esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Tomato and Pepper Processing Waste on Bread Quality
## 3 Results and Discussion
### Farinograph Properties of Dough
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Sample** & **WA [\%]** & **DDT [min]** & **ST [min]** & **DS [FU]** & **FQN** \\ \hline CON & 57.7 \(\pm\) 0.5 \({}^{\text{f}}\) & 2.3 \(\pm\) 0.3 \({}^{\text{c}}\) & 12.7 \(\pm\) 0.3 \({}^{\text{a}}\) & 28.3 \(\pm\) 2.1 \({}^{\text{d}}\) & 108.3 \(\pm\) 37.2 \({}^{\text{a}}\) \\ \hline TP3 & 59.3 \(\pm\) 0.5 \({}^{\text{ed}}\) & 2.7 \(\pm\) 1.1 \({}^{\text{c}}\) & 6.1 \(\pm\) 0.3 \({}^{\text{cb}}\) & 79.7 \(\pm\) 15.6 \({}^{\text{c}}\) & 71.0 \(\pm\) 3.0 \({}^{\text{ba}}\) \\ TP6 & 60.0 \(\pm\) 0.5 \({}^{\text{dc}}\) & 3.3 \(\pm\) 1.8 \({}^{\text{bc}}\) & 6.7 \(\pm\) 2.1 \({}^{\text{b}}\) & 112.7 \(\pm\) 14.8 \({}^{\text{b}}\) & 69.0 \(\pm\) 10.6 \({}^{\text{b}}\) \\ TP9 & 61.2 \(\pm\) 0.4 \({}^{\text{cb}}\) & 4.0 \(\pm\) 0.4 \({}^{\text{b}}\) & 5.3 \(\pm\) 0.3 \({}^{\text{cb}}\) & 140.7 \(\pm\) 6.4 \({}^{\text{b}}\) & 57.3 \(\pm\) 3.2 \({}^{\text{b}}\) \\ TP12 & 61.6 \(\pm\) 0.2 \({}^{\text{b}}\) & 3.5 \(\pm\) 0.4 \({}^{\text{b}}\) & 4.3 \(\pm\) 0.3 \({}^{\text{c}}\) & 133.3 \(\pm\) 8.7 \({}^{\text{b}}\) & 54.0 \(\pm\) 5.2 \({}^{\text{b}}\) \\ \hline PB3 & 59.0 \(\pm\) 0.1 \({}^{\text{fed}}\) & 6.6 \(\pm\) 0.3 \({}^{\text{a}}\) & 6.3 \(\pm\) 0.7 \({}^{\text{cb}}\) & 210.3 \(\pm\) 9.3 \({}^{\text{a}}\) & 83.3 \(\pm\) 1.2 \({}^{\text{ba}}\) \\ PB6 & 62.1 \(\pm\) 0.6 \({}^{\text{abc}}\) & 5.2 \(\pm\) 0.3 \({}^{\text{ba}}\) & 4.9 \(\pm\) 0.2 \({}^{\text{cb}}\) & 236.7 \(\pm\) 11.0 \({}^{\text{a}}\) & 70.0 \(\pm\) 3.0 \({}^{\text{b}}\) \\ PB9 & 57.7 \(\pm\) 0.1 \({}^{\text{f}}\) & 5.3 \(\pm\) 0.2 \({}^{\text{ba}}\) & 4.6 \(\pm\) 0.2 \({}^{\text{cb}}\) & 221.7 \(\pm\) 4.9 \({}^{\text{a}}\) & 72.0 \(\pm\) 1.0 \({}^{\text{ba}}\) \\ PB12 & 58.2 \(\pm\) 0.7 \({}^{\text{fe}}\) & 5.3 \(\pm\) 0.2 \({}^{\text{ba}}\) & 4.8 \(\pm\) 0.6 \({}^{\text{eb}}\) & 232.0 \(\pm\) 8.7 \({}^{\text{a}}\) & 66.3 \(\pm\) 2.5 \({}^{\text{b}}\) \\ \hline \end{tabular} CON—control sample (100% wheat flour); mean value (\(n=3\)) \(\pm\) SD; means in the same column with different letters (\({}^{\text{e-f}}\)) are significantly different (Tukey test, \(p\leq 0.05\)); WA—water adsorption; DDT—dough development time; ST—Stability time; DS—Dough softening; FQN—Fainrograph Quality Number.  
\end{table} 
Table 1: Farinograph analysis of wheat dough with progressive addition of tomato waste (TP) and pepper placenta (PB) from 3 to 12 g/100 g.
