esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Determination of Cyclin Kinase Inhibitors p21 and p27 Expression  
Based on the dose-dependent anti-proliferation activity, A549 cells treated by the DMEM medium with low (200 \(\upmu\)mol-L\({}^{-1}\)) and high (600 \(\upmu\)mol-L\({}^{-1}\)) concentrations of catechin were selected to evaluate the cyclin kinase inhibitors p21 and p27 expression levels. The cells treated by the medium without catechin were used as control. The total RNA of cells in each group was extracted using Trizol reagent. Then, cDNA was produced based on the RNA reverse transcription kit protocol and applied as a template for PCR analysis. qPCR was performed by based on the instruction of the SYBR kit (Takara Bio, Tokyo, Japan). The primer sequences for p21, p27, and GAPDH (Human glyceraldehydeys-3-phosphate dehydrogenase) control were shown in Table 1. The qPCR reaction mixture consisted of 5 \(\upmu\)L of SYBR Premix, 0.8 \(\upmu\)L of forward primer, 0.8 \(\upmu\)L of reverse primers, 1.0 \(\upmu\)L of template DNA, and 2.4 \(\upmu\)L of nuclease-free water. The program of qPCR was set at an initial denaturation at 95 \({}^{\circ}\)C for 5 min, 40 cycles of 95 \({}^{\circ}\)C for 30 s, 60 \({}^{\circ}\)C for 30 s, and 72 \({}^{\circ}\)C for 30 s, and final extension at 72 \({}^{\circ}\)C for 10 min. Then, the reaction was maintained at 12 \({}^{\circ}\)C. The qPCR analysis for each gene was repeated in triplicates. The expression values for each target gene were normalized to GAPDH by the relative gene expression method.
# XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding
## 2 Background and Motivation
### XSS Attack Mechanisms
This request–response mismatch prevents the detection system from establishing the causal relationship required for triggering the “ban” signal. Orange solid lines indicate traffic containing attack payloads, black solid lines represent normal traffic, and blue dashed lines denote control signals. Code: <img src=\"avatar.jpg\" onerror=\"{payload}\">  
In this example, the malicious payload is stored in the user's profile (perhaps as a comment or profile description) and executed whenever any user views the profile page. This characteristic makes traditional traffic monitoring-based defense measures inadequate in simultaneously capturing both the injection and execution processes, resulting in significant limitations when addressing stored XSS. Furthermore, stored XSS attacks have higher stealth and broader impact, as their malicious scripts can execute automatically when infected pages are visited, requiring no active user interaction [7]. The severity of stored XSS attacks lies in how their complex operational characteristics further amplify threats to web security. The persistence mechanism allows malicious scripts to remain dormant for extended periods and execute automatically upon user access, forming a persistent attack vector. These malicious codes seamlessly integrate with legitimate web content, demonstrating strong stealth capabilities [9]. A single injection can affect numerous users, creating a multiplier effect, particularly on platforms relying on user-generated content [10]. Moreover, users typically lack security vigilance when interacting with familiar, trusted websites, and stored XSS's ability to circumvent common defense measures' limitations further increases the difficulty of defense. The importance of LLMs in reshaping the landscape of cybersecurity analysis is increasing. With their exceptional capabilities in understanding code structures, identifying patterns, and parsing semantic relationships, LLMs are gradually becoming core components of security analysis systems. Practice has shown that integrating LLMs into security workflows can significantly improve efficiency; for example, LLM-based code scanning systems have demonstrated detection rates several times higher than traditional methods [11; 12]. This breakthrough development reflects the deep integration trend between artificial intelligence and cybersecurity, not only pioneering more intelligent security solution pathways but also demonstrating significant value in identifying and defending against complex attack vectors [13; 14].
# Forecasting Daily Temperatures with Different Time Interval Data Using Deep Neural Networks
## 4 Data
### Data Description  
We collected the numeric weather data observed from the Automated Surface Observing System operated by the Korea Meteorological Administration [37]. The Automated Surface Observing System (ASOS) is a manned weather measurement system where the Korea Meteorological Administration maintains and observes weather elements. We selected three AOS stations in South Korea as shown in Figure 3, representing metropolitan, Seoul; mountainous, Daegwalleong; and coastal, Seongsan. There are 96 ASO's stations in operation in South Korea, and the average, maximum, and minimum distances from one center to its nearest center are 32.6 km, 142.3 km, and 13.7 km, respectively [38; 39].  
