esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Colorectal cancer is the third-most commonly diagnosed cancer worldwide [1]. Although surgery plays a key role in the diagnosis and treatment of colorectal cancer, there are still increasing attempts to stop the progression of this cancer via the application of new synthetic and naturally-occuring compounds [2, 3]. Bioactive compounds from plants have been screened for anticancer activities [4, 5]. Approximately 50-60% of cancer patients in the United States utilize complementary and alternative medicines with traditional therapeutic regimens, such as radiation therapy and chemotherapy [6].  
Apoptosis pathways are important targets in cancer-related therapies, and insufficient apoptosis results in uncontrolled cancer cell proliferation [7]. The use of natural phytochemicals for inhibiting cancer cell proliferation and inducing apoptosis contributes to promoting cancer cell death [8, 9]. Natural phytochemicals are multiple-target molecules found in plants and microorganisms, and they exert strong anticancer activity [10, 11]. Phytochemicals isolated from natural sources also exhibit various beneficial effects against inflammation, cancer, and neurodegenerative disorders [10]. Thisbroad spectrum of biological and pharmacological activities has made natural compounds suitable candidates for treating multifactorial diseases, such as colorectal cancer.  
_Salviae milliorrhizae_ Radix (SMR) is one of the well-known traditional herbal medicines and has been used in Asian countries [8]. Recently, there has been increasing scientific attention towards SMR for its remarkable bioactivity against cardiovascular disease, renal damage, tumor angiogenesis, and tumor cell invasion [12; 13; 14]. In the last decade, accumulating evidence has shown that SMR exerts a significant anticancer effect against promylocytic leukemia, breast cancer, ovarian carcinomas, and hepatocellular carcinoma (HCC) [15; 16; 17]. In a recent network pharmacology-based study on the anti-HCC effect of SMR, 62 chemical compounds form SMR yielded 101 putative targets that played a critical role in HCC via multiple targets and pathways, especially the EGFR and phosphatidyl-inositol 3-kinase (PI3K)/Akt signaling pathways [18]. However, the effect of SMR and its compounds on human colon cancer cells has not been fully elucidated. The aim of the present study was to simultaneously analyze the compounds of SMR and determine their cytotoxic effects on HCT-116 human colorectal cancer cells.
## 4 Numerical Assessment of the Strategy Using 3D and 2D Models  
Following the methodology proposed in Section 3, numerical assessments are performed using 3D and 2D models. In the beginning, beam properties and [PERSON]'s modulus updating procedure are detailed. Complimenting the 2D model made in MATLAB, a 3D model built with COMSOL is going to be used to be simulated as well as correlated using FSC minimization in order to build a damage library using **Step 1**. Cases with Single and multiple damage are tested to validate the bending stiffness based identification using **Step 2**. Furthermore, rectangular damage cases modeled with 3D FE are investigated; **Step 3** is used in order to distinguish the damage properties and size (width and depth) based on the obtained severity.
## 4 Blind Schnorr Signatures--Results and Discussion
### Unblinding During Signature Creation  
The presented attack can be slightly adjusted, so that during blind-signature creation, \(\mathcal{S}\) learns the message \(\mathsf{m}\). The only prerequisite is that the domain of potential messages \(\mathcal{M}\) is small enough to run a test for all messages \(\mathsf{m}\in\mathcal{M}\). Moreover, \(\mathcal{J}\) cannot inject hidden messages \(\mathsf{m}^{\prime}\), \(\mathsf{m}^{\prime\prime}\) anymore.  
For this attack, the parameters \(\alpha\), \(\beta\) are calculated by \(\mathcal{D}\) in a slightly simplified way (as before, \(\mathsf{PRNG}\) is assumed to return numbers from \(\mathbb{Z}_{q}\) and be indistinguishable from a true random number generator):  
1. \(\alpha=\mathsf{PRNG}(0,\mathsf{ak},R)\),
2. \(\beta=\mathsf{PRNG}(1,\mathsf{ak},R)\).  
