esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Precipitation Process in Aging Steel Pipeline Weldments by Thermoelectric Power Means
###### Abstract  
The microstructural changes due to the aging process in steel pipeline weldments as a function of the thermoelectric power (TEP) were studied. In general, the thermoelectric methods are based on the well-known Seebeck effect. The thermoelectric methods monitor the TEP via an electron flux induced by a temperature gradient in metallic materials, which is affected by the different types of defects that are present in the atomic lattice, such as atoms in the solid solution, precipitates and dislocations. In this present study, the relationship among the TEP data, hardness and the microstructure of steel pipeline weldments was investigated. In addition, the coarse and dendritic grain structure of the welding material is extremely and unpredictably anisotropic. Such microstructures are no longer direction-independent to the electron flux. Therefore, it has an opposite negative effect on the TEP and overlaps the precipitation effect due to the aging process. TEP and hardness measurements were obtained in each zone of the weldments. For each section of the weldment, the weld bead (WB), heat affected zone (HAZ) and base metal (BM) were found to correspond to particular values of TEP. The relationship between the TEP and the microstructure of a weldment of X60 and X65 micro-alloyed steel that was artificially aged was obtained using the conventional contact TEP technique (hot-tip) and scanning electron microscopy (SEM). It was found that thermoelectric power is very sensitive to the aging process in the two-studied steel pipeline weldments.  
thermoelectric power; hardness; aging; pipeline; weldment +
Footnote †: journal: Applied Physics Letters
Textile wastewater treatments are carried out through different processes such as biological [13; 14; 15; 16; 17; 18; 19], oxidative [20; 21; 22], coagulation [23; 24; 25; 26], flocculation [27], membranes [28; 29], and photocatalysis [30; 31; 32; 33; 34; 35; 36].  
Adsorption is one of the most important and efficient purification techniques applied to various nonbiodegradable polluting compounds, producing high-quality water. There are several materials that can be used for adsorption. Among these are microporous materials [37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. Activated carbon is commonly used for these applications [48; 49; 50; 51]. Other adsorbent materials like silica [52], chitosan [53; 54; 55], and natural materials [56; 57; 58; 59] are used. Carbon nanotubes are achieving great success, as they are very versatile materials and applicable in many fields [60]. They can generally be used as reinforcing fibers [61; 62; 63; 64; 65] and as a material capable of efficiently adsorbing different substances [66; 67; 68; 69; 70; 71; 72; 73]. A particular application for which carbon nanotubes have shown promise is the removal of dyes in wastewater [74; 75; 76; 77; 78].  
In our previous work, the removal of Reactive Black 5 (RB5) dye from aqueous solutions with carbon nanotubes in static systems was investigated by evaluating the influence of the nature of the carbon nanotubes, the initial concentrations of dye, the stirring speed, and the times on the adsorption process [78].  
In this work, in particular, we wanted to continue the previous research [78] by studying the absorption of the Reactive Black 5 dye from aqueous solutions with carbon nanotubes in semicontinuous processes, in order to search for the optimal conditions for the purification of water contaminated.  
Reactive black-5 dye was chosen as the representative dye present in textile wastewater. It was chosen because it is one of the most common dyes used in the textile industry thanks to the presence of reactive groups in the molecule that promote the formation of covalent bonds with fibers. Furthermore, it also was chosen for its high solubility in water, which allowed us to prepare its aqueous solutions to be used in the experimental procedures [79].  
For this purpose, different adsorption columns were studied and subjected to different adsorption cycles that were suspended only when the permeate was colorless. The characteristics of each column were studied and compared.
