esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sampling and Methodology
### NMR Methodologies
#### 3.3.3 Pore Components Derived from NMR T\({}_{2}\) Spectra  
The pore components (\(S_{1}\), \(S_{2}\), and \(S_{3}\)) can also be obtained by NMR \(T_{2}\) spectral analysis, representing various pore volumes corresponding to micropores, mesopores, and macropores, respectively [42]. The pore components are critical for evaluating micropore structures. The larger the \(S_{2}\) and \(S_{3}\), the better the micropore structure would be. \(S_{1}\), \(S_{2}\), and \(S_{3}\) can be calculated by Equation (8).  
\[S1=\sum_{i=1}^{n_{1}}T_{2i}\varphi_{i} \tag{8a}\] \[S2=\sum_{i=n_{1}}^{n_{2}}T_{2i}\varphi_{i}\] (8b) \[S3=\sum_{i=n_{2}}^{N}T_{2i}\varphi_{i} \tag{8c}\]  
where \(n_{1}\) and \(n_{2}\) are the category boundaries of pore components.  
NMR-derived pore components have strong regional differences and need to be calibrated by MICP data. As shown in Figure A3, the pore types of the E\({}_{84}\) sandstone reservoirs can be classified into micropores, mesopores, and macropores by using the category boundaries of 0.1 \(\upmu\)m and 1.0 \(\upmu\)m. The category boundaries were selected based on pore throat size distribution of the E\({}_{84}\) reservoirs and regional empirical values proposed by [PERSON] et al. (2019) [39]. The corresponding category boundaries of \(S_{1}\)-\(S_{2}\) and \(S_{2}\)-\(S_{3}\) are approximately 27.4 ms and 289.9 ms, respectively (Figure A3).
# Floor Heave Mechanism and Anti-Slide Piles Control Technology in Deep and Large-Span Chamber
## 3 Model Test Based on Digital Speckle Correlation Method
### Test Scheme  
The self-developed chamber floor deformation measurement system consists of a simulation test device, loading system, and two-dimensional digital speckle correlation measurement system. The schematic diagram is shown in Figure 2. The two-dimensional digital speckle correlation measurement system is composed of a Charge Coupled Device (CCD) camera, camera fixator, image acquisition card, Light Emitting Diode (LED) white light source, computer, etc. The high-resolution camera in collaboration with sub-pixel processing technology enable micron-level measurement accuracy of the system, better than 0.02 pixels. Compared with traditional measurement systems, its accuracy can be improved 100 times. The loading process was carried out on a TAW-2000 microcomputer-controlled electro-hydraulic servo rock triaxial experimental machine. Before loading, artificial speckle treatment should be carried out on the test model's surface. In order to avoid the influence of material transparency on the shooting effect of a CCD camera, a layer of black paint should be sprayed on the surface of the model before the artificial speckle is created. After speckling, the test box (305 \(\times\) 150 \(\times\) 300 mm) holding floor model was placed into the triaxial experimental machine. In order to capture the whole process from the stable state to the instability failure of the model, the displacement loading mode was adopted and the loading rate was set to 2 mm/min. The continuous digital images obtained in the loading process were analyzed by two-dimensional Digital Image Correlation Processing Software (DIC Processing Software). The DIC Processing Software processed the speckle images before and after deformation with grey processing and an analog-to-digital conversion operation; then, the displacement field, strain field, and displacement vector field of the floor model at different times could be obtained.  
Figure 2: Sketch map of the deformation test device.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 4 Energy-Efficient Suboptimal Resource Allocation Algorithm  
The complexity of the near-optimal algorithm proposed in Section 3 becomes high when the value of \(K\) is large. Therefore, a low-complexity suboptimal algorithm with two steps is investigated in this section, whose EE and SE performance is near that of the near-optimal one. This suboptimal algorithm is elaborated:  
1. Intuitively, a higher power value is allocated to a subcarrier pair with better channel quality from the source to the destination nodes. Hence, the subcarriers of the SR and RD links with a higher end-to-end channel gain are paired together with a higher priority. Firstly, the SR subcarriers are organized in a descending order according to their channel gains, and the RD subcarriers are also ordered in similar way. Then, the corresponding subcarriers are paired with each other in sequence. We set \(\rho_{i,j}=1\), if the \(i\)th subcarrier of the SR link is paired with the \(j\)th subcarrier of the RD link; otherwise, \(\rho_{i,j}=0\).
