esa-sceva/satcom-chunk-collection huggingface dataset:
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 2 Drugs Acting on the Cardiovascular System
### Antihypertensive Drugs
#### 2.2.2 Sartans  
Sartans are chemical compounds with antagonistic action against the ATI receptors of angiotensin II [67]. Losartan, an achiral compound, is the progenitor of the group, followed by valsartan (**12**), irbesartan, candesartan, orosartan, telmisartan, and a few other compounds. Among the latter, valsartan is chiral and is used as a single (_S_)-enantiomer, since the activity of the (_R_)-enantiomer is clearly lower than the (_S_) one [68]. The (_R_)-enantiomer of valsartan fails to affect plasminogen activator I activity induced by angiotensin II, and its activity is lower than the (_S_)-enantiomer. Consequently, (_R_)-valsartan is an impurity and is needed to control the levels in commercial preparations of (_S_)-valsartan [69].
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.10 Indoor Navigation
The **AR/VR Application** was found in one article from 2018 only--by [PERSON] et al. [40]. Their system consisted of two main elements: a smartphone used inside the explored building and a server conducting calculations. Based on a 3D model of the building (generated from 2D architectural blueprints), they showed how to process it into a topology model and combined it with a Wi-Fi indoor positioning system. This method first created a 3D geometry replica of the building. Then, it processed it into two representations: a voxelized space for initial room segmentation and a skeleton graph for 3D navigation. The prepared data were stored in a database and, during the navigation, combined with readings from the smartphone's Wi-Fi, resulting in real-time localization information shown on the screen.
## 3 Methodology
### Deep Learning Approach
#### 3.6.2 Proposed BiGRU Model  
The proposed BiGRU model, illustrated in Figure 3, builds on the implementation of Arabic sentiment analysis (ASA) using the Bidirectional Gated Recurrent Unit (BiGRU) architecture for binary classification, distinguishing between positive and negative sentiments.  
(1) Input Layer: The input layer of the model utilizes advanced word embeddings to capture the semantics and context of Arabic text. We incorporate AraBERT, Word2 Vec, and fastText for this purpose. AraBERT, pre-trained on extensive Arabic datasets, captures deep semantic relationships, while Word2 Vec, with a Skip-gram model using a window size of 5 and a vector dimension of 300, reveals syntactic and semantic patterns in the language. FastText, with a window size of 40 and a vector dimension of 300, enhances sentiment analysis by addressing Arabic's complex morphology and treating words as character-level n-grams. These embedding techniques ensure robust input representations for our sentiment analysis model.  
(2) Feature Extraction: The feature extraction phase is conducted using a 32-unit BiGRU layer, which processes the word embeddings bidirectionally. This architecture allows the model to capture both forward and backward contextual information, ensuring a more comprehensive understanding of the text and improving its ability to grasp intricate sentiments in Arabic.  
(3) Classification Layers: After feature extraction, the BiGRU output is passed through classification layers. This stage involves two fully connected layers, starting with a hidden layer of 1024 neurons. To prevent overfitting and improve generalization, normalization layers and a dropout layer are employed. These components work together to refine the features extracted by the BiGRU and enhance model performance.  
(4) Output Layer Configuration: The output layer is designed explicitly for binary classification, with a single neuron responsible for differentiating between positive and negative sentiments. This configuration ensures precise sentiment classification, improving the model's ability to handle the complexity of Arabic text while delivering accurate sentiment predictions.  
Figure 2: The Architecture of the Proposed MCNN.
## 2 Materials and Methods
### Test Method
#### 2.3.6 Analysis of Odor Components and Content in Instant Black Tea Using HS-SPME-GC-MS  
Accurately weigh 0.1 g of instant black tea powder (\(\pm\)0.001 g) into a 20 mL headspace vial. Add 5 mL of boiling purified water and 10 \(\mu\)L of a 50 \(\mu\)g/mL ethyl decanoate solution as the internal standard. Gently shake to mix, add a magnetic stirr bar, seal the vial, and place it on a magnetic stirrer at 250 rpm and 75 \({}^{\circ}\)C for 10 min to equilibrate. Insert the extraction fiber into the headspace vial and extract for 50 min. After extraction, quickly remove the fiber and insert it into the gas chromatograph injection port. Desorb the sample at 250 \({}^{\circ}\)C for 3 min, and then perform GC-MS analysis [35].  