## Materials and Methods
### Milk Fatty Acid Analysis  
The extraction of fat from milk samples was carried out according to White et al. [50], with some modifications. The samples (5 g) were homogenized with ethanol (6.7 mL) and mixed using a Vortex mixer for 60 s. Then, a diethyl ether-heptane mixture (10 mL, 2:1 \(\
u\)/\(\
u\)) was added and mixed by vortexing for 60 s. Samples were then centrifuged at 3000 rpm for 10 min. The diethyl ether phase containing the extracted lipids was transferred and the residue was extracted 3 times using the same procedure. The filtrate was concentrated in a rotary evaporator at 36 \({}^{\circ}\)C. Then, the extracted lipid phase was dissolved in hexane and purified using sodium chloride saturated solution (3 mL). The hexane phase containing purified lipids was dried over anhydrous sodium sulfate and under nitrogen. Fatty acid methyl esters (FAMEs) were prepared using potassium hydroxide in a methanol solution (2N KOH in anhydrous MeOH), according to Ichihara et al. [51], with some modifications. Then 100 mg of extracted fat in a 10 mL glass-stoppered test-tube was dissolved in 2 mL n-hexane. A solution of 2 N potassium hydroxide in methanol (300 mL) was added. The tube was vortexed for 30 s and allowed to react for 4 min at room temperature. A 1 mL aliquot of the upper organic phase was analyzed by high-resolution gas chromatography. For the analysis, a Perkin Elmer Auto-system XL model gas chromatograph, equipped with a fused silica capillary column (100 m 0.25 mm i.d.; 0.20 \(\upmu\)m film thickness; mod. SP 2380; Supelco Bellofonte, company, city, state abbreviation, USA), a flame ionization detector (FID), and a programmed temperature vaporizer (PTV), was used. The oven was set at an initial temperature of 100 \({}^{\circ}\)C for 5 min. The temperature was then increased at a rate of 3 \({}^{\circ}\)C min\({}^{-1}\) to 165 \({}^{\circ}\)C and held for 10 min. This procedure was followed by a second increase in temperature at a rate of 3 \({}^{\circ}\)C min\({}^{-1}\) to a final temperature of 260 \({}^{\circ}\)C, which was held for 28 min. The PTV operating conditions were 50 \({}^{\circ}\)C for 0.1 min, 400 \({}^{\circ}\)C min\({}^{-1}\) to 260 \({}^{\circ}\)C, and held at 260 \({}^{\circ}\)C for 5 min. The split ratio selected was 1:30. The carrier gas was helium, with a linear velocity of 20 cm s\({}^{-1}\). The FID conditions were a 10:1 ratio (air/hydrogen) and the temperature was 270 \({}^{\circ}\)C. The identification and quantification of separated peaks were performed using the Supelco 37 Component FAME MIX (Supelco Bellofonte, PA, USA), a CLA isomer mixture (Nu-Chek Prep., Inc. Elysian, MN, USA), as external standards, and GC retention data available in the literature. A CRM 164 milk fat reference supplied by the Community Bureau of Reference (Commission of the European Communities, Brussels, Belgium) was used to obtain response factors (Rfs) and convert areas of individual FAs into weight percentages of total fat. The FA analysis was performed in triplicate.
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 1 Introduction
Especially, we show that we can adjust the accuracy between the precision and recall of each method by controlling a parameter. Through extensive experiments, we measure the accuracy of the speech segment detection methods with a variety of SNRs and evaluation metrics and observe that a different speech segment detection method shows the best accuracy for each group of SNRs and evaluation metric. This result indicates that we need to select the most effective speech segment detection method for a given target environment. 3. We incorporate the best speech segment detection method for a target environment into a denoising process. Through extensive experiments, we show that the accuracy of the proposed preprocessing strategy is comparable to or even better than that of the original Wavenet-based denoising in terms of multiple evaluation metrics of denoising, i.e., SNR, STOI, and PESQ, while it can reduce the denoising time of the Wavenet-based denoising by 40.06-50.76% according to the used speech segment detection method. The organization of the paper is as follows. In Section 2, we explain preliminaries. In Section 3, we present the proposed method. In Section 4, we describe the experimental results. In Section 5, we conclude the paper.