2. The next step is to get the optimal power allocation \(\left(\mathbf{P}_{S},\mathbf{P}_{R}\right)\) for maximizing EE by solving (P.3) for the obtained subcarrier pairing \(\rho_{i,j}\) in the step 1.
# Multi-Wavelength Path Loss Model for Indoor VLC with Mobile Human Blockage
## 4 Blockage Probability Analysis and Modeling
### Blockage Probability Model in Single-LED Scenario  
Based on the human moving trajectory generating algorithm and the human body shadowing calculation method, the BP can be obtained by calculating the percentage of time that the receiving position is in the shadowing area. The BP in the single-LED scenario is depicted in Figure 9.  
It is founded that, on the whole, the BP increases along with the increasing transceiver distance \(d\). For example, in the diagonal direction of the room, the BP is 0.016 at \(d=3\) m and increases to 0.066 at \(d=4.22\) m with a difference of 6.15 dB. And in the positive \(X\)-axis direction, the BP is 0.016 at \(d=3\) m and increases to 0.037 at \(d=3.9\) m which has a difference of 3.64 dB. This is mainly because the human body shadowing is always toward the outward direction of the human body with respect to the transmitter, thus the receiving position with a larger transceiver distance will have a greater probability to be blocked.  
Furthermore, the BP of the receiving positions with the same transceiver distance is not exactly the same and has a considerable fluctuation. For example, for the receiving positions where the transceiver distance is 4.6 m, the BP fluctuates in the range from 0.02 to 0.06. What's more, there are some specific directions where the lower bound of the fluctuated BP appears. It can be observed that the BP is generally lower in the direction of the symmetry axis of the room, i.e., the \(X\)-axis and the \(Y\)-axis. For instance, for the receiving positions at a transceiver distance of 3.84 m, the BP in the diagonal direction is 0.063. While for the receiving positions at the same transceiver distance in the \(X\)-axis direction, the BP decreases to 0.010 which is 7.99 dB lower than that in the diagonal direction. It is obvious that the distribution of BP has a tight connection with the relative position in the environment.  
Based on the analysis, it is found that the BP in the single-LED scenario is related to three parameters: the transceiver distance, the distance to the \(X\)-axis, and the distance to the \(Y\)-axis. Also, the transceiver distance can be expressed by the two other parameters as  
\[d=\sqrt{d_{X}^{2}+d_{Y}^{2}-2 lz^{2}}, \tag{29}\]  
where \(d_{X}\) and \(d_{Y}\) denote the three-dimensional distance between the receiving position and the \(X\)-axis and the \(Y\)-axis, respectively. \(lz\) is the fixed vertical distance between the transceiver. Therefore, the BP can be modeled as a two-dimensional function related to \(d_{X}\) and \(d_{Y}\):  
\[p_{single}=f(d_{X},d_{Y}), \tag{30}\]  
where  
\[\begin{split} f(d_{X},d_{Y})=& a_{2}+b_{2}\sqrt{d_{X}^{2}- lz^{2}}+c_{2}\sqrt{d_{Y}^{2}-lz^{2}}\\ &+d_{2}\sqrt{d_{X}^{2}+d_{Y}^{2}-2 lz^{2}},\end{split} \tag{31}\]  
The derived parameters of the mean value are \(a_{2}=0.035\), \(b_{2}=0.040\), \(c_{2}=0.029\), \(d_{2}~{}=~{}-0.039\). The RMSE is 0.006 indicating that our proposed model has the goodness of fit to the simulated data.
## 2 A Proposed ASPC Approach for OCRs in a DG-Integrated Distribution Network
### Coordination Constraints of OCRs  
#### 2.3.1 Coordination Time Interval  
According to IEEE 242-2001 standards, a CTI between primary and backup OCRs is usually selected in a range of 0.2 s to 0.5 s [57; 58]. The CTI considers main factors such as the operating time of CBs, current transformer (CT) errors, the signal sending time of OCRs, and a safety margin [75]. If the primary relay fails to clear a fault given at the location \(k\), then the backup relay will be activated to send the tripping signals to the related CBs after a certain CTI plus the failed operating time of the primary relay. The operating time \(T_{F51\_jk}\) of the \(j\)-th backup relay (\(R_{j}\)) for a \(k\) faulted location in the network is expressed by Equation (5).  
\[T_{F51\_jk}-T_{F51\_jk}\geq\text{CTI} \tag{5}\]  
where \(T_{F51\_jk}\) is calculated by Equation (6) as follows.  
\[T_{F51\_jk}=TDS_{j}\Bigg{[}\frac{A}{\left(I_{f\_jk}/I_{pu\_j}\right)^{B}-1}+C \Bigg{]} \tag{6}\]  
where \(TDS_{j}\) and \(I_{pu\_j}\) are the time-dial setting and pick-up current parameters of the \(j\)-th back-up relay, respectively; and \(I_{f\_jk}\) is the fault current seen by the \(j\)-th back-up relay (\(R_{j}\)) regarding a \(k\) fault location in the distribution network. It is noted that all OC relays on a feeder of distribution network will have the same pick-up current threshold \(I_{pu}\). The selection of pick-up current threshold for the OC relays is specifically presented in Section 2.3.4.
