources
### Methods for Estimating Carbon Sinks in Semic Areas  
Currently, there are two main approaches for estimating terrestrial ecosystem carbon sinks: \"bottom-up\" and \"top-down\" [35]. The \"bottom-up\" approach integrates local observations and modeling results to the entire study area, including inventory-based, eddy covariance, and ecosystem process modeling methods. On the other hand, the \"top-down\" approach relies on the inversion of atmospheric carbon dioxide concentrations to estimate carbon sinks.  
The \"bottom-up\" approach allows for estimating regional carbon sinks based on more accurate observational data. However, it requires integrating multiple data types and often necessitates long-term observations, making data acquisition challenging and limiting its applicability at large scales. On the other hand, the \"top-down\" approach enables the calculation of vegetation carbon sinks over large areas by utilizing real-time data. Remote sensing techniques can help overcome the challenges associated with data acquisition. Therefore, in this study, MOD17A3 NPP remote sensing data are used to estimate vegetation's net primary productivity (NPP) in national scenic areas. Additionally, annual precipitation and temperature data estimate soil microbial respiration. By combining NPP and soil microbial respiration, the net ecosystem productivity (NEP) is calculated to represent the vegetation carbon sinks in scenic areas.
## 5 Experiments and Results
### Ablation Study
#### 5.4.2. Neighbor Features with Different Distance Thresholds  
Whether or not there are multi-agent interactions is measured by the distance threshold \(D\) as discussed in Section 4, so we should estimate the effect of changing the values and search for the optimal one from the overall perspective. In our ablation tests, the distance threshold is set to be 0 feet, 30 feet and 60 feet separately. Particularly, when \(D=0\), it means that there are no other interactions except for the target vehicle itself. As depicted in Figure 8, the scenario of \(D=0\) has the largest amount of RMSE errors compared with models that take other movements and traffic rules into account. This indicates that considering intersection mechanisms helps improve our model's prediction accuracy. However, in fact, the target vehicle is more likely to take adjacent agents' features in consideration in its decision making. Thus, if we blindly increase the distance threshold to embrace spare objects, it will result in huge computational complexity but affect few outcomes, which is obviously laborious and unnecessary. As we can see from Figure 8, the model with setting \(D=30\) achieves the lowest evaluation errors in all prediction horizons. Accordingly, we are able to deduce the fact that the more appropriate perception range for most vehicles is around 30 feet when they are driving.  
Figure 8: The evaluation of neighbor features with different distance thresholds.  
Figure 7: Effectiveness of traffic rules features investigation.
## 4 Verification and Analysis of Model Accuracy
Therefore, the universal kriging model, which introduced the trend variables over the whole spatial distribution, was relatively good, with the mean absolute error of 11.46 to 12.68 mm. Figure 10: Comparison of MAE of different models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{3}{*}{**Measuring Points**} & \multirow{3}{*}{**Measured Value (mm)**} & \multicolumn{2}{c}{**IDW**} & \multicolumn{2}{c}{**Ordinary Kriging**} & \multicolumn{2}{c}{**Universal Kriging**} & \multicolumn{2}{c}{**VIP-Cokriging**} \\ \cline{3-10}  & & **Repaired Value (mm)** & **Relative Value (mm)** & **Relative Value (mm)** & **Relative Value (mm)** & **Relative Value (mm)** & **Relative Value (mm)** & **Relative Error** \\ \hline SA6-1 & 39.09 & 108.96 & 178.73\% & 108.90 & 178.58\% & 52.61 & 34.59\% & 50.51 & 29.23\% \\ \hline SA6-2 & 85.12 & 104.76 & 23.07\% & 89.91 & 5.63\% & 90.03 & 5.77\% & 87.74 & 3.08\% \\ \hline SA6-3 & 128.41 & 121.42 & 5.45\% & 124.27 & 3.23\% & 115.36 & 10.17\% & 127.85 & 0.44\% \\ \hline SA6-4 & 176.22 & 163.89 & 7.00\% & 164.10 & 6.88\% & 168.71 & 4.26\% & 173.15 & 1.74\% \\ \hline SA6-5 & 194.3 & 167.87 & 13.60\% & 178.28 & 8.25\% & 182.82 & 5.91\% & 190.19 & 2.12\% \\ \hline SA6-6 & 198.11 & 173.32 & 12.51\% & 183.11 & 7.57\% & 159.29 & 19.60\% & 193.97 & 2.09\% \\ \hline SA6-7 & 187.09 & 170.71 & 8.76\% & 175.31 & 6.30\% & 177.96 & 4.88\% & 183.60 & 1.86\% \\ \hline SA6-8 & 150.56 & 130.33 & 13.43\% & 137.23 & 8.86\% & 136.33 & 9.45\% & 147.58 & 1.98\% \\ \hline SA6-9 & 93.09 & 80.71 & 13.30\% & 89.98 & 3.34\% & 95.58 & 2.67\% & 93.50 & 0.44\% \\ \hline SA6-10 & 71.71 & 98.06 & 36.74\% & 99.46 & 38.70\% & 83.40 & 16.30\% & 79.68 & 11.11\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between measured values and repaired values of different models (24 December 2020). Overall, the VIP-cokriging model had the highest accuracy. The relative error of non-edge measuring points was mostly controlled within 3%, and the relative error of the edge measuring points was reduced compared with the other three models. Its mean absolute error was 3.85 to 4.08 mm, which was 83%, 77%, and 67% lower than that of the inverse distance weighting model, the ordinary kriging model, and the universal kriging model. The main reason was that cokriging introduced one or more auxiliary environmental variables that had a strong correlation with the effect variables, and using the cross-correlation between them could effectively improve the spatial expansion accuracy of the main variable at the unknown points, which could solve the problem of the insufficient number of measuring points.