The remaining steps of the blind-signature creation are unchanged. In particular, note that for the Auditor \(\mathcal{J}\), the numbers \(\alpha,\beta\) are not distinguishable from a pair of elements of \(\mathbb{Z}_{q}\) selected at random. Hence, \(\mathcal{J}\) cannot detect any deviation from the original protocol.  
Figure 8: Protocol for creating a linkable signature and unblinding it.  
Apart from the steps specified by the original protocol, \(\mathcal{S}\) can run the procedure shown in Figure 9. Its main idea is to recalculate \(\alpha,\beta\), then derive \(R^{\prime}\) and \(c^{\prime}\), and check the hash value for each message from \(\mathcal{M}\) until a match is found.  
Since \(\mathcal{S}\) can compute \(s^{\prime}\) as \(s^{\prime}=s+\alpha\) mod \(q\), the signature \((s^{\prime},c^{\prime})\) can be immediately attributed to \(\mathcal{D}\).  
Comment  
Note that if blind signatures are used for tasks like e-voting, then all possible plaintexts (the message space \(\mathcal{M}\)) are known. Indeed, they must not be modified in any way; otherwise, the decrypted ballot would serve as a witness about the vote cast. Moreover, the number of voting options in \(\mathcal{M}\) is small. So, \(\mathcal{S}\) can immediately verify the signature \((c^{\prime},s^{\prime})\) for each voting option and learn the vote cast with \(\mathcal{D}\). This would be a disaster from the point of view of an election process.
## 5 Results and Discussion
### Performance Units  
For security message distribution, our proposed protocol performs well by considering the following units: packet delivery ratio, throughput, packet loss ratio, average end-to-end delay, probability of successful delivery, reliability, safety message travel time, and packet inter-reception time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications** & **Values** \\ \hline Total nodes in our scheme & 5–40 \\ Speed of node’s & 10–40 m/s \\ Area covered while simulation & 10,000 \(\times\) 10,000 (m\({}^{2}\)) \\ Data rate & 6000 kbps. \\ Time taken for simulation & 2.5 min. \\ Total lanes in this scheme & Four \\ Scheme & Highway scenario \\ Transmission range & 0.3 km \\ Type of interface queue & DSRC/Queue \\ Length of interface queue & 50 \\ Network interface & WirelessPhyExt/Physical \\ Type of MAC interface used & 802. 11 Ext \\ Type of propagation framework & 2 ray ground \\ Size of message & 4096 Bits \\ Type of modulation used & Binary phase shift keying \\ Type of antenna & Omnidirectional antenna \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the simulation.
# Relationship between CO2 Fertilization Effects, and Stand Age, Stand Type, and Site Conditions
## 3 Methods
### Comparison of the Differences in \(\beta_{GPP}\) among Different Conditions  
We divided the site conditions into three levels based on SCI values in order to ensure a balanced number of pixels for different site levels. Therefore, an SCI value lower than 12 corresponds to the \"low-quality\" site condition, a between 12 and 14 correspondsto the \"medium-quality\" site condition, and a value greater than 14 corresponds to the \"high-quality\" site condition. In contrast to exploring the impact of stand age on the CO\({}_{2}\) fertilization effect, when exploring the influence of site conditions on the CO\({}_{2}\) fertilization effect, greater emphasis should be placed on long-term effects; therefore, we employed a \(\beta\) calculation approach distinct from that used to explore the effects of forest age. We calculated the annual \(\Delta GPP\) of each pixel at different site levels from 1982 to 2010, as well as its corresponding \(\Delta CO_{2}\), and performed a linear regression analysis (Equation (9)). The slope of the obtained regression model represents the overall \(\beta_{GPP}\) of each pixel from 1982 to 2010 (\(\beta_{1982-2010}\)). To obtain a more accurate estimate under varying site conditions, we utilized the two different remote sensing products and calculated the average of \(\beta_{GPP}\) to represent the CFE of each pixel. To reduce the effect of stand age, we formed groups covering 20-year intervals based on stand age in 1982 and used the average \(\beta_{GPP}\) of all pixels in the same age group as the final estimate.  
