esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Acquired Neurochaperonopathies
### Vcp/p97  
The valosin-containing protein (p97 or VCP) is a homohexameric AAA+ ATPase protein of about 97 kDa. It is an abundant and ubiquitous molecule involved in cell cycle regulation [136], autophagy [137], DNA repair [138], and ER-associated and ubiquitin-mediated protein degradation [139]. VCP has been found within protein aggregates in PD, ALS, AD, and in polyglutamine aggregates [130]. Polyglutamine disease is characterized by deacetylation of histones H3 and H4 and a correlation of this deacetylation with VCP activity has been suggested [130]. VCP was found in the PC12 cell nucleus in protein aggregates with polyglutamine expansions, showing PTM, such as phosphorylation at Ser-612 and Thr-613, and acetylation at Lys-614 [130]. Nuclear, post-translationally modified VCP is associated with neurite retraction and shrinkage, features which have been found also in PC12 cells expressing protein aggregates with polyglutamine expansions [130]. Therefore, it has been suggested that VCP could be a mediator of histones H3 and H4 deacetylation, even if the underlined mechanism remains to be elucidated. VCP appears to be essential for the clearance of \"occasionally\" aggregated proteins, promoting the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Status** & \begin{tabular}{c} **Molecular** \\ **Chaperone** \\ \end{tabular} & \begin{tabular}{c} **Human** \\ **Ortholog** \\ \end{tabular} & **PTM** & **Effect** & **Ref.** \\ \hline _Drosophila_ & Wt & Bip & Grp78 (HSPA5) & AMPylation & Blindness & [123] \\ \hline _Saccha-romyces cerevisiae_ & Wt & Hsp70 & HSPA & AMPylation & \begin{tabular}{c} Decreased cell growth; \\ protein misfolding; toxic \\ protein aggregation \\ \end{tabular} & [125] \\ _Caeno-rhadditis elegans_ & AD; PD & HSP-1; HSP-3; HSP-4 & HSPA & AMPylation & \begin{tabular}{c} Large cytoprotective \\ protein aggregates \\ \end{tabular} & [127] \\ PC12 cells & Wt & Hsp90 & HSPC & Nitration & ALS & [128; 129] \\ PC12 cells & Wt & VCP & p97 & \
\begin{tabular}{c} Phosphorylation \\ and acetylation \\ \end{tabular} &
\begin{tabular}{c} Neurite retraction and \\ shrinkage \\ \end{tabular} & [130] \\ \hline \hline \end{tabular} Abbreviations: PTM, post-translation modification; Ref., reference; Wt, wild type; Hsp or HSP, heat shock protein; AD, Alzheimer’s disease; PD, Parkinson’s disease; ALS, amyotrophic lateral sclerosis; VCP, valosin-containing protein.  
\end{table}
Table 2: Effects of PTM on molecular chaperones produced by in vivo and in vitro wild type and disease models.  
deacetylation of H3 and H4 and, in turn, reducing the transcription of de novo proteins; however, during chronic and prolonged protein aggregation, nuclear translocation and post-translational modification of VCP could induce neurodegeneration and cause an atrophic phenotype as observed in the _Drosophila_ experimental model [130].
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 3 State-of-the-Art
### Offline Storage Technologies  
In legacy systems, offline storage refers to any storage medium that must be physically inserted into a system every time a user wants to access or edit data. Offline storage can be any type of storage that can be easily removed from the computer and is not currently online, live, or connected to the computer. The data stored in offline storage remain permanently in the storage device even if it is disconnected or unplugged from the computer after the data have been stored. Offline storage is generally portable in nature and can be used on different computer systems. Known also as removable storage, examples of offline storage include floppy disks, compact disks, and USB sticks.  