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 4 Fundamentals of Smart Polymeric Materials: Focus on Magneto-Active Elastomers and Hydrogels
### Magnetorheological Elastomers (MREs)
#### 4.2.2 Magnetorheological Elastomers in Soft Robotics  
One of the most widespread uses of MREs is in the field of soft robotics. This field is centred in the study of different mechanisms and materials for controlling and fabricating robots able to perform tasks inaccessible to traditional rigid robots. One of the main challenges of soft robotics involves robot tethering, which is the dependence of a robot on a base for the supply of a form of actuation, which may be electrical, hydraulic, etc. MREs pose as a promising candidate for soft robotics due to the nature of magnetic actuation, which is completely remote. For example, by controlling the magnetic alignment of different domains of 3D printed MEs, an external magnetic field was used to induce specific deformation onto these bodies to achieve complex remote manipulation and repeatable movements [125]. Other soft robots of millimetre-size were able to rotate, swim and roll by varying the intensity and direction of the applied magnetic field. In biomedical applications, a long soft robot with a MRE \"head\" was remotely guided through an intricate path using EMFs [126]. In the case of soft robotics, hard magnetic particles are mostly used, due to the way magnetic forces are translated into the MRE. In this case, since locomotion is the main goal, a higher magnetostriction effect is achieved by using hard particles with a certain orientation. These particles, under the influence of a magnetic field, tend to align with it achieving fast and large deformations.
# Fatigue of Friction Stir Welded Aluminum Alloy Joints: A Review
## 7 Conclusions  
This paper provided a comprehensive review of fatigue of FSW aluminum alloy joints. From the literature reviewed herein, the following observations and conclusions can be drawn.  
* The fatigue crack initiation generally started at the surface of the weld, due to the fact that FSW welds with optimized process parameters do not contain internal defects or flaws. In addition, the initiation site was mainly located between the TMAZ and the HAZ as a result of both high temperature and plastic deformation. The difference in hardness between the TMAZ and HAZ resulted in a weak zone, which is vulnerable to the formation of local slip bands.
* The fatigue performance of FSW joints is mainly affected by process parameters, stress ratio, environment, residual stress, defects, and so on. The process parameters can be optimized to increase the weld fatigue life. Residual stress has a large influence on the crack growth rate, and it is difficult to remove when the welds are complex. The effect of defects on the fatigue properties of materials is complicated and depends on the type of defects.
* Laser peening is recommended for the post weld treatment of friction stir welded joints. Multilayer laser peening can greatly decrease fatigue crack growth rate and improve material fatigue life. At ambient and elevated temperature, shot peening treatment has similar fatigue crack growth resistance as as-welded condition and crack growth rates were higher than the laser peened case.
* The fatigue life data in the literature are still limited. In the high cycle stress life analysis, more testing are required for different materials at various stress amplitude and mean stress combinations. For low cycle fatigue analysis where the plastic part dominates, considerable works such as dissimilar material joint assessment are needed in the future.  
Conceptualization, H.L. and Q.L.; methodology, H.L.; investigation, H.L. and J.G.; writing--original draft preparation, J.G.; writing--review and editing, H.E., J.G., and Q.L.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L.  
This research was supported by the National Natural Science Foundation of China under Grant No. 51605444 and No.51475431, Zhejiang Province Science and Technology Plan Project Grant No. 2017C34007, And The APC was funded by Zhejiang Sci-Tech University.
## 1 Introduction
In the studies on the direct synthesis of DME incorporating CO\({}_{2}\) in the feed together with syngas, the attention has been focused on the effect of the reaction conditions and the composition of the catalyst on the yield and selectivity to DME (economic objective) [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], but without studying in detail the effect on the conversion of CO\({}_{2}\) (environmental objective) and without analyzing the relation between both targets. As to the complex reaction scheme concerns (Equations (1)-(5)), it is observed that CO\({}_{2}\) is both a reactant (Equation (2)), and a product of the WGS reaction (Equation (3)); and therefore, CO\({}_{2}\) conversion does not have a direct relationship with the yield of DME, it depends on the reaction conditions, which must be determined experimentally. The main objective of this work is to determine the appropriate conditions (temperature, pressure, space time, H\({}_{2}\)/CO\({}_{x}\) and CO\({}_{2}\)/CO molar ratios in the feed) to avoid the emission of CO\({}_{2}\) and attain its effective conversion, as well as achieving a good compromise between DME production and CO\({}_{2}\) conversion. The study has been conducted using a CuO-ZnO-MnO/SAPO-18 bifunctional catalyst, selected based on its good kinetic performance (activity, selectivity, and stability) in the direct synthesis of DME co-feeding CO\({}_{2}\)[34].
