esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 4 Recent Trends in Remanufacturing
### Smart Remanufacturing  
With the emergence of industry 4.0, new techniques are being used to improve manufacturing methods. This will be very beneficial to remanufacturing given that a design for remanufacturing perspective is kept in mind during the manufacturing stage, thereby also directly affecting remanufacturing. Smart remanufacturing with the help of industry 4.0 has to follow the norms of sustainable development, which consist in balanced economic, social and environmental performance, also referred to as the triple bottom line [130]. [PERSON] & [PERSON] [131] have pointed out the ways in which smart remanufacturing meets up with the requirements of these three pillars of sustainability. In terms of the social needs, people need to engage in sustainable manufacturing entrepreneurship and collaborative working [132]. There is also a need for a collaborative environment, like personnel being trained using virtual or augmented reality and additive manufactured parts. The design philosophy needs to be understood, whether it be design for disassembly, service, remanufacture or sustainability [67]. In a factory equipped with smart remanufacturing technologies, factory equipment is managed using real-time collaborative,intelligent, distributed decision making and control [133]. The facilities need to be flexible, scalable, reconfigurable, customizable, enterable, modular and diagnosable. These factors are very important given that the systems will need to understand their environment to ensure the safety of the operators before accepting remotely controlled signals [134].  
There are various data that are shared in such factories; the data are a combination of all the data collected during the entire lifecycle of the product. These data include product data, which consist of the data collected during the use stage of a product and the remanufacturing data shared to service providers or OEMs, process data, logistics data and business data [135]. The combination of these data could be used in remanufacturing with all the systems interconnected. At the beginning-of-life of a product, OEMs need to make product nominal data available and during the use stage the product performance and status data need to be monitored against nominal data. At the end-of-life stage, product nominal performance and status data are used to feed remanufacturing product data. The synchronization of this data is what makes smart remanufacturing so efficient, thereby solving a lot of problems encountered in remanufacturing, such as product identification, original production planning and the products' lifecycle history and ratings from users.
## 3 Results and Discussion
### Minerals Content in Bread Samples
#### 3.2.1 Macrominerals
The addition of further portions of CP significantly increased the Ca content of CB20% and CB30%. Compared to the control sample, CB30% contained more than two times more Ca. A different trend was observed in bread with the addition of PP. The addition of PP at 20% caused a significant increase in Ca in the bread, though increasing its contribution by another 10% did not cause significant differences in Ca content. CB30% bread contained 25% more Ca than PB30% (\(p<0.05\)). This is attributable to the higher percentage of Ca in CP. The bread showed the lowest changes in its Mg content concerning the addition of vegetable powders. The addition of CP and PP caused an increase in its content in breads; however, no significant changes in its amount were noted in both cases. The content of Mg in bread with the addition of both powders fluctuated within similar limits (169.43-203.60 mg/kg for bread with CP; 173.07-209.63 mg/kg for bread with PP). Despite the Mg content of vegetable powders at about 90-110 mg/100 g, their addition at 10, 20, and 30% was not sufficient to significantly increase the Mg content of the final bread. Vegetables are not a good source of Mg and fortification toward increasing Mg content with the addition of CP and PP in wheat bread would not be effective. For more effective fortification, an alternative could be the addition of other flours--rye, whole wheat, or out flour--and additives--seeds or nuts--which are rich sources of magnesium [64,65].