## 4 Simulation Environment and Performance Metrics
### Performance Metrics  
The proposed scheme's performance evaluation includes assessing the network's resource efficiency (RE) and throughput. The RE is defined based on how the network allocates resources to each slice requested by the NSR. Ideally, the network should effectively allocate the requested resources without exhausting most of the physical infrastructure resources. As such, the network's RE is expressed as,  
\[\varphi=\frac{\sum_{i=1}^{I}C_{NSR_{i}}+\sum_{i=1}^{I}B_{NSR_{i}}+\sum_{i=1}^{I }N_{NSR_{i}}}{(C_{\mathcal{S}}+B_{\mathcal{S}}+N_{\mathcal{S}})*L_{hops}} \tag{20}\]  
The network throughput measures the ratio of all NSRs served to the total number of NSRs received at time \(t\). The network must serve all requested network slices by effectively allocating resources to achieve the highest possible throughput at each time step. The network's throughput is calculated as,  
\[\Gamma=\frac{\sum_{t=0}^{T}NSR_{served,t}}{\sum_{t=0}^{T}NSR_{received,t}} \tag{21}\]  
Additionally, the proposed scheme utilizes the cumulative mean rewards (Equation (19)) earned for performance evaluation as an additional metric.
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 4 Experimental Verifications  
To verify the effectiveness of the DAA algorithm, the first phase of the StarLink constellation is designed in this paper using Satellite Tool Kit (STK) [29]. The DAA algorithm is verified and analyzed by MATLAB based on the satellites' track data derived from STK. The DAA algorithm is compared with the access selection algorithm based on a single parameter [6; 10] and the access selection algorithm based on multi-attribute [18].
## 2 Materials and Methods
### Aggregation Phenomenon  
When the concentration of molecules increases or the condition of the solution changes such as pH, molecules form aggregates with van-der-Waals interaction, hydrogen bonds, or electrostatic interaction between molecules. The aggregation induces changes in absorption and fluorescence spectra due to the energy shift by inter-molecular interaction. For J-aggregation, the energy of the aggregation state under the interaction between the molecular transition moments, \(E_{k}\), is given as follows under the point-dipole approximation [45].  
\[E_{k}=E_{0}+2J\cos\biggl{(}\frac{k\pi}{N+1}\biggr{)} \tag{1}\]  
\[J=\frac{M^{2}}{4\pi\varepsilon r^{3}}\biggl{(}1-3\cos^{2}\theta_{0}\biggr{)} \tag{2}\]  
where \(E_{0}\) is the electronic excitation energy of the monomer, \(k\) is the state number of the exciton, \(N\) is the aggregation number, \(M\) is the transition dipole moment, \(\varepsilon\) is the permittivity of the surrounding medium, \(r\) is the aggregation distance, and \(\theta_{0}\) is the aggregation angle. The sign of the energy shift given by 2 \(J\) changes at the aggregation angle of 54.7 according to Equation (2). With these equations, we can discuss the arrangement of the constituent molecules in the aggregate from the absorbance spectrum [46]. When the energy shift is negative, aggregates are called \"J-aggregates\" and when it is positive, aggregates are called \"H-aggregates.\" This energy shift can appear as an absorbance change in the relevant spectral range. In our experiment, we detected a small absorbance change upon laser irradiation, which suggests the aggregation-state change induced by the laser light.
## 2 Related Works
### Metaheuristic Techniques  
[PERSON] et al. [14] proposed a genetic algorithm-based approach to schedule tasks. In their proposed approach, the fitness was calculated based on energy usage, and their proposed approach was found to be better at improving makespan and energy consumption compared to variations of PSO and other methods. [PERSON] et al. [15] proposed a deadline-aware approach that allocated tasks to VMs with the aim of minimizing makespan and energy consumption. They achieved this by using learning automaton to achieve better performance for metrics such as the success ratio, makespan, and energy usage. [PERSON] et al. [16] proposed a three-level task scheduling approach using ACO and by prioritizing the optimization of makespan and energy usage. Their deadline-aware approach considered the inter-relationship between three entities, namely tasks, VMs, and hosts, when performing task assignment. They compared their approach with variations in terms of the makespan, deadline, and energy consumption metrics.  