# Evaluating the Quality of Raw Chocolate as an Alternative to Commercial Products
## 3 Results and Discussion
### Chemical Composition and Visual Assessment
The results obtained in the range of the contents of polyphenols, flavonoids, and antioxidant activity supported the correlation between the content of cocoa mass and the oxidative capacity of the products. Roasting cocoa beans generally results in a lowering of antioxidant activity. [PERSON] et al. [50] noted a decrease in the antioxidant activity of cocoa ranging between 37 and 48% after preliminary roasting at 100 \({}^{\circ}\)C and proper roasting of various cocoa cultivars at 130 \({}^{\circ}\)C. In addition, [PERSON] et al. [51] observed a decrease in the antioxidant activity of cocoa, ranging between 44 and 50%, during roasting at a high temperature (190 \({}^{\circ}\)C) for a short time (15 min). With regard to the commercial production of chocolate, one should take into account that the numerous parameters of the complex technological process affect the nutritional as well as the health-promoting values of the obtained product [18]. Cocoa beans are the primary raw material for the production of chocolate and the most important source of phenolic compounds [52]. In the technological process, which is characterised by the use of high temperatures, a majority of polyphenols undergo degradation, which can be observed in the form of lower levels of those health-promoting components relative to the unprocessed raw material. It should be emphasised that certain polyphenols, e.g., (-)-epicatechin, can form highly complex, insoluble complexes that are very hard to determine analytically. Such complex procyanidins, under the effect of high temperatures, may undergo degradation into easily extractable monomeric forms, which affect the total phenolic content in the product [18]. The roasting of cocoa beans results in the appearance of the characteristic colour, flavour, taste, and texture of roasted cocoa beans [53], which hasan effect on the sensory parameters of the final product, i.e., chocolate. As a result of the application of high temperatures during roasting, in the range of 120-150 \({}^{\circ}\)Cover 5-120 min, a reduction is observed in the total phenolic content and the total flavonoid content [53; 54]. Chocolate processing has an effect on the total phenolic content and also on the antioxidant activity of chocolate, and suitable technology can \"optimise\" the retention of polyphenols and the antioxidant activity of chocolate [55]. [PERSON] et al. [56] indicated that, in the case of the total content of polyphenols in chocolate, the conching process did not contribute to a significant degradation of these compounds, unlike the roasting process. The progressive decrease in the content of polyphenols during the production of chocolate was also described by [PERSON] et al. [57]. These authors indicated that roasting played the most important role in this case, although differences in the concentration of polyphenols can also be observed between conched and un-tempered chocolate, as well as finished dark chocolate.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 5 Performance Evaluation
### Determining Operational Bounds
If users are allocated a resource block (RB) group each with 750 kHz bandwidth for instance and the area of a cell pointing at the 60 km distance using (25) is evaluated to be approximately 125 km\({}^{2}\), the best case achievable capacity of a user in this cell is approximately 4.6 Mbps. Practically, signals at cell edges will be considerably degraded due to ICI. System designers can therefore workout how wide their HAP service area can cover based on the number of cells to be deployed and the required capacity of the edge-of-coverage users. The average ASE values obtained from evaluating (38), as shown in Figure 11, are close to the values obtained when all users in a cell have a signal gain that is equal to the boresight gain. The closeness is expected, as there is a small difference between the boresight gain and the gain at the edge of cell due to small angle subtended (\(\rho=3.5^{\circ}\)) by the cell centre and edge at the HAP.