The gas chromatography (GC) conditions are as follows: An HP-5 MS capillary column (30 m \(\times\) 250 \(\mu\)m \(\times\) 0.25 \(\mu\)m) with a splitless injection mode, an injection port temperature of 250 \({}^{\circ}\)C, and high-purity helium gas (purity \(>\) 99.999%) as the carrier gas at a flow rate of 1.0 mL/min. A constant flow column flow control mode is used with a solvent delay of 3 min. The GC oven temperature program is as follows: hold at 50 \({}^{\circ}\)C for 1 min; increase to 80 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min and hold for 1 min; increase to 120 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and hold for 1 min; increase to 140 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and hold for 3 min; increase to 150 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min and hold for 3 min; increase to 160 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and hold for 2 min; and finally, increase to 220 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min and hold for 5 min.  
The mass spectrometry (MS) conditions are as follows: Electron impact ionization (EI) source with an electron energy of 70 eV, a transfer line temperature of 280 \({}^{\circ}\)C, an ion source temperature of 230 \({}^{\circ}\)C, and a quadrupole temperature of 150 \({}^{\circ}\)C. The scan mode is full scan (SCAN), with a mass scan range of \(m/z\) 45 to 600 amu.  
The qualitative and semi-quantitative analysis of volatile odor compounds was performed using the NIST11 spectral library for compound identification, and the internal standard method was used for semi-quantification. Ethyl decanoate was used as the internal standard, and the concentration of volatile odor compounds was calculated using Equation (1):  
\[C_{x}=(S_{x}*C_{i})/S_{i} \tag{1}\]  
where C\({}_{x}\) and S\({}_{x}\) represent the concentration and peak area of the volatile odor compound, respectively, and C\({}_{i}\) and S\({}_{i}\) represent the concentration and peak area of the internal standard ethyl decanoate, respectively.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 4 Fake Image Detection Methods--Using Handcrafted Feature Set
### Forensic Features Based
#### 4.1.2 Image Splicing
Several machine-learning classification techniques were employed. In their work, [PERSON] et al. [55] provide a unique Pixel's fractional mean (PFM) approach to improve pictures before classification to improve recognition of image splicing fraud based on texture attributes. Depending on the intensity of each pixel's occurrence, the suggested PFM enhances each pixel independently. The most important elements from allegedly spliced photos are extracted using two texturing algorithms. The SVM classifier then employs these attributes to classify genuine and spliced pictures. In their proposed system, [PERSON] et al. [56] used a self-supervised method for training splicing detection/localization models using an image's frequency transform \"real-valued fast Fourier transform\" (RFFT) algorithm. The deep network developed a representation to capture an image-specific signature by enforcing (image) self-consistency to detect the spliced areas. To solve this issue, the authors suggested an Edge-enhanced Transformer (ET) for tampering area localization. A novel method by [PERSON] et al. [57] proposed a two-branch edge-aware transformer created specifically to include the splicing edge hints into the forgery localization network, creating forgery features and edge features to collect rich tampering traces. Additionally, the authors provided a feature improvement module to draw attention to edge area artifacts in forged features and apply weight values to the resultant tensor in the spatial domain for essential signal amplification and noise reduction. Table 3 compares the computational efficiency among various methods stated above for detecting splicing. Again, as expected in forensic methods, feature processing techniques like RFE and SVD help drastically reduce the computational power and make the model fast and efficient. Table 4 briefly compares various techniques after 2010 since earlier techniques did not have good accuracy results. Here, it can be observed that the forensic feature set fails to identify the fake image if splicing is fused with other manipulation techniques like compression, retouching, and resizing.