\[\Delta GPP=\frac{\beta_{1982-2010}}{100}\Delta CO_{2}+\varepsilon \tag{9}\]  
where \(\Delta GPP\) represents the difference between GPP values at S1 and S2, \(\beta_{1982-2010}\) represents the change in \(GPP\) for every 100 ppm increase in atmospheric CO\({}_{2}\) concentration from 1982 to 2010, \(\Delta CO_{2}\) represents the difference between the annual global CO\({}_{2}\) average and the 1982 global CO\({}_{2}\) average, and \(\varepsilon\) is the residual error term.
## 3 Results
### Definition and Discussion of the Coupling Coefficient  
In order to reduce the effects of short-term fluctuations, the monthly averaged data are employed to investigate the coupling relationships between the microwave scattering coefficient and _SST/SATD_. In this work, the linear response of the _SCA_ to the _SSTA_ or _SATDA_ is defined as a coupling coefficient (\(k\)), which is expressed in Equation (13):  
\[SCA=k\times W \tag{13}\]  
Here, \(W\) denotes the _SATDA_ or _SSTA_, while the _SCA_ refers to the scattering coefficient anomaly. Then, the coupling coefficient between the _SSTA_ and _SCA_, as well as the coupling coefficient between the _SATDA_ and _SCA_, can be expressed as follows:  
\[\begin{array}{l}k_{satda}=\frac{SCA}{SATDA}\\ k_{ssta}=\frac{SCA}{SSTA}\end{array} \tag{14}\]  
Additionally, the correlation coefficient (\(R\)) is commonly utilized to quantify the correlation between the _SCA_ and _SSTA/SATDA_. Its value ranges from \(-1\) to \(1\). And the closer the absolute value of \(R\) is to \(1\), the stronger the correlation. The value of the correlation coefficient between \(X\) and \(Y\) can be calculated by Equation (15).  
\[R(X,Y)=\frac{Cov(X,Y)}{\sqrt{D[X]D[Y]}}=\frac{E(XY)-E(X)E(Y)}{\sqrt{E(X^{2})-E ^{2}(X)}\sqrt{E(Y^{2})-E^{2}(Y)}} \tag{15}\]  
Here, \(E\) represents the mathematical expectation, \(D\) represents the variance, and _Cov_ represents the covariance.
## 3 Properties and Characteristics of LoRaWAN Technologies
### Carrier Frequency (CF) or Frequency Band  
LoRaWAN operates in free frequency bands such as 433 MHz, 865 MHz, and 915 MHz, which eliminates the requirement for a license to operate. These frequency bands serve as the communication medium for LoRa nodes and LoRa gateways within the LoRaWAN network [27]. The network layer protocol functions on the medium access controller (MAC) and enhances an impressive range of approximately 10 miles or 15 km. LoRaWAN hasa default bandwidth of 125 kHz for wireless transmission on specified frequencies. The choice of bandwidth, which can be according to the frequency plan and lie anywhere between 125 kHz and 500 kHz, also impacts the amount of time it takes to modulate a data packet [19]. The LoRa modules have stated transmission powers of 13 dBm, 16 dBm, and 20 dBm at 433 MHz, 868 MHz, and 915 MHz, respectively [28]. LoRa follows standard protocols that manage the duty cycle, which regulates how long a device is active during transmission. There are specific values that can be specified for this option, including 0.1%, 1%, or 10%. Furthermore, several countries have restrictions regulating the Time on Air (ToA) for packets carried using LoRa. Regulations use mechanisms like duty cycles, dwell times, and Listen Before Talk (LBT) to make sure people follow the rules [18]. In several regions, the commonly used frequency bands are subdivided into specific frequency ranges, each allocated for distinct purposes, such as indoor or outdoor use, with corresponding power limits. The frequency bands are also deployed in certain regions, depending on regulatory constraints, the available spectrum, and interference considerations. The choice of a specific frequency band is based on a critical decision, which can be influenced by these factors to ensure optimal performance and compliance [21].