## 4 Sewage Treatment Process Combined with MOPSO Algorithm  
### Energy Consumption and Effluent Quality  
In order to ensure the sewage treatment process is in an optimal operation state, the analysis starts from the energy consumption and effluent quality models. Their expressions are as follows:  
\[EC=\frac{1}{T}\times\int_{t_{0}}^{t_{f}}\bigg{[}\frac{S_{O,sat}}{1800}\sum_{k=1} ^{k=5}V_{k}K_{L}a_{k}(t)+0.004Q_{a}(t)+0.08Q_{r}(t)+0.05Q_{w}(t)\bigg{]}dt \tag{4}\]  
\[EQ=\frac{1}{T}\times\int_{t_{0}}^{t_{f}}\Big{[}2 SS(t)+COD(t)+30S_{Nkj}(t)+10S_{ NO}(t)+2 BOD_{5}(t)\Big{]}\times Q_{e}dt \tag{5}\]  
\[s.t\Bigg{\{}\begin{array}{l}N_{tot}<18\ \mathrm{mg}\cdot\mathrm{L}^{-1}, COD<100\ \mathrm{mg}\cdot\mathrm{L}^{-1}\\ S_{NH}<4\ \mathrm{mg}\cdot\mathrm{L}^{-1},SS<30\ \mathrm{mg}\cdot\mathrm{L}^{-1}\\ BOD_{5}<100\ \mathrm{mg}\cdot\mathrm{L}^{-1}\end{array} \tag{6}\]  
In the formula, \(EC\) represents energy consumption; \(EQ\) represents the effluent quality; \(V_{k}\) represents the volume of the \(k\) th reaction tank. \(K_{L}a\) is the oxygen transfer coefficient, used to control the dissolved oxygen \((S_{O})\) concentration; and \(S_{O,sat}\) is the oxygen setpoint. \(Q_{a}\) is the internal reflux, used to control the concentration of nitrate nitrogen \((S_{NO})\). \(Q_{i}\) is the external return, used to control the concentration of the mixed suspension solids (MLSS); \(Q_{w}\) is the sludge discharge. \(Q_{e}\) is the effluent flow; \(SS\) is the concentration of suspended solids. \(COD\) is the chemical oxygen demand; \(S_{Nkj}\) is Kjeldahl nitrogen concentration. \(N_{tot}\) is the total nitrogen concentration; \(S_{NH}\) is the ammonia nitrogen concentration.  
In sewage treatment, in addition to meeting the requirements of effluent quality, it is also necessary to reduce the operating cost of sewage treatment. In order to solve these problems scientifically and rationally, the real-time optimization control concept provides the concrete realization steps.
## 3 Experimental Setup  
### Data  
In all the experiments in this paper, we used the RSR2015 text-dependent speaker verification dataset [22]. This dataset consists of recordings from 157 male and 143 female, and nine different sessions for each speaker. This data are divided into three speaker subsets: background (bkg), development (dev) and evaluation (eval). Furthermore, it is also divided into three parts based on different lexical constraints. We develop our experiments in Part I and Part II of this data set. Part I is based on 30 phonetically balanced pass-phrases. Part II contains 30 short commands to control. The average length of the utterances in the second part is half the length of the utterances in Part I. With both parts, we employ the bkg and dev data (194 speakers, 94 female/100 male) for training. The evaluation part is used for enrollment and trial evaluation.  
This dataset has three evaluation conditions, but, in this work, we have only evaluated the Impostor-Correct case that is the most challenging condition where the non-target speakers pronounce the same phrase as the target speakers.
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 2 Doppler Center Frequency Retrieval Method
### Eliminating the Influence Algorithm and Model  
The model function is configured with ASAR Doppler measurements and ECMWF winds, which correlate the Doppler shift and wind field based on the C-band radar configuration. The model is expressed as follows [16]:  
\[f^{DA}=CDOP(\phi,u_{10},\theta,pol), \tag{5}\]  
where \(u_{10}\) denotes the wind speed at a height of 10 m, \(\phi\) is the angle between the direction of the SAR antenna and the wind direction, \(\theta\) is the incidence angle, and \(pol\) is the polarization of the radar.  
