esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Discussion of Adsorption Mechanisms
#### 3.3.3 Adsorption Isotherms  
Two common adsorption isotherm models were fitted for the adsorption of four metal ions with the GO/P(AA-co-AM) hydrogels to investigate the mode of the GO/P(AA-co-AM) hydrogels for the adsorption of four metal ions. The Langmuir isotherm is based on relatively homogeneous and intermolecular force-free single-molecule layer adsorption, while the Freundlich isotherm is based on a non-uniform adsorption system where the adsorbent is a multi-molecular layer. The results of the adsorption of the four metal ions on the GO/P(AA-co-AM) hydrogels were fitted using the two adsorption isotherms and the fitted adsorption isotherms are shown in Figure 8. The fitted equations for the adsorption of the four metal ions by the GO/P(AA-co-AM) hydrogels can be obtained from the fitted results, where the R\({}^{2}\) obtained from the Langmuir isotherm for the four metal ions is greater than the R\({}^{2}\) obtained from the Freundlich isotherm for the four metal ions, which indicates that the GO/P(AA-co-AM) hydrogels exhibit a high adsorption capacity. The adsorption mode of the four metal ions tends to be a relatively homogeneous, intermolecular force-free single-molecule layer adsorption, with a fixed number of active adsorption sites on the surface of the adsorbent, each with the same adsorption energy, and no combination with other adsorbates after the desired sites are occupied. The saturation amounts of the GO/P(AA-co-AM) hydrogels for the adsorption of Cu(II), Pb(II), Zn(II), and Cd(II) ions are 1268.65 mg\(\cdot\)g\({}^{-1}\), 2026.87 mg\(\cdot\)g\({}^{-1}\), 704.24 mg\(\cdot\)g\({}^{-1}\), and 632.91 mg\(\cdot\)g\({}^{-1}\), respectively, which  
Figure 7: Diffusion model of Cu(II), Pb(II), Zn(II), and Cd(II) ions adsorbed by GO/P(AA-co-AM) (\(C_{0}=1000\) mg\(\cdot\)g\({}^{-1}\), pH = 4, T = 25 \({}^{\circ}\)C).  
are consistent with the actual experimental saturation amounts of the GO/P(AA-co-AM) hydrogels. The results were similar to those obtained from the actual GO/P(AA-co-AM) hydrogels for the adsorption of the four metal ions. The experimental results indicate that the hydrogels have good adsorption properties for all four metal ions.  
The adsorption of GO/P(AA-AM) hydrogels and other hydrogels in the literature for metal ions were compared as shown in Table 2.
## 2 Materials and Experimental Testing
### Experimental Testing
#### 2.3.1 Ultrasonic Testing  
The ultrasonic pulse velocity (UPV) was evaluated on samples as specified in Standard NF P 18-418 [44]. This non-destructive test is based on the propagation of ultrasonic waves in the tested material. Thus, the time transmission was measured using a UPV tester (Figure 6). The velocity of the pulse through the specimen was evaluated using Equation (1):  
\[\mathrm{V}=\frac{\mathrm{d}}{\mathrm{t}} \tag{1}\]  
where V (m/s) is the velocity of the ultrasonic pulse, d (m) is the path length of the sample, and t (s) is the traverse time of the pulse.  
The total water absorption (TWA) measurement was crucial because it provided insights into the composite sensitivity to water and porosity, which is correlated with its engineering properties. This test consisted of immersing blocks in bathwater for different periods, namely 1, 2, 3 and 4 days, and then the TWA was calculated using Equation (2):  
\[\mathrm{TWA}(\%)=\frac{\mathrm{Wh-Ws}}{\mathrm{Ws}}\times 100 \tag{2}\]  
where Wh (g) is the wet block weight (after immersion), and Ws (g) is the dry block weight (before immersion).
## 4 Analysis and Experimental Results
### Experimental Setup  
In order to achieve the stated goal, a standard QPSO algorithm was designed with the parameters shown in Table 1, which are used as standard values capable of speeding up the overall QPSO procedure. The search range for the tile dimensions was limited to the interval (0.3, 1.2) m for both horizontal and vertical dimensions, while the offset was limited to the actual tile size to avoid placing tiles without experimental data in the lower-left corner of the research area.  