Note that the proposed deep neural network models perform pointwise temperature forecasting at the ASOS station where the target variable is collected. We stated whether the input data from the stations other than that corresponding to the target variable were used or not, for each experiment in this study, in Section 5.1. We collected 10-year data, from 2009 to 2018, in hourly time units, and we used 15 input variables: temperature, amount of water precipitation, humidity, vapor pressure, dew point temperature, atmospheric pressure, sea-level pressure, hours of sunshine, solar radiation quantity, amount of snowfall, total cloud cover, middle- and low-level cloud cover, ground-surface temperature, wind speed, and wind direction. The meteorological measurement methods of each variable are as follows [40]: (1) all temperatures are measured in every minute with an average of the last five minutes. (2) all pressure variables are measured by several pressure sensors and recorded every minute and compared with standard references for accurate measurements. (3) amount of water precipitation is recorded every minute based on observing accumulated amount of amount of water precipitation using cylinder and applying a post-processing algorithm. (4) humidity is calculated using the five-minute averaged temperature and dew point temperature. (5) hours of sunshine are observed by a sunshine sensor and recorded the cumulative time of sunlight in every minute. (6) solar radiation is observed using a pyranometer and recorded in every minute. (7) amount of snowfall is recorded in every minute by using the cylinder with heating system. (8) total, middle-level, and low-level cloud cover rely on human observation and are recorded in deciles in every hour. (9) wind direction and speed are recorded every minute using the past two-minute average of five-second averaged values. After collecting the data, in this study, we converted all observed variables to hourly unit by selecting the records on the hour.
## 6 Simulation Results and Discussion
### Sensitivity Analysis
#### 6.2.3. Load Consumption  
As shown in Figure 19, when the load varied from 76.94 to 107.72 kWh/day in steeps of 10% change from the minimum (keeping the current diesel price of USD 0.62/L and average GHI of 5.94 kWh/m\({}^{2}\)/day) the LCOE showed a small reduction from USD 0.175 to 0.171/kWh, while the fuel intake increased from 1895 L/yr to 4473 L/yr. Similarly, the NPC increased from USD 119,160 to USD 154,780. As electricity demand rises, DG will increase the power generation to meet the demand. This causes the RF to decline, the fuel consumption and NPC to increase, and COE to become relatively decreased.  
Figure 17: LCOE and TNPC related to GHI variations.  
Figure 18: LCOE and TNPC related to diesel price variations.  
As we have seen from the sensitivity analysis, the variations in the GHI, diesel fuel price and load have been considered to check the capabilities of the PV/diesel/battery system to withstand those fluctuations. When these sensitivity parameters were considered, the new optimization results achieved were LCOE of USD 0.179/kWh, NPC of USD 151,468, and RF of 69.1%. These results prove that the PV/diesel/battery system can operate reasonably well for the Kibran Gabriel island during the specified project lifetime.
# Varnishes with Biocidal Activity: A New Approach to Protecting Artworks
## 3 Results and Discussion
### Sandarac  
The analytical results with the sandarac varnish are shown in Table 3. As in the case of colophony, the biocides studied did not lead to any significant change in the composition of the original varnish. The results from the GC-MS for the type 2 samples were very similar to those obtained in the blank sample, so they were not included in the table. However, the treatments tested inhibited the growth of the fungi _C. sitophila_ and _P. chrysogenum_ in all such samples studied. To simplify the study, the data for these latter samples are not included, since they are very similar to the data for the blank samples. The main chemical change observed in the sandarac inoculated with microorganisms but with no biocides (type 3) compared to the blank samples was a significant decrease in a hydroxylated derivative of sandaracopimaric acid to produce sandaracopimaric acid (example: Sandarac-Cs, \(\Delta Ni\) OH-sandaracopimaric acid = +12.01; Sandarac-Pc/Sc, \(\Delta Ni\) OH-sandaracopimaric acid = -14,54; Sandarac-Cs, \(\Delta Ni\) OH-sandaracopimaric acid = -13,91). This has already been reported in previous studies [33; 41], although this did not generally occur in samples treated with biocides, which may be related to a drop in the microorganisms' biological activity caused by the proposed methods.  