In computers of the prior state-of-the-art, data storage constitutes one of the key components of the computer architecture. However, there is no provision or concept of offline storage within a connected computer. In other words, when a computer connects to the Internet, the stored data, by default, also go online, exposing it to network risks. Hence, the state-of-the-art is completely blank in terms of the in-computer offline storage (ICOS) module that constitutes one of the two modules of the ZVC architecture.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 2 Related Theories and Methods
### Ensemble Patch Transformation (EPT)  
The ensemble patch transformation was proposed by [PERSON] et al. [39]. The EPT method uses the multi-scale concept of computer vision scale space theory to enhance the recognition of local features in the signal data and extract the essential components of the signal data. It consists of two key components, the \"patch process\" and the \"ensemble process\". Due to the flexibility of the EPT framework and the adaptability of the parameters, EPT can be used to discover specific patterns in signal data, such as trends, sudden changes, and periodicity. The text will use EPT to extract the trend component of the load series. Both components will be described below.
## 7 Numerical Results  
### System Data  
The proposed optimization problem is applied to a typical smart MG including two 60 kW similar micro-turbines, one 60 kW PV system, one EVs charging station and some loads. The micro-turbines' parameters are shown in Table 6[52]. The carbon dioxide (CO\({}_{2}\)) produced by a micro-turbine is considered to be 0.7 kg/kWh and its emission cost is 0.001 $/kg [55]. The micro-turbines' quadratic production cost is approximated by a three-piecewise linear function.  
The PV system consists of 6 \(\times\) 25 solar modules parallel to 25 rows, each with 6 series modules. The rated power of each module is 400 W in the standard condition. The price of each module including the power electronics (inverter) and installing costs, is considered $1 per watt. The operation and maintenance cost of the PV system is assumed to be 1.5% of its initial investment. The interest rate and the period for the loan repayment of the PV system are considered to be 7% and 20 years, respectively. According to the PV system's specifications, its capacity factor is considered to be 26%. Historical data of direct solar irradiance power is extracted from [56]. The scheduling horizon is 24 h and each time interval is considered to be 1 h. The forecasted loads and upstream market prices are taken from [52]. As can be seen, hour 8 is the beginning of the peak periods. The PDFs of uncertain parameters are calculated and shown in Table 7. In this table, the mean values of parameters are hourly forecasted parameters. Based on this table and Section 2, the number of 200 scenarios are considered.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Unit** & _a (S)_ & _b (S/kW)_ & _c (S/kW2)_ & _P\({}^{min}\) (kW)_ & _P\({}^{max}\) (kW)_ \\ \hline MT 1 & 0.4 & 0.0397 & 0.00051 & 20 & 60 \\ MT 2 & 0.4 & 0.0397 & 0.00051 & 20 & 60 \\ Unit & _MUT_ (h) & _MDT_ (h) & _RUI_ (kW/h) & _RD_ (kW/h) & CO\({}_{2}\) (kg/kWh) \\ MT 1 & 1 & 1 & 40 & 40 & 0.7 \\ \hline MT 2 & 1 & 1 & 40 & 40 & 0.7 \\ \hline \end{tabular}
\end{table}
Table 6: Parameters of micro-turbines.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Time (h)** & **Solar Irradiance (W/m2)** & **Market Price (c/kWh)** & **Load (kW)** \\ \hline  & \multicolumn{3}{c}{**Beta Distribution**} & \multicolumn{3}{c}{**Normal Distribution**} \\ \hline  & **Mean** & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & **Mean** & **SD** & **Mean** & **SD** \\ \hline
1 & 0 & 0 & 0 & 8.62 & 0.83 & 220.43 & 21.8 \\ \hline
2 & 0 & 0 & 0 & 8.2 & 0.82 & 217.58 & 21.95 \\ \hline
3 & 0 & 0 & 0 & 8.18 & 0.82 & 221.02 & 21.54 \\ \hline
4 & 0 & 0 & 0  
The proposed EVs charging station load model and scenario generation and reduction process are simulated using MATLAB software while the proposed optimization model is solved using CPLEX under GAMS [57]. The computing time for calculating the MCS-based model is 75 s. Furthermore, the scenarios generation process takes 45 s and finally the optimization problem is successfully solved in 105 s.