# Contamination with Pharmaceuticals in Aquatic Environment: Focus on Analytical Methodologies
## 3 Determination of Pharmaceuticals in Environmental Samples
### Sample Preparation Techniques  
Sample preparation is the first step of the analytical method and is crucial for the accurate and correct determination of analytes.  
Suitable pretreatment methods are selected for different analytes in various environmental samples based on the physicochemical properties of the substances. In complex environmental matrices, analytes are converted into forms that can be easily extracted and purified. The result of sample preparation is the reduction of matrix interferences and the concentration of the analyte [51]. SPE has gradually replaced the traditionally used liquid-liquid extraction (LLE) method for the sample treatment, used to isolate organic compounds from the aqueous matrix into the organic layer. SPE is suitable for many types of environmental samples, allows for the analysis of the sample as a whole, and can also be automatized, ultimately enabling the processing of a large number of samples in a set time [10].
## 4 Results and Discussion
### Comparative Analysis  
In order to better evaluate the accuracy of the two models, their output error was quantified by calculating the maximum absolute error, the maximum relative error, and the root mean square (RMS) error, respectively, as shown in Table 4 for the first-order RC model and in Table 5 for the second-order RC model. Under 1C constant current discharge, for the first-order RC model, the root mean square error was 0.0221 V, the maximum relative error was 1.65%; for the second-order RC model, the root mean square error was 0.0156 V, the maximum relative error was 1.22%. Under UDDS cycle, for the first-order RC model, the root mean square error was 0.0298 V, the maximum relative error was 1.88%, and for the second-order RC model, the root mean square error was 0.0282 V, the maximum relative error was \(-1.69\%\), respectively.  
It can be seen from the two tables that the maximum relative error of the first-order RC model and the second-order RC model under the two representative operating conditions were both less than 2%, which can generally satisfy the precision requirements in the engineering calculation. By longitudinal comparison, it can be found that the output error of two models had a certain decrease, but not to the great extent expected in the simple conditions compared the actual complex conditions (UDDS), which may be due to measurement errors of V\({}_{\text{OC}}\). By transverse comparison, the output error of the second-order RC model in the two conditions had a certain decrease but little change. This may be due to the increase in the number of parameters, resulting in the increase of the uncertainty factors of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Type** & **Maximum Absolute Error (V)** & **Maximum Relative Error** & **RMS Error (V)** \\ \hline The first-order RC model & 0.0610 & 1.65\% & 0.0221 \\ The second-order RC model & 0.0452 & 1.22\% & 0.0156 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The output error of the first-order and the second-order model for 1C constant current discharging. Root mean square (RMS): root mean square.  
Figure 16: Output errors of the first-order and second-order RC model for UDDS cycle.  
identification process; thus, the overall accuracy of the second-order RC model did not provide great improvement over the first-order RC model.  
The discharge curve has been used to predict capacity fading of lithium-ion cells, which can reflect the discharge cut-off point, discharge plateau, and capacity of the cell. In this paper, the area surrounded by the discharge curve is used to describe the similarity of the discharge curve, which is defined as follows:  
\[\text{A}=\int\text{Udt} \tag{12}\]  
It can be seen from Table 6 that the area surrounded by the discharge curve of the first-order model is much larger than the second-order model, and therefore the second-order model is more accurate than the first-order model in terms of UDDS profile.