## 2 Materials and Methods
### Toxicity Characteristics Leading Procedure (TCLP)  
The leaching of arsenic from contaminated and stabilized soils was conducted using the USEPA Method 1311 for TCLP [29]. In this experiment, two types of extraction fluids (labelled extraction fluid #1 and #2) were prepared based on the TCLP method for the stabilization material. Briefly, extraction fluid #1 (for the lower pH materials of AMDS, steel slag, and CMDS stabilized soil, and a control) was prepared by adding 5.7 mL of glacial acetic acid (Daejung, Siheung-Si, Korea) to a 1000 mL volumetric flask containing 500 mL of ultrapure water (Human Corporation, Seoul, Korea). Then 64.3 mL of 1 N NaOH (Samchun Chemical, Pyungtaek-Si, Korea) was added to the flask. Ultrapure water was added until the total volume reached 1 L, resulting in an extraction fluid pH of 4.93 \(\pm\) 0.05. If the pH of the resulting extraction fluid was not 4.93 \(\pm\) 0.05, the fluid was adjusted using dilute acid or alkali [30]. Similarly, extraction fluid #2 (for the higher pH material of the  
Figure 1: Schematic diagram of the experimental set-up for (**A**) percolation column test and (**B**) toxicity characteristic leaching procedure (TCLP) tumbling apparatus.  
cement-stabilized soil) was prepared by adding 5.7 mL of glacial acetic acid to a 1000 mL volumetric flask containing ultrapure water until the total volume reached 1 L. The pH was adjusted using dilute acid or alkali when required to ensure the fluid maintained a pH of 2.88 \(\pm\) 0.05.  
The TCLP test involved the addition of 20 g soil/stabilized-soil to different PTFE extraction bottles and 400 mL of the respective extraction fluids (at a solid-fluid ratio of 1:20). Blank samples were also prepared using both TCLP extraction fluids (extraction fluid #1 and #2) in different bottles. The bottles were tightly capped and fitted into a rotatory agitation apparatus (YKZ-08-2, Changsha Yonglekang Equipment Co., Ltd., Changsha, China). The bottles were vertically rotated (end over end) at a constant speed of 30 rpm for 18 \(\pm\) 2 h. All experiments were performed in triplicate. A schematic diagram of the experimental setup of the TCLP test is provided in Figure 1B (see Figure S1B for column setup). After completion of the extraction period, the resulting mixture was filtered using a 0.7 \(\upmu\)m glass microfiber filter (GF/F, Whatman, Hangzhou, China). All the experiments were conducted at ambient room temperature (approximately 25 \({}^{\circ}\)C). The pH, EC, and Eh of the TCLP leachate were measured immediately after filtration. The eluates were preserved by adding nitric acid and stored in a refrigerator (at 4 \({}^{\circ}\)C) until required for the arsenic analysis.
## 2 Materials and Methods
### Adsorption Kinetics Studies  
The adsorption mechanism of TC was investigated by evaluating the kinetic parameters over a range of contact times. For these experiments, 0.3 g of adsorbent was added to 25 mL of a 500 mg\(\cdot\)L\({}^{-1}\) TC solution. The tests were conducted at 25 \({}^{\circ}\)C (room temperature) under constant stirring at 150 rpm, with sampling intervals ranging from 1 to 1440 min (11 data points). Residual TC concentrations were quantified using UV-Vis spectrophotometry.  
The adsorption capacity and kinetic behavior were analyzed by applying multiple kinetic models, including the pseudo-first-order, pseudo-second-order, and Elovich models, as described by Equations (1)-(3), respectively. Data fitting and model comparisons were performed using Excel and Origin 2019b software to identify the most suitable kinetic mechanism governing the adsorption process.  
\[ln\ (q_{e}-q_{t})=ln\ q_{e}-k_{1} \tag{1}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{2}\]  
\[q_{t}=\frac{ln\ (\alpha\beta)}{\beta}+\frac{1}{\beta}\ ln\ t \tag{3}\]  
In the kinetic models applied to analyze the adsorption process, \(q_{t}\) represents the amount of tetracycline hydrochloride adsorbed per gram of adsorbent at time \(t\), while \(q_{e}\) denotes the adsorption capacity of the material at equilibrium, both expressed in mg\(\cdot\)g\({}^{-1}\). The pseudo-first-order rate constant, \(k_{1}\) (L-min\({}^{-1}\)), characterizes the rate of adsorption as proportional to the difference between \(q_{e}\) and \(q_{t}\). Similarly, the pseudo-second-order rate constant, \(k_{2}\) (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)), reflects the chemisorption kinetics, which are often associated with valence forces or electron exchange. The parameter \(\beta\) (g\(\cdot\)mg\({}^{-1}\)) is related to the activation energy for chemical adsorption and provides insights into the extent of surface coverage. Additionally, \(\alpha\) (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1}\)) represents the initial adsorption rate and describes the adsorption behavior as \(t\) = 0. These parameters, derived from fitting experimental data to the kinetic models, provide detailed insights into the adsorption dynamics and the mechanisms governing the process.  