# Word Sense Disambiguation Using Clustered Sense Labels
## 4 Experiment and Result
### Experiment Result
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Corpus** & **Sentences** & **Tokens** & **Annotations** & **Sense** & **Word** & **Ambiguity** \\  & & & & & **Types** & **Types** & **Types** & \\ \hline \multirow{5}{*}{Eng} & SemCor (train) & 37,176 & 802,443 & 226,036 & 33,316 & 20,399 & 8.9 \\  & Senseval2 (test) & 242 & 5766 & 2282 & 1271 & 1057 & 7.2 \\  & Senseval3 (test) & 352 & 5541 & 1850 & 1144 & 948 & 9.3 \\  & Semeval07 (test) & 135 & 3201 & 455 & 371 & 327 & 10.9 \\  & Semeval13 (test) & 306 & 8391 & 1644 & 822 & 751 & 6.9 \\  & Semeval15 (test) & 138 & 2604 & 1022 & 545 & 496 & 7.6 \\ \hline \multirow{5}{*}{Kor} & Small (train) & 37,940 & 881,549 & 146,767 & 9393 & 7684 & 5.3 \\  & Small (test) & 1060 & 24,533 & 4108 & 1671 & 1587 & 5.2 \\ \cline{2-8}  & Medium (train) & 75,880 & 1,765,792 & 294,045 & 11,805 & 9249 & 5.3 \\ \cline{2-8}  & Medium (test) & 2120 & 47,337 & 7816 & 2489 & 2305 & 5.3 \\ \cline{2-8}  & Large (train) & 113,820 & 2,649,071 & 440,157 & 13,299 & 10,126 & 5.3 \\ \cline{2-8}  & Large (test) & 3180 & 72,821 & 12,060 & 3163 & 2901 & 5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of annotations in all-words WSD datasets (Kor uses Sejong sense annotated corpus).
## 4 Results
### Reduction of Power Losses in Peak Hours  
Taking into consideration that the specialized literature reports the power losses for the peak hour, hour 19, Tables 3 and 4 present the results for the study cases informed by the different methods, including the proposed DCVSA method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hour** & **Demand (p.u)** & **PV System Generation (p.u)** & **Hour** & **Demand (p.u)** & **PV System Generation (p.u)** \\ \hline
1 & 0.4240 & 0 & 13 & 0.8013 & 0.926 \\
2 & 0.4108 & 0 & 14 & 0.7899 & 0.851 \\
3 & 0.3999 & 0 & 15 & 0.7774 & 0.521 \\
4 & 0.4083 & 0 & 16 & 0.7774 & 0.255 \\
5 & 0.4744 & 0 & 17 & 0.8022 & 0.035 \\
6 & 0.5301 & 0 & 18 & 0.8926 & 0.028 \\
7 & 0.5669 & 0 & 19 & 1 & 0.015 \\
8 & 0.6326 & 0.0490 & 20 & 0.9682 & 0 \\
9 & 0.7202 & 0.2490 & 21 & 0.8890 & 0 \\
10 & 0.7805 & 0.300 & 22 & 0.7832 & 0 \\
11 & 0.8268 & 0.683 & 23 & 0.6175 & 0 \\
12 & 0.8369 & 0.835 & 24 & 0.5212 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic data of demand and generation of a PV system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **\(\mathbf{P_{loss}}\) (kW)** & **Nodes Localization** & **Sizing (MW)** \\ \hline MOHTLBOGWO & 72.1100 & \{13, 24, 30\} & \{0.9960, 0.9380, 0.8520\} \\ CBGA-VSA & 72.7853 & \{13, 24, 30\} & \{0.8018, 1.0913, 1.0536\} \\ DSCA-SOCP & 72.7853 & \{13, 24, 30\} & \{0.8018, 1.0913, 1.0536\} \\ MSSA & 72.7854 & \{13, 24, 30\} & \{0.8010, 1.0910, 1.0530\} \\ MINLP & 72.7862 & \{13, 24, 30\} & \{0.8000, 1.0900, 1.0500\} \\ GAMS & 72.7900 & \{14, 24, 30\} & \{0.7709, 1.0969, 1.0658\} \\ HSA-PABC & 72.8129 & \{14, 24, 30\} & \{0.7550, 1.0730, 1.0680\} \\ AHA & 72.8340 & \{13, 24, 30\} & \{0.7920, 1.0680, 1.0270\} \\ QOTLBO & 74.1008 & \{12, 24, 29\} & \{0.8808, 1.0592, 1.0714\} \\ KHA & 75.4116 & \{13, 25, 30\} & \{0.8107, 0.8368, 0.8410\} \\ REPSO & 76.9100 & \{06, 14, 31\} & \{1.2274, 0.6068, 0.6870\} \\ CHVSA & 78.4534 & \{06, 14, 31\} & \{1.1846, 0.6468, 0.6881\} \\ LSFSA & 82.0300 & \{06, 18, 30\} & \{1.1124, 0.4874, 0.8679\} \\ PBIL-PSO & 91.5000 & \{12, 15, 31\} & \{0.4035, 0.5245, 0.6422\} \\ PMC & 91.6000 & \{12, 18, 31\} & \{0.4993, 0.3966, 0.6744\} \\ TLBO & 104.000 & \{09, 18, 31\} & \{0.8847, 0.8953, 1.1958\} \\ SOS & 104.190 & \{06, 28, 29\} & \{2.2066, 0.2000, 0.7167\} \\ PSO & 105.350 & \{08, 13, 32\} & \{1.1768, 0.9816, 0.8297\} \\ GA & 106.300 & \{11, 29, 30\} & \{1.5000, 0.4228, 1.0714\} \\ GA-IWD & 110.510 & \{11, 16, 32\} & \{1.2214, 0.6833, 1.2135\} \\ HSA & 135.690 & \{16, 17, 18\} & \{0.5927, 0.2133, 0.1913\} \\ \hline DCVSA & 72.7853 & \{13, 24, 30\} & \{0.8018, 1.0913, 1.0536\} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Location and sizing of PV systems in the 33-node system.
## 2 Materials and Methods  
### WRF-Pretreated Corn Straw Preparation  
Corn straw at late ripening stage with 89.2% dry matter (DM) content was harvested in Songyuan City, Jilin province, China and chopped into 2-3 cm lengths. Then, the substrate containing 75% corn straw, 5% soybean, 20% wheat bran, 0.5% gypsum powder, 1% lime powder and 0.5% sugar (DM basis) was prepared for WRF fermentation. The substrate was mixed with water at a ratio of 5 to 3 (weight basis) and every 550 g mixed substrate was compacted into an autoclavable bag (18 \(\times\) 36 cm) by hand. All bags were autoclaved using a high-pressure steam sterilization pot (YXQ-100 SII, Shanghai, China) at 121 \({}^{\circ}\)C for 2 h to kill bacteria and germinate the spores. Then, after cooling on a super clean bench (SW-C)-2 FD, Suzhou, China), sterile bags were stored in an air-conditioned chamber at 25\({}^{\circ}\)C until inoculated. The spawn of WRF (_Lentiinus codes_, strain WX1) used in the present study were purchased from Shouguang Institute of Edible Fungi (Shandong, China). Cultivation procedures were followed as described in our previous study [14]. Briefly, 8.0 g of liquid spawn was asetically placed into each bag and mixed to distribute the spawn equally over substrate. Then, all bags were fermented at 25 \({}^{\circ}\)C. About 1500 bags with cultivated corn straw for the feeding experiment were pretreated for 28 days for feeding. The control corn straw was not pretreated with sterilization and cultivation. Differences in metabolite profiles (Figure 1) between control and WRF-pretreated corn straw were measured as described by [PERSON] et al. [15] using liquid chromatography-mass spectrometry (LC-MS) ([PERSON], LC-MS2010, Beijing, China). Briefly, 500 mg of each sample was collected within 5 min and immediately transferred into liquid nitrogen for at least 15 min of frozen and then stored at -80 \({}^{\circ}\)C until analysis. Six replicates were determined for each sample.  
Figure 1: Up-or downregulated categories and relative abundance of metabolites between untreated (CON) and fungal-pretreated (WRF) corn straw. (**a**) Relative abundance of individual amino acids in CON and WRF corn straw; (**b**) relative abundance of dipeptides in CON and WRF corn straw; (**c**) Categories and the relative abundance of metabolites in CON and WRF corn straw. CON—untreated corn straw-based diet as control; WRF—replacing 40% untreated corn straw with 40% white-rot fungi-pretreated corn straw (DM basis); down—downregulated; up—upregulated.