# Conformal Titanyl Phosphate Surface Passivation for Enhancing Photocatalytic Activity
## 3 Results and Discussion
### Chemical Modification  
For the sake of understanding the chemical modification, XPS characterization was performed as shown in Figure 2. The C 1s XPS spectra of bare and TP-TiO\({}_{2}\) were utilized to reference the measured XPS results, where the binding energy of major C-C peaks are 284.8 eV (Supplementary Figure S1). The positive shift of Ti 2p\({}_{1/2}\) and Ti 2p\({}_{3/2}\) peaks of TP-TiO\({}_{2}\) were observed in the Ti 2p XPS spectra (Figure 2a). The shift in Ti 2p can be attributed to the ameliorated surface states [32; 33]. This positive shift of Ti 2p demonstrates that the Ti-O-P-O bonds were successfully formed on the surface of the TiO\({}_{2}\) core particle, and resulted in the reduction of trap states originated from Ti\({}^{3+}\) defects. The O 1p XPS spectra are displayed in Figure 2b. Two dominant peaks of bare TiO\({}_{2}\) at 530.1 and 531.6 eV are assigned to the lattice oxygen (O\({}_{\rm L}\)) and surface oxygen (O\({}_{\rm S}\)), respectively. Here, the lattice oxygen indicates Ti-O-Ti chain. However, the TP-TiO\({}_{2}\) exhibited four peaks at 530.1, 531.1, 531.6, and 532.9 eV. The newly arising peak at 531.1 eV, corresponding to Ti-O-P bond, demonstrates the formation of titanyl phosphate on the surface of TiO\({}_{2}\)[34]. Moreover, the sharper O\({}_{\rm S}\) peak centered at 531.6 eV is also attributed to the OH species or O vacancies that originated from the titanyl phosphate groups [35]. Considering the disordered structure of surface titanyl phosphate layer in the TEM image (Figure 2e), the phosphate anion groups can induce -OH groups or O vacancies on TP-TiO\({}_{2}\). In addition, the shoulder peak at 532.9 eV, can be attributed to chemisorbed oxygen species (O\({}_{\rm C}\)) such as CO\({}_{2}\) or H\({}_{2}\)O [36]. During the sol-gel process, the structure determining agent (i.e. PEG) can be bounded to the phosphate anion species via hydrogen bonding. Therefore, the carbon or oxygen related species (including CO\({}_{2}\) or carboxylic groups) can be remained on the surface of TP-TiO\({}_{2}\) after performing the annealing process. As shown in C 1s XPS (Figure S1), the slight increase in C-O (ca. 286.5 eV) and C=O (ca. 288.5 eV) peaks were observed in TP-TiO\({}_{2}\), which implies that the oxygen species can be adsorbed onto TP-TiO\({}_{2}\)[7], P 2p XPS is shown in Figure 2c, where TP-TiO\({}_{2}\) has two sharp peaks at 133.0 and 133.9 eV corresponding to P 2p\({}_{3/2}\) and P 2p\({}_{1/2}\), respectively. These peaks can be assigned to the monodentate coordinated phosphate anion (PO\({}_{4}\)\({}^{\rm 3}\)), when it comes to the binding energies of Na\({}_{2}\)HPO\({}_{4}\) (133.1 eV) and NaH\({}_{2}\)PO\({}_{4}\) (133.9 eV) [23]. Additional T-P bond was not observed in the XPS spectrum.  
Figure 1: (a) XRD pattern of bare and TP-TiO\({}_{2}\). SEM images of bare (a) and TP-TiO\({}_{2}\) (b). (d) and (e) HR-TEM images of TP-TiO\({}_{2}\). The inserted image is a reduced FFT pattern. (f) HAADF TEM image and EDS elemental mapping of Ti, O and P atoms for TP-TiO\({}_{2}\).
