esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption of Emerging Pollutant by Pecan Shell-Based Biosorbent
## 3 Results and Discussion
### Thermodynamics of Adsorption and Its Modeling  
The evaluation of the effects of temperature and thermodynamics provides information about the adsorption mechanism. The adsorption test was performed for different temperatures (10-50 \({}^{\circ}\)C). Figure 7A shows the results. Based on the graph, there is a noticeable improvement in the CPX removal process as the temperature rises; this is because at the mildest temperature tested (10 \({}^{\circ}\)C) the removal was approximately 26%, while for the high temperature (50 \({}^{\circ}\)C), the total removed content was almost 60%. In order to investigate the thermodynamic parameters of the process, the van't Hoff graph (Figure 7B) was plotted; through this graph the variation of Gibbs Free Energy (\(\Delta\)G), enthalpy (\(\Delta\)H), and entropy (\(\Delta\)S) was determined, as Table 4 shows.  
Figure 6: Linear plots of (**A**) Langmuir and (**B**) Freundlich isotherm models for the CPX biosorption by PSB.  
The system moves to a minimum of \(\Delta\)G, which denotes a spontaneity in the process and indicates that the adsorption of CPX by PS develops favorably mainly at higher temperatures. The favoring of this process with the increase in temperature may be due to the dilation of the pores of the adsorbent; this results in larger pores and consequently in an easier diffusion and adsorption of antibiotic molecules [22]. The positive value of \(\Delta\)H shows an endothermic reaction; this also indicates that adsorption is benefited by the increase in temperature. According to the literature [23], enthalpy values below 20 kJ mol\({}^{-1}\) denote a physical adsorption nature. On the other hand, for \(\Delta\)H values greater than 40 kJ mol\({}^{-1}\), the predominant interactions in the adsorption process are chemical. As the enthalpy range obtained in the present study remained at an intermediate value (25 kJ mol\({}^{-1}\)), there is a simultaneous presence of chemisorption and physisorption in the present process; this is the case even though the fitting to the Freundlich isotherm suggests that the process takes place mostly through chemical interactions. The negative value for \(\Delta\)S shows that there is a reduction in randomness on the solid-liquid interface as the adsorption process is carried out [21,23].
## 3 Matrix-Based Establishment of Mathematical Model
### Optimization Function  
According to practical engineering experience, it can be determined that walls possess more complex spatial topological relationships than other prefabricated components, which have the most significant impact on the overall assembly task duration. So this paper focuses on determining the assembly sequence of a group of wall components within a subprocess rather than considering all wall components in an engineering project.  
Optimizing the assembly sequence for prefabricated components fundamentally involves identifying the optimal order for a group of components. Based on the previous study [7; 25], this study utilizes a three-pronged approach to assess the quality of the assembly sequence in terms of weight, occupied space, and interference relationships. In order to address this multi-objective sequence optimization problem, the penalty function method is employed to establish the objective function. This is achieved by assigning relative weight values to each sub-problem and combining them linearly. And the final optimization function is represented by Equation (4).  
\[P_{total}=\kappa\sum P_{weight}+\lambda\sum P_{space}+\mu\sum P_{layout} \tag{4}\]  
where \(P_{weight}\), \(P_{space}\), and \(P_{layout}\) represent the weight penalty, occupied space penalty, and interference relationship penalty, respectively. \(\kappa\), \(\lambda\), and \(\mu\) are the relative weight values reflecting the importance of the penalty. \(P_{total}\) is the total penalty value.