proposed a task scheduling approach for minimizing makespan and energy consumption. In order to achieve this, they used a modified ant colony optimization approach taking the deadline of tasks into account for the modeling of the behavior of pheromones. Secondly, they used a backfillingalgorithm to improve the task waiting times for each VM. [PERSON] et al. [18] proposed a modified ant colony optimization algorithm with the aim of minimizing the cost and makespan associated with processing a task set. They proposed weighted versions of the pheromone update operation and created ant methods to improve the performance over basic ACO. [PERSON] et al. [19] proposed a modified PSO approach that sought to remove the uncertainty caused by having real-valued intermediate positions during the optimization process. They achieved this by using integer values to represent positions of solutions. For the multiple objectives of optimizing cost and makespan, their approach achieved better results compared to other variants of the PSO approach. [PERSON] et al. [20] considered makespan and energy consumption to be the target metrics for optimization. They used DE and a multi-criteria decision making-based approach to divide the task set into priority sets. They further used a fuzzy logic-based PSO method to dynamically schedule tasks for their respective destination VMs. [PERSON] et al. [21] proposed a task scheduling approach using modified differential evolution. They proposed a new scaling factor that varied with the iteration number to both explore the solution space broadly and to look for a more optimal solution when a promising initial solution was found. This, combined with a procedure to improve convergence, allowed their approach to achieve better performance compared to several metaheuristic and heuristic techniques.
## 2 Materials and Methods
### Data Processing  
#### 2.4.1 Estimation of \(fPAR\)  
The \(fPAR\) is the fraction of the incident photosynthetically active radiation (PAR) absorbed by vegetation, which was estimated using vegetation indices. Because the normalized difference vegetation index (NDVI) is susceptible to canopy structure and chloroplast absorption, it has been suggested to be proportional to \(fPAR\) for a fixed view angle [39]. Generally, a large number of studies have used a simple linear model to estimate \(fPAR\) by NDVI, whereas NDVI generally exhibited a saturation effect at a high LAI value. In order to eliminate the saturation effect of the NDVI, some studies have explored the performance of other vegetation indices as a proxy of \(fPAR\), such as the wide-dynamic-range vegetation index (_WDRVI_) [40], which can be expressed as [41; 42; 43]:  
\[WDRVI=\frac{\alpha\times Ref_{800}-Ref_{680}}{\alpha\times Ref_{800}+Ref_{680}} \tag{4}\]where \(Ref\) is the reflectance which was measured by the upwelling radiance divided by the downwelling radiance, and the subscript indicates the corresponding wavelength; \(\alpha\) represents a weighting coefficient, which can be set to either 0.1 or 0.2 [42; 44]. In this study, we used the value of 0.1 to represent \(\alpha\) and calculated \(WDRVI\)[44]. By using the value of 0.1 to represent \(\alpha\), the \(WDRVI\) has been found to be closely related to \(fPAR\) based on SCOPE simulation results (Figures S3 and S4) [41], thus we also used the existing linear model to estimate \(fPAR\) using the \(WDRVI\), which can be expressed as:  
\[fPAR=0.516\times WDRVI+0.726 \tag{5}\]  
where the coefficients of 0.516 and 0.726 were taken from the literature by [PERSON] et al. [41]. The absorbed photosynthetically active radiation (\(APAR\)) can be calculated as:  
\[APAR=PAR\times fPAR \tag{6}\]
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### TN Slicing  
Unlike the NG-RAN and 5 GC, the TN domain is out of the scope of the 3 GPP network slice concept. 3 GPP provides slicing solutions for the RAN and CN domain, but not for the TN. However, to maintain consistency on the slice established between the device and the service application, there is a need to map 3 GPP slice criteria into appropriate transport capabilities offered in the fronthaul, midhaul, backhaul and DN segments. This is not trivial.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ded Min** & **Minimum** & **Maximum** & **RAN Slice Characterisation** \\
**Slice Quota** & **Slice Quota** & **Slice Quota** & \\ \hline
10\% & 10\% & 45\% & Dedicated slice-profile 1 (dedicated + shared) \\ \hline
10\% & 20\% & 45\% & Dedicated slice-profile 2 (dedicated + priori-tized + shared) \\ \hline - & 20\% & 45\% & Prioritized slice (prioritized + shared) \\ \hline - & 0\% & 45\% & Best effort slice (can also use prioritized re-sources if spare left by another prioritized slice) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RAN slice characterization based on configured quotas.  
On the one hand, there is the need to configure WAN resources in such a way that the requirements captured in the ServiceProfile and SliceProfile can be fulfilled in the TN substrate. This requires translating network function layer requirements associated with S-NSSAI information (e.g., maximum delay budget, data rates, availability, mobility speed, usage density) into transport network characteristics that include bandwidth, latency and criteria such as traffic prioritization, directionality, protection and disjoint routes. This translation is done at provisioning time.  