Figure 5 shows the MDS diagram for the samples studied. A nucleus can be seen very near to the blank sample, consisting of the samples inoculated with _C. sitophila_ and treated with biocides. This indicates that there were no substantial chemical changes in the composition of the varnish. The type 3 samples (inoculated with a microorganism but with no biocide), however, appear very much grouped together and far from the control sample; this indicates the efficacy of the treatments tested in inhibiting microbial growth and also in preventing the appearance of chemical changes in the sandarac.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Sample Type** & **Manool** & \
\begin{tabular}{c} **Sandaracopimaric Isopimaric** \\ **Acid** \\ \end{tabular} &
\begin{tabular}{c} **OH-Sandaracopimaric** \\ **Acid** \\ \end{tabular} \\ \hline Sandarac & 1 & **25.79** & **47.34** & **8.02** & **14.54** \\ Sandarac-Pc & 3 & 28.33 & 61.72 & 9.95 & 0 \\ Sandarac-Cs & 3 & 27.47 & 64 & 7.9 & 0.63 \\ Sandarac-Sc & 3 & 26.29 & 65.27 & 7.74 & 0 \\ Sandarac-Benzalkonium & 4 & 22.34 & 55.96 & 8.76 & 12.92 \\ Sandarac-Benzalkonium & 5 & 24.54 & 49.94 & 10.41 & 15.1 \\ Sandarac-Tritbutylin & 4 & 18.43 & 54.33 & 13.58 & 13.65 \\ naphthalene-Sc & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: GC-MS analysis (\(p<0.05\) in all cases) of the diterpenes in the sandarac varnish and the samples inoculated with microorganisms (values expressed in \(Ni\)).
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
###### Abstract  
In this paper, we experimentally investigate the effects of degradation observed on 0.15-\(\upmu\)m GaN HEMT devices when operating under realistic power amplifier conditions. The latter will be applied to the devices under test (DUT) by exploiting a low-frequency load-pull characterization technique that provides information consistent with RF operation, with the advantage of revealing electrical quantities not directly detectable at high frequency. Quantities such as the resistive gate current, play a fundamental role in the analysis of technology reliability. The experiments will be carried out on DUTs of the same periphery considering two different power amplifier operations: a saturated class-AB condition, that emphasizes the degradation effects produced by high temperatures due to power dissipation, and a class-E condition, that enhances the effects of high electric fields. The experiments will be carried out at 30 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, and the results will be compared to evaluate how a specific RF condition can impact on the device degradation. Such a kind of comparison, to the authors' knowledge, has never been carried out and represents the main novelty of the present study.  
device degradation; gallium nitride; HEMT; load-pull measurements; microwave frequency; power amplifier; reliability; stress measurements +
Footnote †: journal: Electronics Letters  
0023
## 4 Experimental Results
### External Comparison
#### 4.2.3 Comparison with Related Works  
In Table 14, we compare various other models from previous studies with TruMuzic. The goal is to compare some famous and current models to understand the significance of TruMuzic in the field. We summarize the results in Table 14 by answering the following questions.  
1. Does the model use a dataset?
2. If yes, what is the number of songs?
3. What method do they use for similarity comparison?
4. What is the application-oriented objective of the model?
5. What aspect of achieving the result does the model focus on?
6. Do they use a neural network-based weight distribution system?
7. Does the method use any questionnaires?
8. Does the method include a user study by domain experts?  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Classes**} & **Edit** & **Silva’s** & \multirow{3}{*}{**SiMPle**} & \multirow{3}{*}{**Worker’s**} & \multirow{3}{*}{**MCSMF [9]**} & **TruMuzic** & **TruMuzic** \\  & **Distance** & & & & **Model** [7] & & **(without Deep)** & **(with deep)** \\  & **[14]** & **[51]** & & & **Model** [7] & & **Learning-Based** & **Learning-Based** \\  & **[14]** & **[51]** & & & & **Weight** & **Weight** \\  & & & & & **Distribution**) & **Distribution**) \\ \hline Class 1 and 2 & 36\% & 51\% & 57\% & 61\% & 60\% & 54\% & 76\% \\ Class 2 and 3 & 59\% & 65\% & 74\% & 77\% & 78\% & 61\% & 89\% \\ Class 1 and 3 & 66\% & 68\% & 77\% & 80\% & 92\% & 76\% & 92\% \\ Class 1 and 4 & 68\% & 76\% & 89\% & 91\% & 94\% & 90\% & 100\% \\ Class 3 and 4 & 36\% & 59\% & 65\% & 78\% & 76\% & 64\% & 81\% \\ Class 1, 2, and 3 & 26\% & 34\% & 47\% & 54\% & 58\% & 46\% & 74\% \\ Class 1, 2, and 4 & 28\% & 37\% & 61\% & 66\% & 68\% & 48\% & 80\% \\ Class 2, 3, and 4 & 32\% & 36\% & 67\% & 68\% & 73\% & 45\% & 86\% \\ \hline Overall results & 44\% & 53\% & 67\% & 72\% & 75\% & 61\% & 85\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Similarity results for allegedly copied songs showing the impact of weight distribution based Trumuzic.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