# CD44 Depletion in Glioblastoma Cells Suppresses Growth and Stemness and Induces Senescence
## 2 Materials and Methods
### Generation of CD44 KO Cells  
_CD44_ KO U251 MG cells were established by CRISPR/Cas9 gene editing technology. Single-guide RNAs (sgRNA) were designed using the online tool at www.broadinstitute.org/gpp/public/analysis-tools/sgrna-design (accessed on 16 September 2016) and cloned into the BsmBI site of the lentiCRISPRv2 lentiviral vector [24]. Two sgRNA were designed to target two distinct sequences within exon 2 (166 bp in length) of the human gene according to the GTEx Portal (www.gtexportal.org, accessed on 16 September 2016). Exon 2 is the first constitutive exon, not subjected to alternative splicing. The sgRNA sequences were G60, 5'-CTTTGCAGGTGTATTCCACGGtgg-3', and G40, 5'-CTACAGCATCTCTCGAGGgg-3' (PAM sequences shown in lower case letters). Lentiviruses containing the sgRNA, the Cas9 nuclease, and puromycin N-acetyl-transferase genes were generated in HEK293T cells by co-transfection of the packaging plasmids psPAX2 and psMD2 (Addgene, Watertown, USA). Supernatants containing lentiviruses were harvested 24 h and 48 h post-transfection. For efficient transduction, polybrene (8 ug/mL) (Sigma-Aldrich Sweden AB, Stockholm, Sweden) was added to the medium. Infected cells were selected in the presence of puromycin (0.5 ug/mL) (Gibco, Life Technologies Europe BV, Stockholm, Sweden). Thereafter, they were sparsely seeded (50, 100 and 250 cells) in 10 cm plates for each sgRNA for clone selection. After expansion of single clones, screening was performed via immunoblotting and confirmed KO clones were selected for experiments. Clone #13 was excluded for further analysis because of potentially expressing a truncated version of CD44.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 4 Results and Discussion
### Effect of Lamination Sequence and Geometry  
The compressive resistance of the composite beams is poor. In the structure design, the compressive stress can be reduced using different arrangement sequences of the laminates or by changing the shape of the beam. In this study, the T- and I-shaped beams in the same lamination sequence were subjected to a uniform force and compared for stiffness, stress, and deflection. The cross-sectional views of the I- and T-shaped beams are shown in Figures 6 and 7, respectively. The length, width, and height of the analyzed beam were 0.381, 0.0127, and 0.0127 m, respectively. The symmetrical flange and web of the I-shaped beam were laminated as \([90/45/-45/0/0]_{s}\) and \([90/45/-45]_{s}\), respectively. Both the web and flange laminates of the T-beam were \([90/45/-45/0/0]_{s}\). The height and width of the T-beam were 0.02032 m and 0.0127 m, respectively. Both beams were made of AS4/3501-6 carbon/epoxy, and the thickness of the laminate was \(1.3208\times 10^{-4}\) m. The material properties were \(E_{22}=1.1\times 10^{10}\) GPa, \(\
u_{12}=0.28\), and \(G_{12}=6.55\times 10^{9}\) GPa. Both beams were simply supported structures with a uniform upward force of 1737.4 N/m. The stress  
Figure 4: Comparison of deflection and stress errors of 1D and 2D symmetrical laminated narrow flange T-beams at different aspect ratios.  
Figure 5: Comparison of deflection and stress errors of 1D and 2D non-symmetrical laminated narrow flange T-beams at different aspect ratios.  
and deflection of the I- and T-beams were obtained using the derived theoretical composite T-beam solution and the I-shaped beam formula reported in the literature [31]. The data shown in Table 1 suggests that when 0 laminations is added to the web and the height of the web is increased, the effective stiffness of the T-beam increases by 14.67% compared with that of the I-beam. Furthermore, the maximum deflection of the T-beam is observed to be 14.53% lower than that of the I-beam, thereby indicating that the fiber-reinforced composites can tolerate tensile stress better than they can tolerate compressive stress. The compressive stress of the T-beam on the lower layer of the upper flange is lower than that of the I-beam. The compressive stress on the flange is reduced by 35.64% because of the decrease in the distance between the centroid position of the T-beam and the centroid of the flange. The 0 laminations added to the web assist in increasing the effective stiffness of the beam.  
Figure 6: Structure and loading diagram of laminated composite I-beam.  
Figure 7: Structure and loading diagram of laminated composite T-beam.
# Degradation of Trace Organic Contaminants by a Membrane Distillation--Enzymatic Bioreactor
## 1 Introduction
Since the mass transfer in the MD process occurs in gaseous phase, it can theoretically achieve 100% retention of all non-volatile compounds [29]. The standalone MD process has been investigated for seawater desalination [30], industrial wastewater treatment [31], municipal wastewater treatment [32] and TrOC removal [29; 33]. Thus, the MD process was selected for coupling to an enzymatic bioreactor in this study. The aim of this study was to assess the performance of a lacase based membrane distillation--enzymatic membrane bioreactor (MD-EMBR) for the removal of TrOCs having diverse physicochemical properties (e.g., EDGs/EWGs, hydrophobicity and phenolic/non-phenolic moieties). A special focus was given to the improvement in TrOC degradation due to the addition of three redox-mediators, namely syringaldehyde (SA), violuric acid (VA) and 1-hydroxybenzotriazole (HBT) at different concentrations. In addition, performance of lacase-mediator systems was systematically compared based on TrOC degradation, enzyme stability and effluent toxicity.