On the other hand, there exists a wide availability of transport technologies in carrier networks. These technologies provide multi-layer connectivity services using different topologies (e.g., hub-and-spoke, ring, point-to-point, point-to-multipoint). Though they do not support slicing natively, these technologies are able to mimic slicing behavior, if configured (and combined) properly.  
In this section, we focus on the main enablers for these two open questions.
## 1 Introduction
This index has no restricting specific condition, as observed in the time-to-collision (TTC) when the collision risk is evaluated. Then, the proposed method finds an optimal position between the preceding and following vehicles to minimize the risk index. The contribution of this paper is as follows. The proposed method performs the real-time estimation of operation characteristics of each driver while previous studies determine the constant values of model parameters from the training data. Our approach concludes the improvement of trajectory prediction accuracy and driving safety. A driver is aware of the distance and relative speed with respect to the preceding vehicle; consequently, the driver determines its acceleration as a response. The proposed method uses the GM model to analyze the behavior of the following driver, as it is widely employed among available car-following models [10]. The model has three parameters to represent the operation characteristics. Several studies reported the optimized values of the parameters using real traffic datasets [21; 22; 23]. However, the values vary across the papers, as all of the data was recorded at different locations. Therefore, the constant values of the parameters cannot be adjusted according to the changes in the driving conditions. To address this limitation, the proposed approach aims to estimate the real-time values of the parameters using the Levenberg-Marquardt algorithm [24; 25]. This approach considers the changes in the driving conditions, unlike the previous methods based on the predetermined values. Moreover, the proposed method estimates the reaction time of the following driver in real time. The previous methods used the fixed reaction time of 1 s, although the performance of the trajectory prediction largely depends on the reaction time [13]. In this paper,the three parameters of the GM model and the reaction time are defined as the operation characteristic variables. The proposed method performs the optimization of the four considered variables and applies the result to the trajectory prediction at each time step. Owing to this approach, the trajectory prediction can be robust with respect to the individual difference between the operation characteristics of drivers and the changes in driving conditions. Consequently, the anticipation of the future risk, with a high level of precision, and the collision avoidance can be realized. This is a state-of-the-art approach, and it is expected to be applied to an advanced safety system. This paper is organized as follows. Section 2 describes the problem definition and a schematic of the proposed method. Section 3 explains the details of the proposed method. Section 4 presents the experiments and results. Section 5 describes the discussions including areas of future work. Finally, Section 6 describes the conclusions of this paper.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Paillier Federated Network  
The federated network proposed in this paper allows multiple parties to perform cooperative machine learning with isolated data. However, in practice, what the cracker actually needs is not only the data provided by the participants, but also the final model trained by multiple parties.  
According to a member inference attack proposed by [PERSON] et al. in 2017, the cracker can invade the server and infer several shadow models from the data in the server. Basedon the idea of ensemble learning, the cracker can finally get a prediction that is similar to the model trained by actual cooperation based on these shadow models. In other words, the federated model under this condition can only solve the data security problem, not the model security.  
Therefore, for model security, homomorphic encryption can be introduced into the federated learning. In addition, the core idea of homomorphic encryption is, after encrypting plaintext \(a\) to ciphertext \(c\), the result of performing some operations on \(c\) in the ciphertext space, which is equivalent to the result of encryption operations on \(a\) in plaintext space. The encryption operation can be expressed as:  
\[E(a)\oplus E(b)=E(a\otimes b) \tag{6}\]  
In Equation (6), \(E\) represents an encryption algorithm, and \(a\) and \(b\) represent two different plain texts. \(\oplus\) and \(\otimes\) represent operators. If the operation is a multiplication operation, then the homomorphic encryption satisfies the multiplicative homomorphism, for instance, the RSA algorithm [20]. If the operation is an addition operation, the homomorphic encryption algorithm satisfies the additive homomorphism. The Paillier algorithm is the most famous one [22]. In addition, if the algorithm satisfies both additive and multiplicative homomorphism at the same time, then the encryption algorithm satisfies full homomorphism [23]. Since in MLP we need to sum the gradient data, the Paillier algorithm can thus be used to do homomorphic encryption.