## 2 Materials and Methods  
### Study Area  
This study took place within the Maltese archipelago, which consists of three primary islands, Malta, Gozo, and Comino, spanning a combined area of 316 km\({}^{2}\) (Figure 1). Situated in the Mediterranean Sea within the Strait of Sicily, between Sicily and Tunisia, the archipelago boasts approximately 190 km of coastline. Its terrain is predominantly characterised by cliffs, clay slopes, and boulder formations, contributing to a diverse array of underwater topographic and ecosystem features. The seabed encompassing the Maltese Islands comprises a broad, level continental shelf situated predominantly on the eastern sides of Malta and Gozo, bordered by a continuous escarpment that parallels the coastline [26]. Conversely, on the western periphery of the islands, the shelf narrows considerably, stretching only a few hundred meters from the coast and featuring steep cliffs reaching heights of approximately 100 m [27]. Consequently, a prevailing pattern emerges wherein the waters to the west of these islands exhibit greater depth compared to those on the eastern side.  
Located at the heart of the Mediterranean Sea, the Maltese Islands are influenced by various oceanic processes, including the thermohaline circulation, which is a distinctive water movement pattern affecting both temperature and salinity [28]. Furthermore, the Mediterranean undergoes considerable seasonal temperature fluctuations that impact ocean dynamics through variations in evaporation and precipitation rates, subsequently altering water density and circulation patterns due to changes in salinity levels [29]. Consequently, these dynamics render the Maltese archipelago an ideal location for evaluating spatial and temporal variations in SSS and SST.
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 2 System Model
### Channel Model
Moreover, \(\widetilde{h}_{k}^{los}=[a(\phi_{k},\psi_{k})_{1},\cdots,a(\phi_{k},\psi_{k}) _{N}]^{\mathrm{T}}\) represents the LoS component vector, and the \(n\)-th element \(a(\phi_{k},\psi_{k})_{n}\) can be given by:  
\[a(\phi_{k},\psi_{k})_{n}=e^{j\frac{2 nd}{\lambda}\left\{s_{n}\sin(\phi_{k}) \cos(\psi_{k})+i_{n}\sin(\psi_{k})\right\}} \tag{8}\]  
where \(s_{n}=\mathrm{mod}(n-1,N_{t})\) and \(i_{n}=\lfloor(n-1)/N_{t}\rfloor\). \(\phi_{k}\) and \(\psi_{k}\) are the azimuth and elevation angles of arrival (AoA) from the IRS to user \(k\). \(d\) is the interelement spacing of IRS, and we assume \(d/\lambda=2\).
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 5 Optimization Techniques
### Automated Parameters Optimization  
As we know, the XGBoost model has more than 10 parameters, including general parameters, tree parameters, learning task parameters, etc. The optimization of the parameters is a complex problem that requires the use of AutoML technology to improve the efficiency of model training and optimization.  
In our method, Optuna ([[https://optuna.org/](https://optuna.org/)]([https://optuna.org/](https://optuna.org/)), (accessed on 10 November 2024)) [16] is used as an instrument to optimize model parameters, which is an advanced automatic hyperparameter tuning framework. Its core idea is to efficiently explore the hyperparameter space through an intelligent sampling algorithm and the accumulation of historical information, so as to find the best hyperparameter configuration.  
Specifically, Optuna models the optimization problem as an objective function, which is the metric we need to optimize. In this paper, we set the objective function as the weighted F1 score that assigns different weights based on the proportion of each category of the model. Optuna defines a search space that contains hyperparameters that can be tuned and their range of values, which constitute the decision variables of the optimization problem. Optuna uses the idea of Bayesian optimization to guide the subsequent sampling direction by building a probabilistic model of the objective function to find the optimal solution faster. Common sampling strategies include Tree-Structured Parzen Estimator (TPE) [17], Cost-Function-Based Policy Optimization (CFPO) [18], etc. In this paper, the Categorical sampling strategy is used in the TPE method. The Categorical sampling strategy allows for setting a selectable list of parameters, and Optuna will sample from these options based on the specified sampling method. Optuna also provides support for parallelization, with multiple hyperparameter combinations enabling simultaneous evaluation and enhancing optimization efficiency. Moreover, Optuna is capable of acquiring intermediate results during the training process, including loss values for each epoch, and making adjustments to the subsequent search strategy accordingly. In general, Optuna achieves automatic and efficient optimization of hyperparameters through the construction of an objective function, definition of the search space, adoption of an efficient sampling strategy, and utilization of intermediate results for timely adjustments. The results of automatic parameter tuning are shown in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Candidate Features** \\ \hline
0 & (LONPC*LATPC) \\
1 & max(LONPC,LATPC) \\
2 & (LONPC+LATPC) \\
3 & residual(LATPC) \\
4 & log(ALT,STD) \\
5 & freq(RUDD) \\
6 & max(TAT,ALT\_STD) \\
7 & log(GSC) \\
8 & (FF1*GSC) \\
9 & freq(FF1) \\
10 & (N11*TLA1) \\... &... \\
25 & (N11/VB11) \\... &... \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results information for candidate features.