In this paper, to retrieve the Doppler shift caused by wind, the retrieved wind from the SAR (SLC image) is used to denote the parameters for the CDOP model function. Then, the \(f_{b}\) caused by Bragg waves and \(f_{ls}\) caused by large-scale waves are regarded as wind field modulation results. Thus, \(f^{DA}\) can be approximated as:  
\[f^{DA}\approx f_{ls}+f_{b}, \tag{6}\]Therefore, processing of the Doppler anomaly can be derived as:  
\[f_{c}=f_{dc}^{plus}-f^{DA}, \tag{7}\]  
For an easier geophysical interpretation, the Doppler shifts are converted to the surface radial velocity (\(V_{c}\)) using the following relation:  
\[V_{c}=-\frac{\lambda f_{c}}{2\sin\theta}, \tag{8}\]
## 6 Result Discussion
### Result Accomplished
#### 6.3.3 Analysis of Cost  
To assess the practicality of the proposed system, it is sufficient to understand the overall cost involved in the implementation. The proposed system defines the term _cost_ as overall resources and operations involved in executing the proposed strategic game model in order to resist malicious node participation. The mathematical expression used for this purpose is as in Equation (9):  
\[C=\frac{C_{TRM}+C_{stages}+C_{pay}+C_{Cloud}}{n} \tag{9}\]  
Equation (9) shows that the evaluation of cost, \(C\), depends upon the cost of maintaining TRM, \(C_{TRM}\), the cost of three stages of operation, \(C_{stages}\), the cost of payoff computation, allocation, and updating, \(C_{pay}\), and the cost of dual-cloud-based game modelling, \(C_{Cloud}\), with respect to the total number of deployed nodes, \(n\). The proposed system uniformly allocates the hypothetical and dimensionless numerical value of one unit of consumed cost for each of the four operations. With each operation observed in 1000 simulation rounds, the analysis arrived at the outcome shown in Figure 8, which exhibits a lower cost for the proposed system.  
From the observation in Figure 8, it can be noted that Nash equilibrium is not significantly different from Bayesian Nash Equilibrium. The prime reason for the higher cost involved in this existing system is that the Nash equilibrium strategy depends on all the known and correct strategies of other nodes. Such computation can never be carried out, especially if the intrusion type is highly dynamic, where a node could alter their strategy of attack even if they announce a different strategy during game interaction (to increase their payoff). Furthermore, a similar problem exists in Bayesian Nash Equilibrium. Still, other forms include extensive operation steps to compute payoffs usually maintained in a multi-dimensional matrix. Hence, the system takes more effort to compute the payoff and visit and re-visit the payoff matrix every time, increasing the value of the \(C_{pay}\) parameter. [PERSON]'s game has an obvious advantage in this regard. However, the payoff still significantly reduces for the second mover. Hence, it has to carry out more operations to get an appropriate payoff, increasing the value of \(C_{pay}\) and \(C_{stages}\), incurring more costs to complete the entire security operation.  
Figure 7: Comparative analysis of energy consumption.
## 4 Experimentation
### Acoustic Emission Signals Preprocessing  
Airborne acoustic emission signals were acquired due to them lying in the audible frequency range of 20 Hz to 20 kHz [26]. The advantage of the air-borne AE signal acquisition is that the experimental setup used was rich in relevant information yet economically cheap. The whole machining process was acquired on a single ordinary microphone with a sampling frequency of 44.1 kHz to meet the Nyquist criteria. Figure 11 illustrates the flow diagram of the preprocessing of the AE signal. Thirty recordings were taken for each class of new, used, and worn-out for all tools and materials, as discussed in the experimentation section. Each recording was of 10 s duration and was further segmented into 10 pieces using a MATLAB(r) script. Furthermore, the segments were saved in a.mat file format containing the time-domain vector of 44,100 samples. In total, six cases were formed with all the combinations. Figures 12-14 are the raw 10 s AE signal time-domain representations for new, used, and worn-out tool categories (without any preprocessing) for the aluminum job and carbide tool for Case-1, respectively. The amplitude difference could be observed in the figures. Figure 15 presents the amplitude contrast for Case-1. A short window comparison of a few milliseconds of 10 s signals is shown. The new tool with the blue wave has the minimum amplitude, yellow for the medium tool has moderate amplitude, and red has the highest amplitude, representing a worn-out tool signature.  
Figure 11: Flow diagram of data acquisition and preprocessing.  
Figure 10: All three categories of high-speed steel (HSS) tool are shown where the level of tool degradation can be observed on each tooltip.  
Figure 14: Plot of raw AE signal of 10 s duration for worn-out carbide tool on aluminum work-piece (Case-1).  
Figure 12: Plot of raw acoustic emission (AE) signal of 10 s duration for new carbide tool on aluminum work-piece (Case-1).  
Figure 13: Plot of raw AE signal of 10 s duration for used carbide tool on aluminum work-piece (Case-1).
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
###### Abstract  
The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data.  
metaheuristics; evolutionary algorithm; bees algorithm; harmony search algorithm; swarm intelligence; swarm-based optimization; mixed integer and integer linear programming; combinatorial optimization; optical network design; optical node resources +
Footnote †: journal: Applied sciences
## 4 Validation of the Proposed SDoF Model  
For the purpose of validating the proposed SDoF model, the analytical and numerical results are compared in terms of the overall dynamic response histories and some characteristic response parameters.