## 3 Results and Discussion
### Satellite Clock Comparison
As for the other five models, while the DD clocks for most satellites fall in \(+/-2.0\) ns after convergence, satellite C45 shows a derivation of around 3.0 ns, which may have resulted from differences between bias products used in real-time and reference clock generation. With the convergence part neglected, the DD clocks exhibit rather high intra-day stability, which indicates a small STD value compared to the corresponding RMS value. Unlike diversities among satellites with the other five models, the DD clocks with the B1C/B2a UC model fall into two distinct groups, which vary between \(+1.2\) ns and \(-0.8\) ns. When further examining the satellite types it is found that the two groups correspond to satellites manufactured by the CAST (China Academy of Space Technology) and the SECM (Shanghai Engineering Center for Microsatellites), respectively. Since the GFZ post-processed clock is based on B1I/B3I signals, the intra-day stability of DD clocks with the B1C/B2a UC model suggests good consistency between the B1I/B3I and B1C/B2a signals. Moreover, we must emphasize that the satellite PCO corrections for the B2a signal are absent in the jigs14.atx file and replaced with corresponding B2b values in the study. Being aware of this kind of model imperfection, the consistency between the B1I/B3I UC model and the B1C/B2a UC model is really inspiring. The daily STD values averaged over the last 6 days are depicted in Figure 5 for all of the six models. The STD values are generally less than 0.05 ns, except for satellite C45 with the B1I/B3I/B1C and B1I/B3I/B1C/B2a UC models. The variation of STD values between each model is less than 0.01 ns with the exception of satellite C45. The largest STD value difference between the six models appears for satellite C45, for which the STD value with the B1C/B2a UC model is about 0.015 ns smaller than that with the B1I/B3I/B2a model. In all, the high accuracy and consistency of the real-time clock are clearly visible for all six models, which confirms the feasibility of the developed multi-frequency UC models. The constellation-mean STD values over the experimental period are summarized in Table 3. The statistics generally coincide with those inferred from Figure 5. The relatively larger STD values for the first day are the result of the real-time filter convergence as shown in Figures 3 and 4.
# Recent Advances in Flexible Resistive Random Access Memory
## 3 Material System of Flexible Resistive Memory
### Flexible Electrode Materials
#### 3.2.1 Flexible Metal Electrode  
Flexible metal electrodes include pure electrode (Pt [13, 17, 28, 82, 83, 84, 85], Au [32, 36, 38, 85, 54, 70, 86, 87, 88, 84, 85], W [17, 34, 36, 89, 90, 8, 8, 8, 71, 35, 89, 21, 22, 43, 62, 73, 74, 87, 88, 91, 92], Al [18, 24, 27, 44, 45, 55, 58, 79], Ni [23, 63, 93], Ti [47, 94], Mg [53, 90], Cu [41, 58, 94], etc.) and alloy electrode (Au/Ni [95], Pt/Ti [19], Ni/Ti [96], CdTeb [25], GalnSn [97], etc.), with excellent electrical conductivity and mechanical properties, being easy to manufacture and easy to connect with the advantages of power. Using numerical simulations based on finite element analysis, [PERSON] [98] demonstrated that the defects, the cracks and the delamination of the electrode/active layer (storage medium) interface influences the formation of the conduction path (Figure 10). According to this report, the interface of metal/oxide/metal device has a high Schottky barrier to ensure the electrical stability generally. [PERSON] [99] has proved that the barrier height between the metal electrode and storage medium is an important factor affecting the resistance transition, and the higher the Schottky barrier is, the more stable the bipolar device is observed. On the other hand, the surface of the metal electrode provides ions to form conductive filaments by undergoing a redox reaction, which ensures the progress of resistance transition.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 4 Methodology of Algebraic Point Projection
### Algebraic Point Projection for a NURBS Curve  
As illustrated using Figure 1, iterative numerical solution for point projection may lead to a discontinuity in the projected point or may miss the correct solution. Hence, we develop in this section a purely algebraic point projection algorithm with the following properties:  
1. Exact at any point on the curve or surface, i.e., exact point inversion
2. Controllable accuracy when projected from points near the curve or surface
3. Efficient, non-iterative, and non-recursive solution procedure
4. Footpoints are continuous even near curve segments with high curvature
5. Valid solutions even when projected onto curves with only \(\mathcal{G}^{0}\) continuity  
The present method consists of two steps: estimation of the footpoint in physical space and parametric inversion using the resultant matrix.
## 5 Discussion  
Most global localization methods are based on GPS [27]. However, the GPS technology is problematic.  
Figure 14: Error of the algorithm for all days of 2010. (**a**) Latitude error; (**b**) Longitude error.  
On the other hand, the SGPS is a stand-alone system that is not dependent on any technical infrastructure. It requires a modest hardware and simple software. SGPS is a low cost system with a prototype cost of less than 40 dollars (30 euros).  
SGPS system is energy efficient. The energy consumption depends mainly on the number of measures taken. The system only need to take measurements during sunrises and sunsets, and the system can remain asleep the rest of the time.  