# Trajectory Tracking with Adaptive Robust Control for Quadrotor
## 4 Adaptive Controller and Robust Mechanisms Design  
To achieve the desired trajectory by the quadrotor, we have reduced the mathematical model; this can help demonstrate the effectiveness of the robust mechanisms to achieve the control objective despite the reduced mathematical model. The design of the adjustment mechanisms is with the gradient method used by the MIT rule with sliding mode control theory to give robustness to the adjustment mechanisms. The controller is a PD with adaptive gains, and the critical part of this adaptive law is the adjustment mechanism. Then, the control law is given by  
\[u_{\Theta}=\hat{k}_{p\Theta}^{al}(\bar{\Theta}_{c}(t)-\bar{\Theta}(t))-\hat{k }_{d\Theta}^{al}(\dot{\bar{\Theta}}_{c}(t)-\dot{\bar{\Theta}}(t)) \tag{13}\]  
with \(\bar{\Theta}_{c}(t)=[\theta_{c},\phi_{c},\psi_{c}]\) and \(\bar{\Theta}(t)=[\theta,\phi,\psi]\). Then, \(u_{\Theta}=u_{\theta},u_{\phi},u_{\psi}\) are the adaptive controllers defined to achieve the desired angles and in consequence attain the desired trajectory. The adaptive proportional gain is given as \(\hat{k}_{p\Theta}^{al}\) and the adaptive derivative gain is \(\hat{k}_{d\Theta}^{al}\). The acronym \"\(al\)\" is used to indicate the adjustment mechanism that schedules the controller gains (\(l\in{1,...,4}\)):  
* \(a1\): The MIT rule. * \(a2\): The MIT rule with first-order sliding mode (MIT-SM). * \(a3\): The MIT rule with twisting (MIT-Twisting). * \(a4\): The MIT rule with high order sliding mode (MIT-HOSM). The adaptive controller (13) is applied at a reduced model (pure motion Euler angles) obtained fromf Equations (3) and (4). Then, based on the MIT methodology presented in [28], the mathematical model that represents the unmanned aerial vehicle and the adaptive law is defined in a close-loop transfer function and is given by  
\[\bar{\Theta}(t)=\frac{(\hat{k}_{p\Theta}^{al}+\hat{k}_{d\Theta}^{al}s)C_{2}}{ s^{2}+(C_{2}\hat{k}_{d\Theta}^{al}-C_{1})s+C_{2}\hat{k}_{p\Theta}^{al}}\bar{ \Theta}_{c}(t) \tag{14}\]  
In order to avoid redundancy in the definition of (14), let us define, \(C_{2}=\frac{1}{l_{y}}\cdot l\cdot b\) when is applied the closed-loop transfer for the pitch angle \(C_{2}=\frac{1}{l_{x}}\cdot l\cdot b\) for the roll angle, and \(C_{2}=\frac{1}{l_{z}}\cdot d\) for the yaw angle. Furthermore, \(C_{1}\) is considered such an aerodynamic damping coefficient.
# A Low-Profile Dielectric Resonator Antenna Array for OAM Waves Generation at 5G NR Bands
###### Abstract  
This paper presents the generation of orbital angular momentum (OAM) vortex waves with mode +1 using dielectric resonator antenna (DRA) array. The proposed antenna was designed and fabricated using FR-4 substrate to generate OAM mode +1 at 3.56 GHz (5G new radio band). The proposed antenna consists of 2 \(\times\) 2 rectangular DRA array, a feeding network, and four cross slots etched on the ground plane. The proposed antenna succeeded in generating OAM waves; this was confirmed by the measured radiation pattern (2D polar form), simulated phase distribution, and intensity distribution. Moreover, mode purity analysis was carried out to verify the generation of OAM mode +1, and the purity obtained was 53.87%. The antenna operates from 3.2 to 3.66 GHz with a maximum gain of 7.3 dBi. Compared with previous designs, this proposed antenna is low-profile and easy to fabricate. In addition, the proposed antenna has a compact structure, wide bandwidth, high gain, and low losses, thus meeting the requirements of 5G NR applications.  