## 6 Control System of USV Swarm with the Learning Ocean Current Model  
The USV determines its direction of travel by calculating both the designated position and its heading angle, utilizing a propellant positioned laterally at the front [19; 20; 21; 22; 23]. When a certain range of a given position is reached, the heading angle is recalculated, and the lateral propellant is adjusted to move to the next position. During this phase, the aforementioned ANN-PID control algorithm is utilized for precise control. The impact of external forces during motion is studied and predicted, and this information is incorporated into the control system design to enable precise positioning.  
Figure 9: Artificial neural network PID.  
To combine the external force prediction model and the ANN-PID controller, the control system shown in Figure 10 was designed.  
\[x_{new}=\frac{x-\mu}{\sigma} \tag{15}\]  
In the equation above, \(x_{new}\) is the normalized data, \(x\) is the training data, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. The reason for normalizing learning data is to reduce overfilling in learning when using ANNs.  
The control algorithm can be primarily segmented into two parts. One component involves an ANN dedicated to predicting ocean currents. It undergoes training using time-series data, predicts the magnitude of the next disturbance based on measured ocean current data, and subsequently feeds these disturbance data into the control system. The second algorithm, the ANN-PID control algorithm, differs from traditional PID controllers with fixed control gains. Instead, it dynamically adjusts the gain by employing feedback weights based on the error magnitude between the current controlled state and the target.  
The USV control algorithm using these two learning models is an algorithm that determines the next output value by adding the predicted ocean current components to the weight-adjusted output value of the ANN-PID control algorithm and delivers it to the system.
## 2 System Model
### Channel Model
The large-scale fading (in dB) between the \(k\)th GU and the \(l\)th satellite is calculated by [24]  
\[PL_{k,l}=L_{k,l}^{prop}+L_{k,l}^{sh}+L_{k,l}^{ant}+L_{k,l}^{ather}, \tag{3}\]  
where \(L_{k,l}^{prop}\) denotes the basic path loss, \(L_{k,l}^{sh}\) represents the losses due to the shadowing effect, \(L_{k,l}^{ant}\) denotes the gain losses due to beam misalignment, and \(L_{k,l}^{other}\) denotes other large-scale losses, such as atmospheric gasses attenuation and ionospheric scintillation. The basic path loss \(L_{k,l}^{prop}\) is a function of the operating frequency \(f_{c}\) and the distance \(d_{k,l}\) between the \(k\)th GU and the \(l\)th satellite, which can be expressed as  
\[L_{k,l}^{prop}=32.45+20\text{log}_{10}f_{c}+20\text{log}_{10}d_{k,l}. \tag{4}\]  
The shadow fading loss \(L_{k,l}^{sh}\) is modeled as a log-normal random variable, i.e., \(L_{k,l}^{sh}\sim\mathcal{N}\big{(}0,\sigma_{sh}^{2}\big{)}\), where \(\sigma_{sh}^{2}\) denotes shadowing variance. \(L_{k,l}^{ant}\) is modeled as [24]  
\[L_{k,l}^{ant}=\frac{1}{4}\bigg{|}\frac{2\pi\eta\sin\omega_{k,l}}{J_{1}(2\pi\eta \sin\omega_{k,l})}\bigg{|}^{2}, \tag{5}\]  
where \(J_{1}(\bullet)\) is the first-kind and first-order Bessel function, \(\omega_{k,l}\) denotes the bore-sight angle of the \(l\)th satellite's antenna beam with respect to the \(k\)th GU, and \(\eta\) denotes the aperture radius of the antenna in wavelengths. Since the heights of scatterers near the GUs are usually much lower than those of the satellites, it can be assumed that the propagation paths of all the antennas have the same angle for both the \(k\)th GU and the \(l\)th satellite, and each antenna of the satellites can be denoted by \(\mathbf{v}_{k,l}=\mathbf{v}_{k,l}^{x}\otimes\mathbf{v}_{k,l}^{y}=\mathbf{v}_ {x}\Big{(}\bar{\mathbf{\sigma}}_{k,l}^{x}\Big{)}\otimes\mathbf{v}_{y}\Big{(} \bar{\mathbf{\sigma}}_{k,l}^{y}\Big{)}\in\mathbb{C}^{N\times 1}\), where \(\bar{\mathbf{\sigma}}_{k,l}^{x}\) and \(\bar{\mathbf{\sigma}}_{k,l}^{y}\) are the spatial angles obtained by the transformation of the angle of divergence (AoD) \(\bar{\mathbf{\sigma}}_{k,l}^{y}\) and \(\theta_{k,l}^{y}\), \(\mathbf{v}_{k,l}^{x}=\sin\theta_{k,l}^{y}\otimes\theta_{k,l}^{x}\), \(\mathbf{v}_{k,l}^{y}=\cos\theta_{k,l}^{y}\). For \(d=\{x,y\}\), channel direction vectors can be expressed as \(\mathbf{v}_{k,l}^{\Delta}=\mathbf{v}_{d}\left(\bar{\mathbf{\sigma}}_{k,l}^{d }\right)=\frac{1}{\sqrt{Nd}}\left[1\exp\Bigl{\{}-j\pi\theta_{k,l}^{d}\Bigr{\}} \cdots\exp\Bigl{\{}-j\pi(N^{d}-1)\bar{\mathbf{\sigma}}_{k,l}^{d}\Bigr{\}} \right]\) for the \(k\)th GU and the \(l\)th satellite [25].