On the down side, the accuracy of the SGPS accuracy is low compared with other systems. Moreover, the position refresh rate is lower than for other systems. For instance, GPS or GSM can refresh the position every few seconds.  
Another drawback is that the object must be stationary and the system does not work properly during the equinoxes, which means that there are several days in which the results are not reliable at all. However, this problem can be solved by interpolation as it is well known when equinoxes occur.  
The system will not work beyond the Northern or Southern polar circles during the summer or winter seasons since there are no sunrises or sunsets. More specifically, the limitation implies that the system can be used only when there is a sunrise or sunset in those latitudes which satisfy the following conditions:  
\[-90^{\circ}+\delta<\varphi<90^{\circ}-\delta\]  
during the Northern Hemisphere summer, or:  
\[-90^{\circ}-\delta<\varphi<90^{\circ}+\delta\]  
during the Northern Hemisphere winter. In both equations \(\delta\) is the sun declination and \(\varphi\) the latitude. Out of the latitudinal ranges it is total daylight or total nighttime.
## 5 Simulation and Discussion
### Sensory Filtration for Aquatic Monitoring
It is worth mentioning that the proposed system achieved 98.5% of the sensory filtration method in most of the acquired samples and close to 99.1% of an adaptive prediction method. Compared to the recent methods, the researchers in [8; 10] focused on improving water quality prediction and anomaly detection in sensory monitoring of fish farming, which is the same principle as our method. Furthermore, the wireless transceiver boards for underwater sensors deployed in genuine underwater environments are identical. Also, they measured the same water quality variables obtained from similar fish farms. The comparison methods have improved decision-making for fish farmers and reduced economic costs by minimizing labour-intensive monitoring. However, these methods were only sufficient to detect anomalies in real-time from complex time series data collected by water sensors, unless deep learning techniques are exploited to cover the issued anomalies and filter the observed sensory yields from the network to ensure the safety and sustainability of the water. Also noteworthy is their lack of limited pure signals and their reliance on sensory inputs. In addition to the misbehaviour caused by intermittent signal transmission and noisy data. The proposed method leveraged the strengths of both convolutional networks and LSTM networks in the selected comparative methods and addressed adaptive filtration for aqua signals, to efficiently identify unexpected patterns or values that could indicate water quality issues. The LSTM-CCN approach has shown impressive accuracy, with a rate of 98.5% for the sensory filtration method in the majority of the obtained data and close to 99.1% for an adaptive prediction method, while also consuming little energy during lengthy monitoring, making it highly effective in aqua environments. The suggested system achieved a detection accuracy in the monitoring field estimated at 97.8% and an error rate of ~2.3% in alerting messages throughout testing samples. The proposed method used a huge quantity of data, then processed by each node individually. Additionally, the suggested approach is more energy efficient because it reduces the amount of time that each node has to spend processing data. And each node goes into sleep mode until the next epoch of data is ready to be processed. Finally, the suggested approach is highly scalable for underwater environmental monitoring applications because it can be easily adapted to different network sizes and topologies.
# An Event-Driven Agent-Based Simulation Model for Industrial Processes
## 3 Agent-Based Model
### Agents Behavior
#### 3.2.4 Product Reallocation  
The interaction between Product Agent, Customer Order Agent and VPA Agent as well as their behavior when a reallocation occurs is depicted in Figure 8.  
After being processed by a machine, a Product Agent receives the updated processing parameters by the Machine Agent, as shown in Figure 6. The Product Agent is able to compare the new parameters with those ones required by the customer. Therefore, the Product Agent sends a request to the Customer Order Agent in order to know the quality target parameters. After receiving the response from the Customer Order Agent, the Product Agent checks if the new processing parameters respect all the quality target constraints. If the constraints are not satisfied, then the Product Agent sends a customer reallocation request to the VPA Agents. The VPA Agent will try to find a new customer order for the product and if a new one is found, the VPA Agent will inform the Product Agent. In this way, the product is reallocated to a new customer and the Product Agent can update its customer information, whereas if a new customer is not found, the VPA Agent sends a suspension request to the Product Agent and it becomes available for a new reallocation in the future.  
Figure 8: Interaction between Product Agent, Customer Order Agent and VPA Agent when a product reallocation is requested.