The algorithm for the proposed technique was developed in Matlab and ran on a Windows-based 2.6 GHz Core i7 desktop. The QPSO algorithm, like any other heuristic optimization process, is supposed to provide better search results using larger populations and a greater number of generations. However, for this work, each cost calculation took an average of 50 s; thus, the values reported in Table 1 were accepted as a good compromise between computing time and the quality of the QPSO capabilities.  
The outcome of any MLP is usually heavily dependent on the initial random set of the training weight matrixes. Consequently, a large amount of MLP training runs are required to identify suitable initial weight choices and achieve meaningful results.  
A number of validation runs were performed to investigate the effect of the random positioning of the 20 test points inside the research area. In agreement with the investigation presented in our previous research [32], no meaningful difference was observed in the positioning error for constant test points versus random ones. Both in these validation runs and in the actual QPSO algorithm, the MLP was run 1000 times in order to achieve the best performances, i.e., to identify a suitable set of weights for which the positioning error of the test population was minimal. For each training run, the profiles of both the training and testing positioning errors versus the learning iteration were recorded, and the absolute minimum of the testing error was identified, as shown in Figure 3.  
It was observed during the validation runs that the absolute minimum of the test error was most probably achieved shortly after 4000 iterations. Consequently, it was decided to stop the MLP learning run after 5000 cycles of feedforward-backpropagation as a good trade-off between accuracy and total computing time. The cost value for each QPSO individual was evaluated as the average of the best 50 of 1000 MLP runs, which would correspond to fortunate initial random choices of the weights.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Design Parameters** & \multicolumn{3}{c}{**Architecture Parameters**} \\ \hline Number of individuals & 20 & \(c_{1}\) & 1 \\ Number of iterations & 100 & \(c_{2}\) & 1.5 \\ Number of cost calculations & 2000 & Initial \(\beta_{0}\) & 1 \\ Search range (tile size) & (0.3, 1.2) & Final \(\beta_{0}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: QPSO design and architecture parameters for tile size optimization.
# The Exploitation of Lysosomes in Cancer Therapy with Graphene-Based Nanomaterials
## 5 The Role of Lysosomes in GNM-Based Cancer Therapy
### Lysosomes Enable Detection of Cancer Cells by the GNMs  
Finally, lysosomes are not only directly involved in the antitumor activity of GNMs, but the specificities of the lysosomal environment, such as its acidity or the presence of proteolytic enzymes, may also facilitate the identification of tumor cells, as demonstrated in the following studies.  
[PERSON] and colleagues loaded DOX onto a (CA-BDP)-PPDN/rGO complex, composed of chloro-3\({}^{\prime}\),\({}^{\prime}\)-dihydroxyacetophenone (CA), a fluorescent dye boron-dipyrromethane [BODIPY(BDP)], PEGylated pH-sensitive \(N\),\(N\)-dimethylacrylamide and thermosensitive N-isopropylacrylamide (PPDN), and rGO. Within the nanocomposite, rGO quenched the emission of [BODIPY(BDP)] fluorescence at a normal pH of 7.4, but not at a pH of 6, which mimicked lysozyme. Accordingly, the complex displayed intense fluorescence in the lysosomes of human MDA-MB 231 breast cancer cells. Therefore, the complex has potential not only as an antitumor therapy mediated by DOX, but also as a fluorescent probe to identify cancer cells [84].  
In the aforementioned research by [PERSON] et al., GO was used to quench the fluorescence of the photosensitizer Ce6 within a nanocomposite composed of Ce6-Pep, DSPE-PEG2000-FA, and GO. Upon entering the lysosomes of HeLa cells, cathepsin B cleaved the peptide to release the fluorescent Ce6 from GO. The lysosomal pH also stimulated the discharge of Ce6 from the nanocomposite. The nanocomposite was internalized by cancer cells in mice bearing HeLa tumors via folate receptor-mediated endocytosis and displayed strong fluorescence after entering their lysosomes, enabling the identification of cancer cells in vivo [16].  