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 2 Related Work
Reference [20] integrates MEC and mmWave communications to enable a high-level network architecture with an application- and user-centric orchestration that collects various parameters using a liquid RAN C-plane such as user position, network load, and data popularity. The work in [21] provides an overview and strengths, weaknesses, opportunities, and threats (SWOT) analysis of the integration of mmWave and MEC from a business and economic aspects of 5G systems. However, the work is only limited to SWOT analysis, without provisioning caching schemes or a technical framework. In [22], a prefetching algorithm is proposed for mmWave communications in MEC. It develops mobility and traffic models and reduces system latency and enhances user data rates Finally, the work in [23] addresses computation offloading mechanisms that consider blockage effects in mmWave links in cloud-RAN. However, it lacks service request models, network function virtualization, and operating constraints of fog nodes. Overall, existing schemes on caching in F-RAN focus on content prediction to enhance the hit ratio, while considering limited user request specifications such as computation and delay bound. Further, the studies lack analysis of the resource constraints at the edge of the network without accounting for network cost in the caching process as compared to online prefetching.
## 2 Materials and Methods
### Features Generation in Frequency Domain  
In the frequency domain, the following features are generated: mean frequency, median frequency, band power, occupied bandwidth, power bandwidth, peak amplitude, and peak Location [23; 24; 25].  
The extraction of these features from the frequency domain is pivotal for a comprehensive analysis, offering nuanced insights into the tool's condition and operational efficiency.  
For the frequency domain, a total of seven distinctive features were generated in order to enhance the performed analysis. The definitions and formulas for these features are as follows [23; 24; 25; 26]:Mean Frequency: The average of all frequency components, weighted by their amplitudes. \[\text{Mean Frequency}=\frac{\sum f\cdot P(f)}{\sum P(f)}\] (12) where \(f\) represents the frequency components, and \(P(f)\) denotes the power spectral density at frequency \(f\).
2. Median Frequency: The frequency at which the power spectrum is divided into two equal halves. \[\int_{0}^{\text{Median Frequency}}P(f)\,df=\frac{1}{2}\int_{0}^{\infty}P(f)\,df\] (13)
3. Band Power: The sum of spectral power within a specified frequency band. \[\text{Band Power}=\int_{f_{\text{low}}}^{f_{\text{high}}}P(f)\,df\] (14) where \(f_{\text{low}}\) and \(f_{\text{high}}\) are the lower and upper bounds of the frequency band.
4. Occupied Bandwidth: The frequency bandwidth contains a specified percentage (e.g., 90%) of the total power. \[\int_{f_{\text{low}}}^{f_{\text{high}}}P(f)\,df=0.9\cdot\int_{0}^{\infty}P(f)\,df\] (15)
5. Power Bandwidth: The width of the frequency band within which a significant portion of the signal power is concentrated. \[\text{Power Bandwidth}=f_{\text{high}}-f_{\text{low}}\] (16) where \(f_{\text{low}}\) and \(f_{\text{high}}\) are defined such that they encompass a specified percentage of the total power.
6. Peak Amplitude: The maximum amplitude within the specified frequency band. \[\text{Peak Amplitude}=\text{max}(P(f))\] (17) where the maximum is taken over the specified frequency band.
7. Peak Location: The frequency at which the peak amplitude occurs. \[\text{Peak Location}=\text{argmax}(P(f))\] (18) where \(\text{argmax}\) returns the frequency at which \(P(f)\) is maximized.  
In this study, a total of 7 features were generated for each of the 11 signals, resulting in 77 features from the frequency domain.
