esa-sceva/satcom-chunk-collection huggingface dataset:
# Production of Cellulosic Ethanol from Enzymatically Hydrolysed Wheat Straws
###### Abstract  
The aim of this study is to find the optimal pretreatment conditions and hydrolysis in order to obtain a high yield of bioethanol from wheat straw. The pretreatments were performed with different concentrations of sulphuric acid 1, 2 and 3% (_v_/_v_), and were followed by an enzymatic hydrolysis that was performed by varying the solid-to-liquid ratio (1/20, 1/25 and 1/30 g/mL) and the enzyme dose (30/30 \(\upmu\)L/g, 60/60 \(\upmu\)L/g and 90/90 \(\upmu\)L/g Viscozyme(r) L/Celluclast(r) 1.5 L). This mix of enzymes was used for the first time in the hydrolysis process of wheat straws which was previously pretreated with dilute sulfuric acid. Scanning electron microscopy indicated significant differences in the structural composition of the samples because of the pretreatment with H\({}_{2}\)SO\({}_{4}\) at different concentrations, and ATR-FTIR analysis highlighted the changes in the chemical composition in the pretreated wheat straw as compared to the untreated one. HPLC-RID was used to identify and quantify the carbohydrates content resulted from enzymatic hydrolysis to evaluate the potential of using wheat straws as a raw material for production of cellulosic ethanol in Romania. The highest degradation of lignocellulosic material was obtained in the case of pretreatment with 3% H\({}_{2}\)SO\({}_{4}\) (_v_/_v_), a solid-to-liquid ratio of 1/30 and an enzyme dose of 90/90 \(\upmu\)L/g. Simultaneous saccharification and fermentation were performed using _Saccharomyces cerevisiae_ yeast, and for monitoring the fermentation process a BlueSens equipment was used provided with ethanol, O\({}_{2}\) and CO\({}_{2}\) cap sensors mounted on the fermentation flasks. The highest concentration of bioethanol was obtained after 48 h of fermentation and it reached 1.20% (_v_/_v_).  
Keywords:wheat straws; pretreatment; hydrolysis; fermentation; bioethanol +
Footnote †: journal: Applied Physics Letters
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 4 Relation Tree
### Maintenance of the Relation Tree
#### 4.1.2 Tree Deletion  
At the end of each cycle, all nodes of the Relation Tree whose number of encounters is below M with depth 2 and their children are deleted. If M = 2, node D and all its child nodes that are less than 2 are deleted at the end of the cycle, as shown in Figure 2.
# Applying Selective Laser Melting to Join Al and Fe: An Investigation of Dissimilar Materials
## 3 Results and Discussion
### Join Quality in Term of Energy  
Relationship between supplied energy and join quality was investigated. The energy, \(E\), is described by laser power, \(P\), and volume of molten material, \(V\), as \(E=P/V\). Other studies [28, 29] calculated energy as follows:  
\[E_{1}=\frac{P}{v.h.t} \tag{1}\]  
Here, \(v,h\), and \(t\) are laser scanning speed, hatch spacing, and layer thickness, respectively. However, Equation (1) can present a rough estimation for comparable process parameters [30]. Meanwhile, by assuming that the melt tracks are semi-circular, energy is calculated as follows [31]:  
\[E_{2}=\frac{P}{v.\frac{1}{2}\pi\Big{(}t^{2}+\frac{h^{2}}{4}\Big{)}} \tag{2}\]  
Figure 10a,b illustrates a comparison between experimental observation and energy calculated by \(E_{1}\), \(E_{2}\) respectively. The orange color represents a flaked area, whereas the green color represents a non-flaked area. In this case, laser power was kept at 180 W and layer thickness was fixed at 20 \(\upmu\)m. The results indicate that it is easier to obtain a good joining when laser velocity and hatch distance are increased, as mentioned in Figure 9. This can be explained by a decrease in the reaction layer when laser scanning speed and hatch space are increased, or energy is reduced. The diagonal stripes in Figure 10 indicate energy calculated by the formulas. The diagonal striped areas cover not only the non-flaked area, but also broadly cover the flaked space. The more covering of the flaked area, the less the accuracy of the formula. Figure 10a,b indicate that the energy formulas \(E_{1}\)\(E_{2}\) cannot be used to describe the flake behavior and become less accurate as the flaked area increases.  