Finally, in the previously mentioned study by [PERSON] and colleagues, graphene from the smart vehicle GPCP quenched the fluorescence of the photosensitizer ICG. When ICG was released in lysosomes due to the acid-induced charge reversal effect, it began to fluoresce.  
Since the complex preferentially accumulated in tumor tissue, fluorescent tumors were readily observed in the MDA-MB-231 tumor-bearing mice [12].  
It is worth mentioning that some studies discussed earlier have used GNMs for imaging purposes. However, we avoided discussing them in this section for two reasons: the lysosomal degradation of graphene-based DDSs did not permit specific tumor cell detection but only intracellular localization of the DDSs [59; 63]; and GNM-dependent imaging of tumor cells was not related to lysosomal activity [62]. Fluorescence not only plays a crucial role in the detection of tumor cells but can also be important in determining the exact time and location of irradiation in PTT and PDT.
# A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
## 1 Introduction
It is worth noticing that the amount of data to be transferred is an important aspect to be considered in a wireless link, as it affects the power consumption, the required bandwidth, and also the reliability of the communication, eventually. In order to address such issue, compression techniques, such as those shown in [20; 21], were proposed to minimize the amount of raw data to be transferred, without affecting the fault detection. Besides such solutions, a different approach consists in the distributed computing paradigm, where each node is provided with sufficient computational capability to execute some part or the entire signal processing. The authors of [22] proposed a system where the frequency spectrum of the envelope is evaluated by the module itself, and then wirelessly transferred to a host computer, which is also responsible for the evaluation of the optimum band-pass filter to be applied. Nonetheless, none of the aforementioned works proposed a low-cost wireless module able to detect the faults locally. In such a way, the data transfer can be truly minimized, as the node would occupy the wireless link only in the presence of a fault. Aiming to minimize the cost of such a module, a low-cost microphone and a microcontroller with an integrated radio transceiver are exploited. In such a way, the acoustic emission delivered by the bearing is acquired and processed, and the faults are detected, with all the steps locally performed. This work proposes such a wireless module, where the cost and hardware and software complexity are kept at minimum. Indeed, the standard Bluetooth Low Energy wireless link allows for a on-the-fly configuration of the module from several compatible devices, thus achieving both user and machine-to-machine (M2M) interaction. By choosing target designs of low cost and small size, it results in different benefits, as the former allows one to place more modules, and the latter to position the proposed system in the most convenient location closed to the bearing to be monitored. In such a way, even in the presence of a noisy environment, the module is able to assess the healthy status of the bearing to be monitored. The paper is organized as follows. In Section 2, the processing technique and its optimization are discussed. In Section 3, the proposed system is introduced and its main functional blocks are analyzed, along with the fault detection algorithm. Experimental results, regarding the computational performance and the detection capability of the designed module in the presence of environmental noise, are discussed in Section 4, and final conclusions are drawn in Section 5.
# System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam
## 1 Introduction
It improved stability but only used limited parameters. Contact conditions and surface roughness predictions are proposed in [26; 27]. Visual-based inspection after grinding is presented using a deep learning method in [28]. Similar to the above research status of robotic grinding, as far as the author knows, most of the monitoring methods are for surface grinding. For welding seam grinding, the most concerned problem is that the shape and state of the welding seam are unknown. Therefore, it is necessary to measure the weld seam before welding. At the same time, these measurement results also need to be combined with the real-time grinding data to be used in the monitoring method. Due to the special characteristics of the welding seam, the traditional monitoring method based on mathematical models is not applicable, so only statistical learning methods can be used. Although the method of deep learning has been applied to weld seam inspection [28], for the research in this paper, sample acquisition is difficult and laborious. This is because each sample acquisition requires robotic scanning of welds, weld analysis, robot grinding, and evaluation of grinding results. Therefore, it is difficult to adopt a large sample method. In this paper, the authors present a system of robot grinding for friction stir weld seam. In order to monitor the excessive grinding, a monitoring method combing the weld seam measurement result and the processing data is proposed. Meanwhile, in order to extract the suitable weld seam data for the monitor method, a weld seam point cloud extraction algorithm based on graph-cut is proposed. It should be noted that there are two kinds of weld seam in actual production: uniform line weld seam and the uneven part, as shown in Figure 1. The uneven part is a large trimming, which is usually unavoidable. In the grinding process, the uniform line weld seam is ground paralleled to the line, and the uneven part are ground vertically from the top of the workpiece. The authors put forward two methods to monitor whether there has an excessive grinding. Our contributions are: (1) proposing a monitoring-integrated robot grinding system, (2) presenting a method for extraction of the friction stir weld seam based on the point cloud, and (3) presenting a method for monitoring the excessive grinding based on the Particle Swarm Optimization-Support Vector Machine (PSO-SVM) method. This paper is organized into four sections. The method for system design, extraction of the weld seam, and monitoring lie in Section 2. The experimental results and discussions are reported in Section 3, and finally, the conclusion is presented in Section 4.