The Mean Squared Error (_MSE_) was used to evaluate the accuracy of the model in estimating the TC concentration after the adsorption process. The _MSE_ is a widely used metric to quantify the difference between experimental and predicted values, calculated using Equation (4):  
\[MSE=\frac{1}{n}\ \sum_{i=1}^{n}\Bigl{(}C_{exp,i}-C_{pred,i}\Bigr{)}^{2} \tag{4}\]  
where \(C_{exp,i}\) represents the experimental drug concentration at point \(i\), \(C_{pred,i}\) is the concentration predicted by the model, and \(n\) is the total number of observations. Lower _MSE_ values indicate greater accuracy of the model in fitting the data.
## 2 Methods
### Degradation Models
#### 2.3.2 Speech Coding  
To simulate the effects of different low bit rate speech codecs, three speech coding schemes found in our real data and also commonly found in the call recording industry have been chosen: GSM-FR [33] and MP3 with bit rates of 8 and 16 Kbps [34].  
* MP3 is a lossy audio codec designed originally to compress the audio channel of videos. It is therefore not specifically designed for speech, but it is a very popular standard in Internet audio applications and streaming because it achieves large compression rates, which can even be adjusted to the particular needs of the application. This makes it very convenient for audio transmission and storage. In this paper, MP3 with 16 Kbit/s and 8 Kbit/s bit rates have been applied using the standard FFmpeg tool ([[https://ffmpeg.org/](https://ffmpeg.org/)]([https://ffmpeg.org/](https://ffmpeg.org/))).
* Full rate GSM 06.10 is a digital speech codec used in digital mobile telephony. The bit rate is 13 Kbit/s with an 8 KHz of sampling rate. The codec works with packets of 160 samples (20 ms). We have applied the full rate GSM coded available in the SoX tool ([[http://sox.sourceforge.net/](http://sox.sourceforge.net/)]([http://sox.sourceforge.net/](http://sox.sourceforge.net/))).
## 2 Materials and Methods
### SMOC Ocean Surface Current Data  
The SMOC product integrates the three primary components of ocean surface currents: large-scale ocean circulation, wave-induced Stokes drift, and tidal currents. These components are derived from three independent modeling systems: (1) the global high-resolution (\(1/12^{\circ}\)) real-time forecasting system provided by the Copernicus Marine Environment Monitoring Service (CMEMS) [35; 36]; (2) the CMEMS global wave forecasting system, with a \(1/10^{\circ}\) resolution, used for estimating the Stokes drift [37]; and (3) the FES2014 tidal model, also with a resolution of \(1/12^{\circ}\), which provides estimates of the tidal currents [38]. The SMOC product provides hourly averaged ocean surface current data (filename: cmems_mod_glo_phy_anfc_merged-uv_PT1H-i) at a horizontal resolution of \(1/12^{\circ}\), and is publicly available through the Copernicus Marine Service (CMS). A validation against the drifter observations has demonstrated strong agreement, with a reported standard deviation of approximately 0.09 m/s [39].
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 3 Chemical Modifications: Guardians of tRNA Integrity and Function  
### RNA Modification on tRNA  
tRNAs contain the highest diversity and abundance of RNA modifications, with an average of 13 modifications per eukaryotic tRNA molecule, accounting for approximately one-fifth of its nucleotides [5] (Figure 2a). These modifications are catalyzed by specific modification enzymes, which could enhance tRNA stability and translation efficiency, maintaining overall cellular homeostasis, and play crucial roles in various biological processes [39; 40; 41]. Due to the great variety of tRNA modifications, a complete exposition is beyond the scope of this article. We highlight seven exemplary tRNA modifications that are prevalent and representative. It should be noted that some other tRNA modifications are also crucial, such as taurine methylation modifications (e.g., \(\tau\)m5U and \(\tau\)m5s2U) present in mitochondria, and highly correlated with mitochondrial diseases such as MELAS (mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes) [42] and MERRF (myoclonic epilepsy with ragged-red fibers) [43].
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 4 Outage Probability Analysis  
In this section, the outage performance of the proposed wireless network with ambient backscattering is analyzed. The outage performance of an OMAC link (i.e., uplink) per node and the NOBC link (i.e., downlink) per node is derived. Then, using an OMAC link outage, the end-to-end outage probability is derived.