## 5 Conclusions
\begin{tabular}{l l} \(x,y,z\) & coordinates \\ \(k\) & conductivity \\ \(\rho\) & density \\ \(c\) & specific heat \\ \(q\) & heat flux \\ \(\varepsilon_{s}\) & emissivity \\ \(\sigma_{s}\) & Stefan-Boltzmann constant \\ \(h\) & convective heat transfer coefficient \\ \(T_{T}\) & TFS temperature \\ \(T_{A}\) & environment temperature \\ \(E\) & tension modulus \\ \(G\) & shear modulus \\ \(\mu\) & [PERSON]'s ratio \\ \(\alpha\) & coefficient of thermal expansion \\ \(\varepsilon_{xx},\varepsilon_{zz},\gamma_{xz}\) & strains \\ \(\sigma_{xx},\sigma_{zz},\tau_{xz}\) & stresses \\ \(U_{x},U_{z}\) & displacements \\ \(q_{in}\) & heat transferred by conduction \\ \(\lambda\) & equivalent thermal conductivity \\ \(Q\) & heat transfer rate \\ \(T_{out}\) & outside face sheet temperature \\ \(T_{in}\) & inside face sheet temperature \\ \(W\) & strain energy \\ \(\varepsilon\) & total strain \\ \(\varepsilon_{\mathbf{th}}\) & strain produced by thermal expansion \\ \(\mathbf{D}\) & stiffness matrix of the material \\ \(p\) & penalty factor \\ \(E_{0}\) & initial elastic modulus of the material \\ \(k_{0}\) & initial thermal conductivity of the material \\ \(\rho_{0}\) & initial density of the material \\ \(E_{SIMP},k_{SIMP},\rho_{SIMP}\) & penalized material properties \\ \(W_{\mathrm{int}}\) & strain energy of the reference design \\ \(Q_{\mathrm{ini}}\) & heat transfer rate of the reference design \\ \(N\) & weight factor \\ \(d_{T,\mathrm{max,ini}}\) & maximum deflection of TFS in the reference design \\ \(d_{T,\mathrm{min,ini}}\) & minimum deflection of TFS in the reference design \\ \(d_{T,\mathrm{max}}\) & maximum deflection of TFS \\ \(d_{T,\mathrm{min}}\). & minimum deflection of TFS \\ \(V_{w}\) & volume fraction of the webs \\ \(V_{w,\mathrm{ini}}\) & volume fraction of the webs in the reference design \\ \(\rho_{\mathrm{min}}\) & minimum density \\ \(t_{w1},t_{w2},t_{w3}\) & upper-, mid- and lower-web thickness \\ \(L_{cr1},L_{\alpha 2},L_{cr3}\) & critical load factors calculated at Times 1, 2 and 3 \\ \(f_{cri}\) & approximated critical load factors \\ \(m_{0}\) & mass of the reference design \\ \(t_{\mathrm{min}},t_{\mathrm{max}}\) & upper and lower bounds of the design variables \\ \(T_{t,\mathrm{max}}\) & maximum temperature of the TFS \\ \(T_{b,\mathrm{max}}\) & maximum temperature of the BFS \\ \(T_{w,\mathrm{max}}\) & maximum temperature of the mid-point of the webs \\ \(S_{w,\mathrm{max}}\) & maximum stress of the webs \\ \end{tabular}
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
###### Abstract  
The depletion of conventional light petroleum reserves has intensified the search for alternative sources, notably, low-quality heavy oils and byproducts from heavy crude processing, to meet the global demand for fuels, energy, and petrochemicals. Heavy crude oil (HO) and extra heavy crude oil (EHO) represent nearly 70% of the world's reserves but require extensive upgrading to satisfy refining and petrochemical specifications. Their high asphaltene content results in elevated viscosity and reduced API gravity, posing significant challenges in extraction, transportation, and refining. Advanced catalytic approaches are crucial for efficient asphaltene removal and the conversion of heavy feedstocks into valuable light fractions. Kaolin, an aluminosilicate mineral, has emerged as a key precursor for zeolite synthesis and a promising catalyst in upgrading processes. This article provides a comprehensive exploration of kaolin's geological origins, chemical properties, and structural characteristics, as well as the various modification techniques designed to improve its catalytic performance. Special focus is given to its application in the transformation of heavy crudes, particularly in facilitating asphaltene breakdown and enhancing light distillate yields. Finally, future research avenues and potential developments in kaolin-based catalysis are discussed, emphasizing its vital role in addressing the technological challenges linked to the growing reliance on heavier crude resources.  
kaolin; synthesis methodologies; modification strategies; industrial applications; catalysis; petrochemical industry +
Footnote †: journal: _Sci._  
_Applied_  
_sciences_