Moreover, in the SLM process, hatch overlap (\(d/h\)) was investigated as an important parameter [32] (here, \(d\) is laser spot size). Figure 11 illustrated the effect of the hatch overlap. The current line track nth of a laser generates a post-heating zone during printing, which reheats the solidified material of the last scan line n - 1 th, as well as a pre-heating zone, which affects the melting process of the next scanning line n + 1 th. Finally, hatch overlap affects melt geometry, heat-affected zone, formation of a solidification microstructure, and residual stress. In this analysis, the hatch overlap parameter was considered to affect energy. Combined with the assumption that the melt track is semi-circular, the following formula was generated:  
\[E_{3}=\frac{P}{v.\frac{1}{2}\pi\Big{(}t^{2}+\frac{h^{2}}{4}\Big{)}\frac{d}{h}} \tag{3}\]  
Figure 10c illustrates the fit of the \(E_{3}\) formula calculation and experimental area. Although there is an existing small gap between the calculated area and experimental result, the \(E_{3}\) formula could be applied to determine the relationships between joining quality and process parameters in terms of energy.  
Figure 10: Comparison between experimental results and energy calculated by (**a**) \(E_{1}\), (**b**) \(E_{2}\), and (**c**) \(E_{3}\) formulas.
## 5 Results and Evaluation
### Discussion  
From the performance of the TEAR protocol is obvious, that the exchange of temperatures between direct neighbors makes this solution very scalable and the protocol performs well regardless of the network density. Another important outcome is that the temperature is calculated without knowledge of the actual data sink. That is, the forwarding is based on anycast paradigm and if there are several data sinks in the network, the actual destination (data sink) depends on the trade-off between the distance in hops, the density towards the sink and actual residual energy of each sensor node on the path towards the data sink. Proactive behavior of our protocol design is very suitable for applications with frequent data exchange and might be ineffective in the network where only few nodes generate data or the data are generated very rarely. Another limitation of our protocol is convergence time of DAG, which depends on the period of the update message. For example, when there is sudden disconnection of data sink due to mobility of the sensor node, the temperature values of the neighbors of that data sink will drop significantly. Similarly, the neighbors of those nodes will recalculate their temperatures when received new beacon. This process will continue until all nodes with temperatures affected by that data sink update their tables and recalculate new temperature values. The overall convergence time strongly depends on the update period. In such situation, the packets are forwarded based on the outdated path, which might lead to longer paths or even packet drops due to temporal local maximum. Therefore, it is very important to properly select the update period in order to prevent temporal packet drops due to outdated paths caused by longer DAG convergence time.
## 5 Simulation Results and Discussions
### Impact Factors  
The impact factor is defined as the ratio of the increased vertical deflection induced by moving trains to the static deflection of the bridge. Figure 9 shows the simulation results of the impact factor in the single line and double line loadings, respectively. The impact factor reaches the maximum values when the vehicle speed is 40 km/h in both the single line and double line loading cases due to resonance induced by the moving trains. Overall, a slightly greater impact factor is achieved in the double line loading case. The largest impact factor is 4.2% in the double line case and 2.7% in the single line case, both achieved by the arch rib. The reason could be that the stiffness of the arch is larger than that of the beam due to the \"arch effect\" [41; 42] and that the structure with the bigger stiffness will bear the greater impact force. The results from the two loading cases follow a similar trend and resonance occurs at the same speed in the two loading cases, indicating that whether single line or double line, the loading applied does not significantly affect the impact factor of the bridge.  
Figure 8: The displacements of the bridge at the mid-span: (**a**) the vertical direction; (**b**) the transverse direction.  
Figure 9: The impact factors of the bridge at the mid-span: (**a**) single line; (**b**) double line.