# Synthetic Approaches to 1,3,4-Oxadiazole-Containing Boronic Derivatives
## 3 Synthesis of Boronic Derivatives from Reagents Containing 1,3,4-Oxadiazole Moiety
### Indirect Connection of Boron with 1,3,4-Oxadiazole Ring via Phenotype Linker
#### 3.2.3 Derivatives of Ortho-Substituted (1,3,4-Oxadiazol-2-yl)phenylboronic Acid  
The 2-(1,3,4-oxadiazol-2-yl)phenylboronic acid or its pinacol ester derivative can be obtained via three approaches (methods A, B, and C). The first, method A, involves a lithiation bordism sequence of 2-(2-bromophenylyl)-1,3,4-oxadiazol derivatives using _n_-BuLi and triisopropyl borate in a toluene/THF mixture at \(-78\)\({}^{\circ}\)C, followed by acidic hydrolysis (Scheme 15) [15].  
An alternative method involves the Miyaura bordism reaction (Scheme 15, method B). (2-Bromophenyl)-1,3,4-oxadiazole intermediate can be coupled with bis(pinacolato) diboron using Pd(dppf)\({}_{2}\)Cl\({}_{2}\) as a catalyst, AcOK as a base, and DMSO as a solvent at 80 \({}^{\circ}\)C for 2-8 h. The resulting 2-(1,3,4-oxadiazol-2-yl)phenylboronic acid pinacol ester can then be hydrolyzed using 0.2\(M\) sulfuric acid to yield 2-(1,3,4-oxadiazol-2-yl)phenylboronic acid [15]. (2-Chlorophenyl)-1,3,4-oxadiazol can also be used as substrates in Miyaura bordism reactions. These reactions typically utilize Pd(OAc)\({}_{2}\) as the catalyst, AcOK as the base, and tricyclohexylphosphine as the ligand. Refluxing the reaction mixture in 1,4-dioxane for 2 h affords the pinacol boronic ester in 75-86% yield [21].  
A third strategy involves the formation of the 1,3,4-oxadiazol ring from a precursor containing a boronic ester group (Scheme 16, method C). The derivatives of benzhydrazideor benzoic acid bearing a boronic ester group can be cyclized using trichloroacetonitrile and triphenylphosphine in acetonitrile at 130 \({}^{\circ}\)C. Subsequent hydrolysis of 2-(1,3,4-oxadiazol-2-yl)phenylboronic acid pinacol ester with ammonium acetate and sodium periodate in an acetone/water mixture affords the desired boronic acid [15].
# Effect of Laser Treatment on Intrinsic Mechanical Stresses in Titanium and Some of Its Alloys
## 2 Materials and Methods
### Mechanical Tests  
Nano-indentation tests were performed using a nano-indenter NanoTestTM Vantage (MicroMaterials, Wrexham, Great Britain) with a Berkovich diamond indenter. Tests were performed for base material (BM), BM after annealing, laser treatment (LT), and LT after annealing samples. For each sample tested, 25 (\(5\times 5\)) independent measurements with a distance between each indentation of 30 \(\upmu\)m in both axes were performed. Measurements were performed for two maximum force values: 50 and 200 mN. For both force values, the loading time was 20s, the holding time with the maximum force value was 5s, and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Annealing Temperature** & **Annealing Time** \\  & **(\({}^{\circ}\)C)** & **(Minute)** \\ \hline Ti grade IV & 600 & 30 \\ Ti7 Nb6 Al & 500 & 10 \\ Ti15 Mo & 800 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Heat treatment parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Power** & **Duration of the Laser Beam** & **Speed of Laser Beam** & **Frequency [Hz]** & **Laser Wavelength** & **Overlapping** [\%] \\
**[W]** & **Laser Pulse [ms]** & **[mm\(\backslash\)s]** & **[Hz]** & **[\(\upmu\)m]** & **[\%]** \\ \hline
1000 & 1 & 1 & 25 & 1.064 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The laser treatment parameters.  
unloading time was 20s. To convert the reduced Young's modulus into Young's modulus, the Poisson's ratio value for the test material was assumed as 0.23.