# Green Hydrogen Production--Fidelity in Simulation Models for Technical-Economic Analysis
## 2 System Component Modeling
### Energy Storage Model  
Energy storage systems have proven to be one of the leading solutions when there is a shortage of renewable energy generation in order to balance demand with generation. Its application is of great importance to achieve the correct performance of all elements in the electrical grid. In this context, the use of batteries is based on the production of green hydrogen with minimal or zero consumption from the grid. Excess PV energy is stored in the batteries during peak power production hours. Then, the accumulated energy is returned in the case of demand from the electrolyzer. This guarantees optimal performance for hydrogen production without interruption. Although each ESS depends on constructive and technological characteristics, the control of the SoC is exclusively detailed and can be applied to any ESS. Therefore, the SoC of the batteries is analyzed through simulation in the following way.  
The ESS allows charging or discharging in random sequences with any amplitude between 0 and 100% of its effective capacity. The ESS control algorithm to limit the SoC is shown in Figure 5, where the value of \(P_{t}^{ref}\) is initially assigned as the difference between the photovoltaic power \(P_{t}^{PV}\) and the electrolyzer demand power \(P_{t}^{ETZ}\), in such a way that being a value greater than zero, it allows the charging of the \(P_{t}^{ESS}\) from a time \((\Delta t_{charge}=t_{2}-t_{1})\) in the positive cycle and defines \(P_{t}^{charge}=P_{t}^{ref}\). Subsequently, the charge energy value \(E_{t}^{ESS}\) is calculated as the integral of the charge power. The same is applied for the discharge energy, assigned for the negative cycle \(Ed_{t}^{ESS}\). The total energy of the ESS at \(E_{t}^{ESS}\) during a time \(t_{n}\) and \(t_{n}^{\prime}\) varies as a function of the difference in the charge and discharge energy, respectively. This procedure is summarized in Equation (25):  
\[\left\{\begin{array}{c}P_{t}^{ref}=P_{t}^{PV}-P_{t}^{ETZ}\\ E_{t}^{ESS}=\int_{t_{1}}^{t_{2}}P_{t}^{charge}dt\to P_{t}^{ref}\geq 0\\ E_{t}^{ESS}=\int_{t_{1}}^{t_{2}}P_{t}^{discharge}dt\to P_{t}^{ref}<0\end{array}\right. \tag{25}\]  
Then, the value of the total energy in the storage is calculated as follows:  
\[E_{t}^{ESS}=\eta_{c}\times E_{t}^{ESS}-\frac{1}{\eta_{d}}\times Ed_{t}^{ESS} \tag{26}\]  
where \(\eta_{c}\) is charging efficiency and \(\eta_{d}\) is discharging efficiency. Then, the calculation to determine the SoC is calculated with the maximum storage energy \(E_{max}\), that is,  
\[SoC_{t}^{ESS}=SoC_{t-1}^{ESS}+\frac{E_{t}^{ESS}}{E_{max}}\times 100\% \tag{27}\]  
The SoC restriction is limited in its maximum \(SoC_{max}\) and minimum \(SoC_{min}\) values allowed according to the following equation:  
\[\left\{\begin{array}{c}P_{t}^{ESS}=P_{t}^{ref}\rightarrow\left(SoC_{min} \leq SoC_{t}^{ESS}\leq SoC_{max}\right)\\ P_{t}^{ESS}=0\rightarrow\left(SoC_{max}< SoC_{t}^{ESS}< SoC_{min}\right)\end{array}\right. \tag{28}\]  
Figure 5: ESS state of charge control algorithm.
# Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
## 2 Related Work
The FE calculates the (mean, entropy) values from the packet characteristics to categorize traffic into one of eight classes, including seven types of DDoS attacks, and recalls upon the TC. According to test results, the model's accuracy is over 95% for each type of DDoS attack and over 98.5% for a binary class that only includes attack and normal states. [PERSON] et al. [16] used SVM in combination with the sFlow toolset to detect threats to SDN. The model pulls behavioral variables for SVM learning from traffic information gathered from switches via the controller. The purpose of the examination is to discover the behavioral traits of recognized assaults. SVM can identify an attack by contrasting normal and malicious behavioral profiles. The outcomes revealed that the accuracy (A) is 96.8%. In recent years, there have been several studies focused on comparison techniques for detecting distributed denial-of-service (DDoS) attacks, as evidenced by research papers [7; 9; 10; 11; 12; 13; 15]. However, these studies have certain limitations that require further research. Specifically, many comparison methods evaluate only various machine learning (ML) or deep learning (DL) algorithms using simulation offline analysis, which poses a challenge for creating and assessing the performance of one's own ML-DL model combination. Moreover, some approaches concentrate on assessing the performance of the training model without taking into account the performance of the prediction model, which is crucial for assessing how the algorithm behaves with new data. Another limitation is that most studies evaluate the model's performance based on accuracy, F1-score, and other metrics without considering the time taken for detection during training or prediction, which is an important factor for real-time detection. Lastly, the majority of research on this topic evaluated the model's performance using default parameter values and without considering the type of kernel, thereby missing opportunities to explore how these parameters and kernel types impact the algorithm's behavior on both linear and non-linear datasets.