## 2 Materials and Methods
### Transfer Factor Calculations  
Transfer factor (TF) was calculated according to the following equation [26; 27; 28]:  
\[\text{TF}=\text{Cplant/Csoil} \tag{1}\]  
where Cplant is the element concentration in the plant tissue (mg kg\({}^{-1}\)) and Csoil is the element concentration in the soil (mg kg\({}^{-1}\)). The results are categorized based on their TF into low (less than 0.09), medium (0.10-0.99), and high (>1.0) levels. Besides, TF > 1 indicates enrichment of the element in the plant [27].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Elements** & **SRM** & **Certified Values (mg kg\({}^{-1}\)) \(\pm\) SD** & **Measured Concentration (mg kg\({}^{-1}\)) \(\pm\) SD** \\ \hline Cr & NIST 1573a & 1.99 \(\pm\) 0.06 & 1.44 \(\pm\) 0.13 \\ \hline Mn & NIST 1573a & 246 \(\pm\) 8 & 199 \(\pm\) 12 \\  & BCR 281 & 81.6 \(\pm\) 2.6 & 62.6 \(\pm\) 0.1 \\ \hline Ni & NIST 1573a & 1.59 \(\pm\) 0.07 & 1.74 \(\pm\) 0.12 \\  & BCR 281 & 3.00 \(\pm\) 0.17 & 2.25 \(\pm\) 0.003 \\ \hline Cu & NIST 1573a & 4.70 \(\pm\) 0.14 & 4.03 \(\pm\) 0.25 \\  & BCR 281 & 9.65 \(\pm\) 0.68 & 9.17 \(\pm\) 0.02 \\ \hline Zn & NIST 1573a & 30.9 \(\pm\) 0.7 & 35.5 \(\pm\) 3.08 \\ \hline As & NIST 1573a & 0.112 \(\pm\) 0.004 & 0.24 \(\pm\) 0.03 \\  & BCR 281 & 0.057 \(\pm\) 0.004 & 0.095 \(\pm\) 0.00 \\ \hline Cd & NIST 1573a & 1.52 \(\pm\) 0.04 & 1.77 \(\pm\) 0.20 \\  & BCR 281 & 0.120 \(\pm\) 0.003 & 0.165 \(\pm\) 0.000 \\ \hline Pb & BCR 281 & 2.38 \(\pm\) 0.11 & 2.71 \(\pm\) 0.00 \\ \hline Sb & NIST 1573a & 0.063 \(\pm\) 0.006 & 0.088 \(\pm\) 0.008 \\  & BCR 281 & 0.047 \(\pm\) 0.005 & 0.062 \(\pm\) 0.000 \\ \hline V & NIST 1573a & 0.835 \(\pm\) 0.01 & 0.879 \(\pm\) 0.05 \\ \hline Co & NIST 1573a & 0.57 \(\pm\) 0.02 & 0.38 \(\pm\) 0.10 \\ \hline \end{tabular}
\end{table}
Table 1: Results summary for the standard reference materials National Institute of Standards and Technology (NIST) 1573a (tomato leaves) and Community Bureau of Reference (BCR) 281 (rye grass).
## 2 Case Studies
### Basilica of Santa Maria di Collemaggio
As a consequence of the 1997 Umbria and Marche earthquake, further interventions were carried out to improve the seismic performance of the church: e.g., the reinforcement of the longitudinal walls. Furthermore, a new horizontal dissipation bracing system, composed of steel hysteretic dampers, was realised beneath a wooden roof [24; 25]. The junctions between the buttress, the facade, and the longitudinal walls were enhanced. The masonry walls were reinforced through the implementation of drilling reinforced with steel bars and grout injections. On 6 April 2009, the church was struck by the famous L'Aquila earthquake, which caused serious damage: the collapse of the transport (highlighted by the red dashed outline in Figure 5) involved the failure of the two main pillars, the triumphal arch, the dome, the barrel vaults, and part of the wooden roof structure. The reconstruction interventions were concluded in 2017 and involved the complete reconstruction of the transport area and the repair of the damage suffered by the columns [26; 27; 28]. It is important to note that the church configuration considered in this research is the one prior to the partial collapse that occurred during the 2009 L'Aquila earthquake. Consequently, the above-mentioned retrofitting interventions carried out after 2009 were not taken into account in the following structural analyses. Figure 5: Basilica of Santa Maria di Collemaggio. (**a**) Plan; and (**b**) longitudinal section (before the partial collapse that occurred during the 2009 L’Aquila earthquake). The red dashed outline indicates the transport area that collapsed during the 2009 L’Aquila earthquake.