## 2 Materials and Methods
### Phytochemical Analysis  
Phenolic acids were identified using LC-MS. The crude hydromethanolic extract from _S. austriaca_ hairy roots was obtained through a 45 min extraction in an ultrasonic bath at room temperature. For qualitative analysis, the dry residue was dissolved in methanol and analyzed using UHPLC-DAD-ESI-MS/MS. Chromatographic separation was performed on a UHPLC-3000 RS system (Dionex, Germering, Germany) equipped with a DAD detector and an AmaZon SL ion trap mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany). The separation was achieved on a Zorbax SB-C18 column (150 \(\times\) 2.1 mm, 1.9 \(\upmu\)m) (Agilent, Santa Clara, CA, USA) with a mobile phase consisting of water (A) and acetonitrile (B) and the following gradient: 0-60 min, 5-40% B. The ESI interface parameters were as follows: nebulizer pressure at 40 psi, drying gas flow rate at 9 L/min, nitrogen gas temperature at 300 \({}^{\circ}\)C, and capillary voltage at 4.5 kV. Mass scanning was conducted over a range of 100 to 2200 \(m/z\), with UV spectra recorded from 200 to 400 nm and detection at 325 nm. Compounds were identified by comparing the mass spectrometric data with literature references. Table 1 presents data on the identification of phenolic acids in the tested material, while Figure 2 displays the corresponding UHPLC chromatogram from this analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Peak No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{\
\begin{tabular}{c} **Retention** \\ **Time (min)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(\uplambda_{\text{max}}\)** \\ **(nm)** \\ \end{tabular} } & \multicolumn{2}{c}{**Negative Ion Mode**} \\ \cline{4-5}  & & & & **[M-H]**– & **Major** \\  & & & & & **Fragments MS\({}^{2}\)** \\ \hline
1 & Caffeic acid & 16.0 & 323 & 179 & 135 \\
2 & Rosmarinic acid & 30.1 & 327 & 359 & 197, 179, 161 \\
3 & Salvianolic acid A & 30.7 & 312 & 493 & 359 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data of identified phenolic acids (Retention time, \(\uplambda_{\text{max}}\), and MS spectroscopic data) in _S. austriaca_ transformed roots.  
Figure 1: _S. austriaca_ hairy root cultivation bioreactors: (1)—mist-trickling bioreactor; (2)—temporary immersion bioreactor (Plantform); (3)—air compressor; (4)—peristaltic pump; (5)—temporary electric relay.
# Deep Subspace Clustering with Block Diagonal Constraint
## 3 Experiments
### Experimental Settings
#### 3.1.2 Databases  
We evaluated DSC-BDC method on four public databases, i.e., the Extended Yale B, ORL and PIE face image databases and the COIL20 object image database. A brief introduction of these databases is listed in Table 1.  
* **Extended Yale B database:** Extended Yale B [50] consists of 2414 frontal face pictures of 38 subjects under 9 poses and 64 illumination conditions. For each subject, there are 64 images. Each cropped face image consists of \(192\times 168\) pixels. In the experiment, we use the first 14 subjects with a total of 882 images for testing. All images are resized to \(32\times 32\) pixels.
* **ORL database:** The ORL database [51] is composed of 400 photographs of size \(112\times 92\) from 40 different individuals where each subject has 10 images taken under diverse variation of lighting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **Sample Number** & **Size** & **Class** \\ \hline Extended Yale B & 882 & \(32\times 32\) & 14 \\ ORL & 400 & \(32\times 32\) & 40 \\ PIE & 1428 & \(32\times 32\) & 68 \\ COIL20 & 1440 & \(32\times 32\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed information about used databases.  
conditions, poses and facial expressions. Following the literature, we downsample the images from their raw size to \(32\times 32\).
* **PIE database:** This face image database [52; 53] consists of 40,000 photographs of 68 individuals, illustrating 13 pose conditions for each person, 43 with illumination levels and four expressions. It is worth noting that the pictures in the PIE database are all color pictures. In the experiment, we select 1428 samples of 68 subjects, and the sample size is \(32\times 32\). For convenience, the color images are converted to gray-scale images.
* **COIL20 database:** COIL20 [54] is a color object image database with 1440 images of 20 objects. Each object was placed on a turntable against a black background, and 72 images were taken at pose intervals of 5 degrees. For convenience, the color images are converted to gray-scale images; however, this is not necessary. The images are down-sampled to 32 \(\times\) 32.