## 3 Seismic Structural Response
### Results for 6-Story Building
When both components were included, this led to 0.0303 rad and 2.790y, representing a very significant increase of 203.2% with respect to the single-component analysis. Figure 7: Non-linear response of the outer-most fiber in different beams for different earthquakes; 6-story buildings (Part 1). For the earthquake recording of station LANE, the most critical beam is on the right side in Floor 5. When only the horizontal component is considered, the maximum rotation reaches 0.0183 rad, or 1.699y. When both components are included in the analysis, the values are 0.0295 rad and 2.710y, respectively, which represents an increment of 60.3% in this case (Figure 8, LANE-5S-RS). For the recording of station HMTT the rotations and equivalent values considering one or the two components for the non-linear dynamic analyses are 0.0110 rad and 1.010y and 0.0154 rad and 1.410y, respectively. This represents an increase of the two-component dynamic analysis in relation to the horizontal component analysis of 39.6% (Figure 8, HMTT-5S-RS). Finally, for earthquake recorded at station RABO (Figure 8, RABO-6S-LS), the most critical beam is located on the left side of the 6 th level. In this case, for the horizontal component seismic analysis, 0.0094 rad and 0.870y were obtained. When both components were included, this led to 0.0191 rad and 1.750y, representing a significant increase of 101.1%. As a summary, the previous values are listed in Table 2.  
Figure 9 shows plastic hinges for JAMI seismic record where the red color indicates that the plastic hinge is formed. The color contour represents the plastic rotation depicted graphically with the GiD+OpenSEES interface, i.e., it shows the zones where the plastic hinges occur. The red/blue color indicates higher/lower degree of plastic rotation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rotation**} & \multicolumn{4}{c}{**Rotation Values for Earthquake Station Register Considered (rad)**} \\ \cline{2-7}  & **NILT** & **CALE** & **JAMI** & **SCRU** & **LANE** & **HMTT** & **RABO** \\ \hline \multirow{2}{*}{\(\theta_{h}\)} & 0.0293 & 0.0268 & 0.0102 & 0.0100 & 0.0183 & 0.0110 & 0.0094 \\  & (2.699y) & (2.469y) & (0.940y) & (0.920y) & (1.699y) & (1.010y) & (0.870y) \\ \multirow{2}{*}{\(\theta_{h+\texttt{p}}\)} & 0.0348 & 0.0407 & 0.0134 & 0.0303 & 0.0295 & 0.0154 & 0.0191 \\  & (3.200y) & (3.730y) & (1.230y) & (2.799y) & (2.710y) & (1.410y) & (1.750y) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Rotations generated by horizontal earthquake components and by horizontal and vertical earthquake components acting together (6-story building). Figure 8: Non-linear response of the outer-most fiber in different beams for different earthquakes; 6-story buildings (part 2).
## 2 Scanning Electron Microscopy and Mineral Characterization
### Automated SEM Mineral Liberation Analysis (SEM-MLA)  
Recent software developments in SEM have incited dominant growth in its application in solid matter investigations. One of the economic solutions is the use of mineral liberation analysis (MLA) for optimizing the mineral processing methodology of metallic ores. SEM-MLA has been an important driver in transforming numerous software versions for SEM applications [84]. SEM-MLA was designed to quantify the mineralogy of ores. After the mining process, the ore is processed to increase the concentration of minerals of interest (and value). The processing of ores is also important for removing minerals of no value or those with detrimental effects on the needed mineral products. This processing of grinding  
Figure 6: SEM-AM methods of one measurement frame showing BSE (**upper row**, **a–d**) and EDS (**lower row**) images. Numerous single EDS analysis points map each grain with a distinguishable BSE gray level and are visualized as color-coded pixels, such as the garnet grain, which is indicated by red-colored pixels [84]. CC-BY.  
the ones and liberating the minerals of interest provided rapid automated analysis of target minerals and extensively improved the process.  