## 3 Results and Discussion  
### Model Validation  
#### 3.1.1 Gasification  
The flowsheet result of gasification in Aspen Plus is presented in Appendix B. The simulation model is validated by using experimental data from gasification of pyrolysis oil published by [PERSON] et al. [21]. The experimental data and simulation results are shown in Table 5. In this research, pyrolysis oil is used to produce syngas through gasification in a fluidized bed with nickel-based catalysts at 800 \({}^{\circ}\)C. It may be observed that the model syngas concentration is in good agreement with the experimental results except the concentration of CO and CH\({}_{4}\). In the experiments, the steam methane reforming reaction is restricted by the reaction time so it cannot reach a state of complete equilibrium [29]. Therefore, the concentration of CH\({}_{4}\) simulated by RGibbs reactor without the time limitation is very low and this part of CH\({}_{4}\) reacts with steam into CO and H\({}_{2}\) (R-8). Because the nitrogen and sulfur content in pyrolysis oil is relatively low, the content of H\({}_{2}\)S and NH\({}_{3}\) in the produced syngas is low.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Syngas Composition (Mole \%)** & **Experiment** & **Model** \\ \hline \(\mathrm{H_{2}}\) & 55.5 & 55.9 \\ CO & 19.3 & 24.0 \\ CO\({}_{2}\) & 19.0 & 19.8 \\ CH\({}_{4}\) & 5.4 & 0.3 \\ C\({}_{2}\)H\({}_{6}\) & 1.0 & trace \\ H\({}_{2}\)S & - & trace \\ NH\({}_{3}\) & - & trace \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between experimental and simulated results of gasification process.
## 1 Introduction
By combining elements of both MTS and MTO strategies, hybrid systems seek to strike a balance between efficiency and responsiveness to demand fluctuations, thereby improving overall production efficiency. [PERSON] and [PERSON] [5] identified three primary types of hybrid production systems: sequential, float, and parallel systems. In sequential systems, MTS and MTO operations are integrated in a linear fashion. This means that part of the production process operates under MTS principles to minimize lead time and efficiently meet customer demands, while the remaining part of the process switches to MTO to accommodate variations in customer order volume [6]. The transition point from MTS to MTO, known as the customer order decoupling point (CODP), marks the shift in production strategy. In floating systems, the CODP is allowed to move dynamically. This means that the transition between MTS and MTO is not fixed at a specific point in the production process but can adjust based on various factors such as demand patterns, production capacity, or inventory levels. This flexibility enables the system to adapt more effectively to changing market conditions and customer requirements, enhancing overall production efficiency. This study proposes an approach to dynamically adjust the decoupling point based on the observation of order tardiness and earliness in deliveries to customers. When earliness increases, the decoupling point shifts towards the initial production stages; conversely, when tardiness increases, the decoupling point moves towards the final production stages. The proposed approach is tested using two control methods: the first based on periodic review and the second on continuous review. The performance of the proposed model is compared to traditional MTO and MTS systems using various performance measures. The remainder of this paper is organized as follows. Section 2 presents a literature review summarizing the contributions regarding hybrid control methods of MTS-MTO systems. Section 3 describes the reference context studied, while Section 4 explains the proposed dynamic decoupling model proposed. Section 5 describes the simulation models developed to test the proposed model, and Section 6 discusses the numerical results. Finally, Section 7 presents the conclusions of this study and recommends future research directions.