###### Abstract  
Erdafitinib is an oral pan-fibroblast growth factor receptor (FGFR) inhibitor and has a potent antitumor activity against FGFR-aberrant malignancies. Erdafitinib has a narrow therapeutic index, and its pharmacokinetics are influenced by genetic variability and interacting medication. Routine therapeutic drug monitoring and dose adjustment are recommended. This study aims at developing a new UPLC-MS/MS method for determination and quantitation of erdafitinib in human plasma using brtutinib as an internal standard. The sample ionization was performed by using electrospray ionization in positive mode, and multiple reaction monitoring was used for the quantification of target analytes. The chromatographic separation of erdafitinib and IS was achieved by an UPLC BEH C\({}_{18}\) column (2.1 mm \(\times\) 100 mm, 1.7 \(\upmu\)m). Erdafitinib metabolic stability was studied using intrinsic clearance and in vitro half-life. The greenness of the developed method was evaluated using appropriate, analytical Eco-Scale and AGREE software. The linearity of the established UPLC-MS/MS assay ranged from 0.5 to 1000 ng/mL with r \(>\) 0.99 with a limit of quantitation of 0.5 ng/mL. The accuracy and precision were within acceptable limits and the average recovery and matrix effects were 86.11% and 90.51%, respectively. Erdafitinib metabolic stability was studied and its in vitro half-life was 7.28 min and intrinsic clearance was 95.11 \(\upmu\)L/min/mg. The assessment of the greenness profile of the method indicated that the method is eco-friendly. The proposed method can be utilized for therapeutic drug monitoring and pharmacokinetic studies.  
Keywords:erdafitinib; cofriendly; metabolic stability; UPLC-MS/MS +
Footnote †: journal: Applied Sciences
## 2 Convolutional Neural Network
### Fully Connected Layer  
Features extracted through the pooling layer are input to the fully connected layer (dense layer) for compressing data and pattern classification. The fully connected layer is a multi-layer neural network and its structure is given in Figure 2. The number of layers and neurons in each layer are set manually and different layers as well as the number of neurons affect the accuracy of the network output. Each neuron is equivalent to an McCulloch-Pitts neuron Model (M-P Model), as shown in Figure 3 and receives input data transmitted from the previous layer of neurons. These data are transmitted through a connection with weight and the total input value received by the neuron needs to be added with a bias term and activated by a nonlinear activation function to generate the d into a column v output of the neuron. The M-P neuron model can be expressed as the following Equation (7):  
\[\text{y}=g(\sum_{i=1}^{n}w_{i}a_{i}+b) \tag{7}\]  
where \(n\) is the number of neurons in the previous layer. \(a_{i}  
\[a^{[l]}=g^{[l]}(z^{[l]}) \tag{9}\]  
where the superscripts \([l]\) and \([l-1]\) represent the layer where the parameters are located. \(W^{[l]}\) is a matrix with dimensions (\(n\), \(m\)). Among them, \(n\) and \(m\) are the number of neurons in the previous layer and the next layer, respectively. The output \(a^{[l-1]}\) of the neuron in the previous layer undergoes a linear transformation of \(W\) and the bias vector \(b^{[l]}\) to obtain an inactive output \(z^{[l]}\). Then, the nonlinear mapping of \(z^{[l]}\) through the activation function \(g^{[l]}\) obtains the output \(a^{[l]}\) of the current layer.  
The output of each neuron also needs to be activated by a nonlinear activation function that is like the convolutional layer. Because the different activation functions of each layer will affect the performance of the model, it is necessary to choose the appropriate activation function for different problems. In the output layer, if the study is preparing to solve the binary classification problem, the output layer generally contains only one neuron and the activation function is usually the sigmoid function as shown in Equation (10). However, the output layer generally contains multiple neurons and the activation function is usually the softmax function for multi-classification problems. The expression is given as Equation (11). Besides, the middle-hidden layer also usually selects ReLU as the activation function:  
\[g(z)=\frac{1}{1{+}e^{-z}} \tag{10}\]  
\[g(z_{i}^{[l]})=\frac{e^{z_{i}^{[l]}}}{\sum_{i=1}^{n_{i}^{[l]}}e^{z_{i}^{[l]}}} \tag{11}\]  
where \(z_{i}^{[l]}\) represents the output value of the \(i\)-th neuron in layer \(l\) before activation. For the fully connected layer, the weight matrix and the bias term denote the parameters that need to be obtained through training.