A mineral liberation analyzer (MLA) based on SEM was developed in the late 1990s by the JKRMC (Julius Kruttschnitt Mineral Research Centre, Australia), and it is currently commercially available [118]. In MLA, minerals are differentiated by attaining and combining the information gathered from EDS and BSE. Depending on the size range of the particles in the sample, size fractions from the sample are produced. Then, liberation is measured in each size fraction, followed by liberation reconstruction of the whole sample. The measurement of mineral liberation is usually carried out through one of two methods, i.e., either the area method or the linear intercept method. Liberation by area measurement has shown lower stereological error compared to linear measurement.  
It is important to note that liberation measurements by the linear intercept method are known as one-dimensional, while area method measurements are called two-dimensional liberations. Both of the measured liberations are lower dimensional projections of the true volumetric liberation, which is three-dimensional. Stereological correction is based on stereological transformation and prediction of liberation measurements. This stereological correction can be based on entropy regularization [119]. Correction of the apparent liberation and production of three-dimensional liberations have also been described in several other investigations [120; 121; 122]. Various operating modes for the MLA system are available, i.e., X-ray modal analysis (XMOD), particle X-ray mapping (PXMAP), selected particle X-ray mapping (SXMAP), sparse phase liberation analysis (SPL), standard BSE liberation analysis (BSE), extended BSE liberation analysis (XBSE), and rare phase search (RPS) [123]. The use of SEM-MLA is shown in Figure 7 for quantifying the mineralogy of a hydrothermally overprinted alkali plutonite [85].
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### Performance Evaluation of Pido
#### 4.4.1 A Comparison between Pido, CTP and ORW  
We first provide a brief introduction to CTP and ORW, and then compare the metrics among them. A summary of Pido, CTP and ORW is revealed in Table 2.  
**Collection Tree Protocol (CTP)** is a classic routing protocol, which is widely utilized in TinyOS and other embedded operation systems. CTP is designed for WSN with tree topology, and it proposes ETX as routing metric. With continuous link quality estimation, CTP makes nodes to select parents with the least ETX to the sink. In conclusion, routing paths selected by CTP should have optimal expected transmission times, which is energy efficient.  
**ORW**, an opportunistic routing protocol, introduces EDC (Expected Duty Cycled wakeups) as routing metric. EDC can be regarded as an adaption of ETX. Besides link quality, EDC also takes the duration of waiting neighbors to be awake into account. ORW strikes a balance between link quality and delay, which should be energy efficient to some extent and can decrease end-to-end delay when compared with CTP.  
With the analysis above, CTP should be the most energy-efficient one, and ORW decreases end-to-end delay with the consideration of link quality in the meanwhile. However, both CTP and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Metric** & **The Calculation of Metric** \\ \hline Pido & _delay_ & \(delay=\sum\limits_{i=1}^{n}\mathit{ld}_{i,i+1}+\sum\limits_{j=2}^{n-1}\mathit{ nd}_{j+1}\) \\ CTP & ETX (Expected Transmissions) & Calculated by a Link Quality Estimator \\ ORW & EDC (Expected Duty Cycled wakeups) & \(EDC_{i}(S_{i})=\frac{1}{\sum\
olimits_{i\in S_{i}}p_{ij}}+\frac{\sum\
olimits_{i \in S_{i}}S_{i}p_{ij}ED_{i}}{\sum\
olimits_{i\in S_{i}}S_{i}p_{ij}}+\omega\)* \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison between Pido, CTP and ORW.  
ORW do not take node status into account, while Pido does. Based on this, in harsh environments like intertidal zones, Pido shows efficiency with lower end-to-end delay.