## 3 Objective Function for Parameters Tuning  
The objective function to compute the gains of the controllers can be designed in different ways. In previous works, the objective function is constructed by one or more performance indices, such as: integral squared error (JISE), integral absolute error (JIAE), integral time and squared error (JITSE), mean squared error (MSE) and integral of time and absolute error (JITAE). Nevertheless, some researchers have been used different fitness functions based on time domain specifications response, such as: settling time (\(t_{s}\)), rise time (\(t_{r}\)), maximum overshoot (\(M_{p}\)) and steady state error (\(e_{ss}\)). In this paper, to design the objective function, both criteria of transient response and performance indices are used in order to minimize all the indices as mentioned earlier. [14,38,39]. Thus, the respective objective function is defined as [40],  
\[\min J=(1-exp^{-\gamma})(M_{p}+e_{ss})+exp^{-\gamma}(t_{s}-t_{r}) \tag{9}\]  
subject to the following constraints:  
\[\begin{split} k_{p,j}^{\min}&\leq k_{p}\leq k_{p,j} ^{\max}\\ k_{i,j}^{\min}&\leq k_{i}\leq k_{i,j}^{\max}\end{split} \tag{10}\]  
where \(j=[i_{q},i_{d},\omega_{r}]\); \(k_{p,j}^{\min}\), \(k_{i,j}^{\min}\), \(k_{p,j}^{\max}\) and \(k_{i,j}^{\max}\) are the maximum and minimum values of the gains of the PI controllers, and \(\gamma\) is the weighting factor which could be changed according to the dynamics of the system, in this work \(\gamma=0.5\). The search space for proportional gains (\(k_{p,j}\)) are defined between 0.1 and 100; and that of the integral gains (\(k_{i,j}\)) are between 0.1 and 250.
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
## 1 Introduction  
Biomedical image analysis has gained more importance and is attracting increasing attention from researchers. With the development of image analysis techniques, biomedical image analysis facilitates feature extraction in retinal images and the diagnosis of such retinal diseases as diabetic retinopathy, papilledema, and glaucoma.  
The optic disk (OD) is a bright area on the retina where blood vessels pass through the OD and which does not carry any light sensors. The diameter of the OD is approximately 80-100 pixels in a retinal image with an average resolution [1], which is an important indicator for the detection of the fovea and other retinal anatomic structures [2; 3].  
The detection of retinal blood vessels provides preliminary knowledge for the classification and grading of glaucoma and diabetic retinopathy. Other fundus features, such as the fixed distance between the center of the macula and OD, are also used as an indicator for estimating the region of the macula [4; 5]. The OD is used as a starting point for retinal vessel tracking methods as well [4].  
A perusal of the literature shows that the starting point for an operation is always the OD, whether to detect blood vessels from retinal images, to detect the fovea and other anatomical structures, or to diagnose a retinal disease. Therefore, localization of the OD is the most basic and preliminary step in the automatic analysis of retinal images and in the detection of retinal diseases [6].  
Anatomical structures on the retina--the optic disk, blood vessels, and fovea/macula regions--are shown in Figure 1[7].
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 3 Paint Industry Wastewater Treatment Methods
### Physicochemical treatment
#### 3.1.2 Adsorption  
adsorption is also a frequently applied method for treating paint industry effluents. Heavy metals are removed from the wastewater by many different adsorbents, both chemical and natural [44; 45; 53; 55]. In recent years, alternative adsorbents are produced, which have the ability to bind and remove heavy metals from the effluent. Low-cost materials have been tested for this purpose, such as natural zeolite, ash, rice husk, vermicompost, peat, volcanic stones, bentonite, and clinoptilolite. From these natural adsorbents, wood ash has been applied for the paint industrial treatment process. Heavy metal removal (Co and Pb) by wood ash as an adsorbent has been studied [55] and found to be easily obtainable and inexpensive. Heavy metals were reported to be effectively removed from paint industry effluents using wood ash as an adsorbent. The treatment process is influenced by the pH and the adsorbent quantity. The maximum removal efficiency was 96.1% and 99% for lead and cobalt, respectively.  
Less satisfactory removal efficiency was obtained using light-expanded clay aggregate (LECA) as an adsorbent for the removal of Cd and Pb from paint industry effluents under various conditions, such as the amount of adsorbent, contact time, and pH. The removal efficiency of clay adsorbent was reported to be 93.75% for Pb and nearly 89.7% for Cd [44].  
adsorption is a proper method for the elimination of heavy metals from the wastewater effluents, meaning that for the removal of organic pollutants, dyes, and other toxic compounds the combination of other treatment processes is needed.