# Evaluation of Different Work Zone Road-Occupation Schemes for Monorail Construction
## 3 Problem Statement and Data Collection
### Data Collection  
In VISSIM (2022 student), the calibration and microscopic simulation of models typically necessitate actual traffic data measurements. (Figure 2--actual diagram of the intersection). According to Auto Navi Traffic statistics, the peak traffic congestion on Zhangba Fourth Road in Xi'an is between 7:30 and 8:30, as well as between 17:30 and 18:30. Therefore, on-site data collection is conducted during peak and undervalued traffic flow times. Throughout the data collection period, favorable weather and road conditions prevailed, with no reported traffic accidents (Table 1).  
The traffic volume collection method in this article employs an unmanned aerial vehicle (UAV), which statistically classifies vehicles passing through intersections. The collected data includes the following:  
1. Traffic volume in all directions at the intersection of Zhangba Fourth Road and Jinye Second Road.
2. The proportion and the type of left and right turns at each entrance.
3. The geometric shape and width of the occupied road during intersection construction.  
After data processing, the peak traffic volume hours at the intersection is 3420 veh/h, and the collected traffic data has the following characteristics:  
Figure 1: The investigated intersection location scheme.  
Figure 2: Monorail work zone.  
There is a certain disparity in traffic volume between the two main roads at signalized intersections, with traffic flow in the east-west direction significantly greater than that in the north-south direction.  
The left-turn rate of traffic flow at the east entrance of the signalized intersection is 10%, and the left-turn rate in other directions is 18%.  
Due to construction occupation, the east entrance occupies a nonmotorized lane, and the traffic volume of this lane is basically the same as the normal two-lane traffic volume.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 4 Proposed Deep-Learning Model
### Data Generation  
In this paper, the OFDM with 64 subcarriers is considered. Each OFDM packet consists of two pilots and one data symbol. The quadrature phase shift-keying (QPSK) modulation is considered, and each symbol consists of 2 bits per subcarrier. After the IDFT is performed and CP data is added as a guard interval to avoid inter-symbol interference, the OFDM packet is transmitted through the Rayleigh channel. The BS receives the sum of the OFDM packet from the multiuser with noise.  
The received OFDM packet is stored as the training data sample by creating a feature vector \(y_{u}\). The feature vector \(y_{u}\) is constructed with the real and imaginary values of all the symbols in the OFDM packet. The total training sample is comprised of the multiplication of the number of total data packets and the number of labels. The model can be trained to restore data on an arbitrary subcarrier \(f\) by using the corresponding \(B(f)\) in the training. The system has a total number of \(2^{4}\) combinations or labels for UEs transmitting QPSK symbols. The total label can be expressed as \(B(f)=1,2,3,4,\ldots,N_{l}\) for \(N_{l}=16\). As the 64 subcarriers are considered, one OFDM packet contains 3 OFDM symbols and two active UEs. The input size of the training model is \(64\times 3\times 2=384\). In total, 50,000 data packets are used and the total data samples are comprised of 50,000 \(\times\) 16 = 800,000 which are generated to train the model. The total generated data sample is split into 2 sizes, such as train data size and validation data size, to justify the efficiency of the model. The sizes of training and validation data samples are \((4/5)\), i.e, 640,000 and \((1/5)\), i.e, 160,000, respectively.
## 3 Fundamentals of GLI-Split
### Communication between Upgraded GLI-Nodes
#### 3.4.2 Communication between GLI-Domains  
GLI-node 1 communicates with GLI-node 3 in a different GLI-domain (see Figure 1). When node 1 queries the local mapping system for local GLI-addresses of ID 3, it receives a negative answer. Then, node 1 queries the global mapping system for a global GLI-address of ID 3. Alternatively, the local mapping system can forward the request to the global mapping system, which returns the global GLI-addresses so that GLI-node 1 needs to issue only a single query. Node 1 uses its own local GLI-address as source address and one of the returned global GLI-addresses of ID 3 as destination address for communication with node 3.  