# Cyanobacterial Potential for Restoration of Loess Surfaces through Artificially Induced Biocrusts
## 2 Materials and Methods
### Screening of Cyanbacterial Strains for Toxin, Biomass and Polysaccharide Production
#### 2.1.3 Quantification of Biomass Production  
Biomass production of the strains was expressed through the concentration of chlorophyll \(a\) (Chl _a_), measured spectrophotometrically (SPECTROstar Nano, BMG LABTECH GmbH, Ortenberg, Germany) at seven-day intervals for seven weeks. The Chl \(a\) concentrations were measured according to the Mackinney [75], while the corresponding biomasses were calculated indirectly according to the APHA method [76]. Samples for determination of Chl \(a\) concentration were prepared in the following way: 10 mL of each sample was filtered through the filter paper (Filtratech QL05, Saran, France), extracted in dark at 4 \({}^{\circ}\)C for 24 h in glass tubes containing 5 mL of 100% methanol. The samples were further sonicated for 10 min in an ultrasonic bath (Bransonic 12) and centrifuged (Tehtracia, Zelezniki, Slovenia) at 1150\(\times\)\(g\) for 10 min. The supernatant was separated and analyzed spectrophotometrically at 663 nm. 100% methanol was used as a blank.
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 2 Thermal Model  
A set of thermal balance equations are established according to the thermal behavior inside the belt drive system to formulate a thermal model, which, along with the heat generation information from the existing power loss theory, can be used to calculate the temperature distribution. Two approaches are considered to create two different groups of thermal balance equations. The first approach is to use the analytical method, which is suitable for establishing the equations for the belt heat dissipation and belt-pulley heat exchanges, because the belt has a simple structure. The second approach corresponds to the pulley thermal analysis. In this analysis, the temperature distribution of the pulleys under various conditions is examined, and a mathematical algorithm is formulated to describe the temperature deviations at different operating parameters. Subsequently, the numerical method is used to perform a thermal analysis for the baseline temperature distributions. This step provides the coefficients and functions for the pulley thermal balance equations. The next step involves simultaneously solving all the thermal balance equations to determine the belt temperature and heat dissipation of each pulley. Finally, the proposed model outputs the temperature plot of each pulley under the designed operating conditions by calculating the temperature changes based on the baseline numerical simulations. At this point, the model can effectively provide the temperature distributions of a belt drive system under a large number of conditions in a reasonable processing time.
## 4 Conclusions  
In conclusion, we have experimentally demonstrated remote distribution of multifrequency optical frequency references via both a fiber-optic and a free-space transfer links. Using a multiheterodyne technique, we have characterized the excess phase noise introduced to the clock signals by the transfer links. In the case of fiber-optic distribution, a single-tone, external acoustic perturbation is introduced and its impact on the phase noise spectrum is studied. Enhancement of noise power is found at the perturbation frequencies and their harmonics, whereas, for a wide span of perturbation frequencies (100-1000 Hz), noise suppression is observed at frequencies below the perturbation frequencies. In the case of free-space distribution, spectral broadening is observed after a 60 m atmospheric transmission and the resulted clock instability is characterized by an Allan deviation measurement. Overall, this research shows the effectiveness of multiheterodyne in the characterization of multifrequency remote clock distribution based on frequency combs.  
Figure 5: (**a**) A typical spectrum of the transmission-broadened optical clock signal. Inset: a time-domain trace of the beat note between the recovered clock and the original clock; (**b**) A typical Allan deviation measurement result.