dielectric resonator antenna; orbital angular momentum; uniform circular array; 5G communications; 5G New Radio (NR) 2023314841. [[https://doi.org/10.3390/mi14040841](https://doi.org/10.3390/mi14040841)]([https://doi.org/10.3390/mi14040841](https://doi.org/10.3390/mi14040841))
###### Abstract  
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.  
machine learning; in process monitoring; online control; Laser powder bed fusion; sensors +
Footnote †: journal:  
_Applied_  
_sciences_
# Application of Near-Infrared Spectroscopy for Monitoring and/or Control of Composting Processes
## 3 Composing: General Information
Compost used in the agricultural sector must not have a dominant smell, the amount of nutrients must be known, and it must contain only trace amounts of impurities (homogeneous and disinfected). When compost is used as a growing medium, some physical properties that will affect water availability to plants (e.g., particle size, density, and water-holding capacity) become extremely important [30]. The composting process can be performed in different types of facilities: open systems, such as windows or piles (either aerated, static, or turned) and closed systems, such as reactors or composters [31] (Figure 3). Closed (in-vessel) systems have advantages over open systems: they require less space and provide better control of the process, and they involve high process efficiency [32; 33; 34]. For example, [PERSON] et al. [20] investigated different grape pomace composting treatments during 140 days: two of them compost piles by turning the pomace after some period, and one was a static pile. In their work, all treatments achieved the thermophilic phase (the temperature was above 45 \({}^{\circ}\)C) at the 2 nd composting day, and those conditions continued for almost 50 days through the composting. Aerationwas achieved by turning the piles, increasing oxygen levels, and improving microorganism activity; consequently, the temperature increased and composoting progressed faster. According to the rates of organic matter mineralization, the highest was in the static pile compared to the turned ones because the thermophile phase lasted longer without any temperature decrease [20]. Moreover, the composoting process of poultry manure and sawdust in closed cylindrical and rectangular reactors was performed by [PERSON] et al. [35]. They investigated the effect of different aeration rates on the performance of the composoting process. Their results showed that low aeration rates correspond to higher temperatures and a longer thermophilic phase. Furthermore, the literature reports many studies on vermicomposting of different types of organic waste [36; 37; 38]. Vermicomposting is defined as utilizing several earthworm species for composoting organic matter [1]. Fertilizer rich in nutrients can be created from organic solid waste, and this requires aerobic conditions and earthworms. One species of earthworm (_Eisenia fetida_) has the potential to convert organic waste into products of a high nutritional value, but the environmental conditions (such as pH, temperature, and humidity) must be suitable [39].
(3) Optimal algorithms: Generally, the traditional algorithms are adopted in most studies, such as the mix-integer linear programming method [3; 24], mixed-integer quadratic programming method [37], and interior point method [35]. However, the intelligent optimization algorithms with high global search ability and fast convergence speed are rarely used. (4) Constraints handling methods: The constraints handling methods affect the operation results directly. Few articles give full details about the constraints handling methods, especially for the complicated dynamic nodal balance constraint and volume limits of gas storage in the natural-gas system. On the above premises, this paper establishes the optimal operation model of the hybrid electricity-natural gas energy systems considering operation cost, natural-gas cost reduction due to P2G, CO\({}_{2}\) emissions, and SO\({}_{x}\) emissions to achieve low-carbon, economic, and environmental benefits. The multi-objective black-hole particle swarm optimization algorithm (MOBHPSO) [39; 40; 41; 42] is adopted. The power flow is calculated using the Newton-Raphson method. The non-linear gas flow equations are solved by the trust region method [43; 44] and Levenberg-Marquardt (L-M) method [45; 46], respectively. The gas demand management strategy is proposed to balance the gas flow. Moreover, the detailed handling methods of inequality constraints in natural-gas system are also given in this paper. Several case studies are carried out on a hybrid IEEE 39-bus power system and Belgian high-calorific 20-node gas system in a period of 24 h to investigate the low-carbon, economic and environmental benefits of P2G in terms of cost reduction ($6.165 \(\times\) 10\({}^{5}\)), rate decline of wind curtailment (from 24.85% to 4.04%), CO\({}_{2}\) emissions reduction (3630 tons), and SO\({}_{x}\) emissions reduction (0.254 ton).