Figure 4 shows how source and destination address fields of IP packets change on the path between GLI-nodes 1 and 3. Depending on the configuration of the local routing system, packets are forwarded either to a default GLI-gateway or to a specific GLI-gateway. We assume that packets are routed to the gateway with global locator E. When a GLI-gateway receives a packet destined to an outbound global  
address, it substitutes the local source address with the global source address reflecting its own global locator. Then, the packet contains globally routable source and destination addresses. It can be carried over the standard IPv6-Internet backbone towards the GLI-gateway whose global locator is reflected in the packet's global destination address. The GLI-gateway in the destination GLI-domain queries its local mapping system for a local GLI-address of ID 3 and substitutes the global destination GLI-address in the packet by a local destination GLI-address. Based on the local destination GLI-address, the packet is eventually delivered to GLI-node 3.  
When GLI-node 3 sends a response back to node 1, it also queries the mapping system to obtain a global locator of ID 1. When GLI-domains are multihomed, different GLI-gateways may be chosen. As a result, different global GLI-addresses may be used in the two directions of a single communication session (see Figure 4). This example demonstrates that GLI-Split is by design able to utilize different paths for domains with several gateways. However, if access control or filtering devices like firewalls require that packets enter and leave the GLI-domain through the same GLI-gateway, this can be supported by the mechanisms described in Sections 4.2.1 and 4.2.2.
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 4 Enrichment of Low-Grade Pyrophyllite Ore
### Flotation  
The main objective of enrichment by flotation is to recover pyrophyllite from the associated minerals, thus increasing the Al\({}_{2}\)O\({}_{3}\) content and decreasing the SiO\({}_{2}\) content [98; 99]. Several studies have found that pyrophyllite responds to cationic collectors because of its lower aluminum: silica ratio, and its crystal structure contains the greatest number of cleavable planes. The most common flotation-based cationic collector for pyrophyllite is dodecylamine due to its satisfactory ability to collect aluminosilicate minerals at specific pH ranges [10; 100; 101]. The flotation of pyrophyllite was studied using dodecylamine and found to recover 96% of pyrophyllite. In a previous study, flotation was applied to pyrophyllite. In this study, the main associated minerals were quartz and feldspars (Table 6), and the collector was dodecylamine [101]. The study concluded that the Al\({}_{2}\)O\({}_{3}\) content increased from 26.2 to 29.5%, the SiO\({}_{2}\) content decreased from 63.62% to 55.71%, and the product's brightness was enhanced [10]. Flotation of the pyrophyllite was also carried out using 3-diaminopropane and N-dodecyl-1, with a recovery rate higher than 80%. There were also attempts to use an anionic collector for the flotation of pyrophyllite ore containing quartz as a gangue mineral. It was found that using sodium oleate (SO) as a collector can improve the flotation efficiency of pyrophyllite [38].  
It should be highlighted that the hydrophobic surfaces of pyrophyllite serve as natural adsorption sites for non-polar organic molecules. As a result of its hydrophobicity, pyrophyllite can be easily separated from quartz and feldspar using collectorless flotation and a single type of frother. Based on this phenomenon, a study was conducted using a collectorless froth flotation to enrich pyrophyllite using Methyl Isobutyl Carbinol (MIBC) as a frother reagent. The associated minerals were quartz and kaolinite (Table 6). The study concluded that using collectorless flotation to upgrade the pyrophyllite with different doses of MIBC produced an increase in Al\({}_{2}\)O\({}_{3}\) content between 25% and 27% and decreased the SiO\({}_{2}\) content from 73.41% to 65.56% [94]. The XRD analysis in Figure 2 shows that the major crystalline phase was pyrophyllite, which increased via flotation with MIBC compared to raw pyrophyllite in which quartz was a major phase.  
Figure 2: Comparison of the XRD patterns of raw pyrophyllite and the selected pyrophyllite concentrate obtained via collectorless froth flotation. P: pyrophyllite; Q: quartz; K: kaolinite [94].  
In other cases, the flotation of pyrite from pyrophyllite was studied using N-dodecyl mercaptan as a collector along with a novel depressant (i.e., glucan) to achieve the selective flotation of pyrite and depress the pyrophyllite minerals. The studies demonstrated that a high recovery of Fe up to 98.51% could be obtained [98; 102].