## 5 Conclusions  
A RIS-aided multi-user MISO downlink system was investigated in this paper. An optimization model was used to design the transmit precoding matrix at the BS and the phase shift values at the RIS to conduct the maximization of the weighted sum of EE and SE, considering the power constraint at the BS, RIS's RC with constant modulus, and RIS's phase shifters with continuous values. We finally attained EE-SE trade-off under different circumstances by changing the value of the weight factor and got the closed-form solution of phase shift values at the RIS, while the optimal precoding matrix was obtained through the standard convex optimization algorithms. Simulations results illustrated that the presented optimization algorithm possesses fast convergence rates under different values of the power budget at the BS. It also showed that the optimization over the RC of RIS could significantly improve the EE and SE of the system.  
[PERSON] perceived the idea. [PERSON] wrote the manuscript, performed the analytic calculations, and derived the models. [PERSON] performed the simulations and analyzed the data. [PERSON] contributed to the revising and proofreading. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Fundamental Research Funds for the Central Universities and School of Information Science and Engineering of Southeast University through Student Research Training Program.  
The authors declare no conflict of interest.
## 4 Algorithm Description
### Meta-Data Extraction
#### 4.2.1 Activity Meta-Data
As illustrated at Figure 4, \(x_{1}\) lies within a stationary duration at the 2 nd, 3 rd and 4 th epochs, the estimated probability of \(x_{1}\) is: \(\sum_{i=1}^{5}f(x_{1},e_{i})\)=\(f(x_{1},e_{2})+f(x_{1},e_{3})+f(x_{1},e_{4})\)=\(\frac{1}{2^{5+1-2}}+\frac{1}{2^{5+1-3}}+\frac{1}{2^{5+1-4}}\) = 0.4375. Figure 4: Mapping the maximal stationary durations (\(sd\)) into set of key stationary durations (\(ksd\)) with threshold \(\theta=0.6\). ADG then aggregates and maps the _stationary durations_ (\(sd\)) into a set of _key stationary duration_ (\(ksd\)). All points in the \(ksd\) has a estimated probability greater than a threshold parameter \(\theta\), and it means the node would stay stationary or only roams within a small area during the time interval \(ksd\) with probability higher than \(\theta\). A \(ksd\) should meet the following conditions:  
\[(1)\forall x\in ksd,\;\;p(x)\geq\theta;\;\;\;(2)\;\
ot\exists ksd^{\prime} \supset ksd,\;\;s.t.\;\;\forall y\in ksd^{\prime}\;\;p(y)\geq\theta \tag{5}\]  
Condition 2 means \(ksd\) is the maximal superset of the range, and it does not exist another key static duration that contains the \(ksd\). Figure 4 also illustrates the set of \(ksd^{\prime}s\) with \(\theta=0.6\) (is also the default value in simulation), and readers could refer to Appendix A for the detailed algorithm that extracts a nodes's the \(ksd^{\prime}s\). A stationary stay is denoted as \(ss=(sd,C_{sd})\), where \(sd\) is the stationary duration and \(C_{sd}\) is the central point. Correspondingly, stationary stays are mapped into a _key stationary stay_ (\(kss\)), and a _key stationary stay_ is denoted as \(kss=(ksd,\Omega)\). Here \(\Omega\) denotes the possible locations and their weights the node might be in during the key stationary duration \(ksd\). A node might have multiple key stationary stays. The set of key stationary stays of node \(s_{i}\) is denoted as \(\aleph_{i}\), then the _accumulated weight of stationary stay_ of node \(s_{i}\) is denoted as \(\mu_{i}\), which is a key factor for the proxy node selection:  
\[\mu_{i}=\sum_{kss\in\aleph_{i}}|kss.ksd|*|\aleph_{i}|^{\frac{1}{2}} \tag{6}\]  
where \(|kss.ksd|\) denotes the length of key stationary duration in \(kss\), \(|\aleph_{i}|\) is the number of elements in the set. From the equation, we could see that \(\mu_{i}\) goes up when the \(ksd^{\prime}s\) have longer accumulated time length and are distributed to more separated segments on the time line of an epoch.