## 2 Results and Discussion
### Optimum Reaction Conditions  
The effect of solvent on the hydration of phenylacetylene (1a) was also investigated by screening different solvents, and the results showed that the solvent played a key role in this process (Table 2, entries 1-9). When the reaction was carried out in 1,4-dioxane or DMF, a trace amount of acetophenone (2a) was detected in the reaction mixture (Table 2, entries 1 and 2). Other common solvents such as toluene, DMSO, CH\({}_{3}\)CN, DCM and acetone gave moderate yield of acetophenone (2a) (Table 2, entries 3-7). When the methanol with active proton was used as a solvent for the hydration of phenylacetylene (1a), the yield of acetophenone (2a) was increased to 91%, respectively. It was noted that the addition of water in solvent did not accelerate this reaction, and the yield of acetophenone (2a) was decreased slightly swine the mixture of methanol and water was used as a solvent (Table 2, entry 10). When using ethanol alternative methanol, a similar yield of acetophenone (2a) was obtained (Table 2, entry 8). This indicates that optic solvent could accelerate the hydration of alkyne.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Entry** & **Solvent** & **Temperature \({}^{\circ}\)C** & **Time h** & **Yield \({}^{\circ}\) \%** \\ \hline
1 & 1,4-dioxane & 120 & 18 & 1 \\
2 & DMF & 120 & 18 & \(<\)1 \\
3 & Toluene & 120 & 18 & 46 \\
4 & DMSO & 120 & 18 & 49 \\
5 & CH\({}_{3}\)CN & 120 & 18 & 37 \\
6 & DCM & 120 & 18 & 42 \\
7 & CH\({}_{3}\)COCH\({}_{3}\) & 120 & 18 & 66 \\
8 & EtOH & 120 & 18 & 90 \\
9 & MeOH & 120 & 18 & 91 \\
10 \({}^{\circ}\) & MeOH & 120 & 18 & 84 \\
11 & MeOH & 110 & 18 & 60 \\
12 & MeOH & 130 & 18 & 90 \\
13 & MeOH & 120 & 14 & 80 \\
14 & MeOH & 120 & 16 & 88 \\
15 & MeOH & 120 & 20 & 90 \\ \hline \end{tabular} \({}^{\mathrm{a}}\) Reaction conditions: phenylacetylene (2 mmol), SnCl\({}_{4}\cdot\)5H\({}_{2}\)O (15 mol %), solvent (1 ml); \({}^{\mathrm{b}}\) GC yield;  
\({}^{\mathrm{c}}\) 2 mmol H\({}_{2}\)O was added in the reaction mixture.  
\end{table}
Table 2: Optimization of solvents and reaction conditions for the hydration of phenylacetylene \({}^{\mathrm{a}}\).  
In this work, the effect of reaction temperature on the hydration of alkyne was evaluated. When the reaction temperature was decreased to 110 \({}^{\circ}\)C, the yield of acetophenone (2a) was decreased to 60% (Table 2, entry 11). Increasing the reaction temperature to 130 \({}^{\circ}\)C did not improve the yield of acetophenone (2a), which indicates that 120 \({}^{\circ}\)C was the optimal reaction temperature (Table 2, entry 12). The yield of acetophenone (2a) was increased with prolonging the reaction time, but the yield was not further improved when the reaction time was increased more than 18 h. The acetophenone (2a) was yield to 90% when the reaction time was prolonged to 20 h (Table 2, entry 15).
###### Abstract  
Silicon photonics is one of the most prominent technology platforms for integrated photonics and can support a wide variety of applications. As we move towards a mature industrial core technology, we present the integration of silicon nitride (SiN) material to extend the capabilities of our silicon photonics platform. Depending on the application being targeted, we have developed several integration strategies for the incorporation of SiN. We present these processes, as well as key components for dedicated applications. In particular, we present the use of SiN for athermal multiplexing in optical transceivers for datacom applications, the nonlinear generation of frequency combs in SiN micro-resonators for ultra-high data rate transmission, spectroscopy or metrology applications and the use of SiN to realize optical phased arrays in the 800-1000 nm wavelength range for Light Detection And Ranging (LIDAR) applications. These functionalities are demonstrated using a 200 mm complementary metal-oxide-semiconductor (CMOS)-compatible pilot line, showing the versatility and scalability of the Si-SiN platform.  
silicon photonics; silicon nitride; transceiver; multiplexing; grating coupler; Coarse Wavelength Division Multiplexing (CWDM); LIDAR; optical phased array; beam steering; frequency comb; Kerr nonlinearity +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Short Overview of Reliability Methods
#### 2.5.4 Simplified FORM  
The calculation procedure, according to the Simplified FORM method used in this paper, is described in detail in [2]. It consists of three main parts. The first part is an iterative procedure in which the value of the reliability index is assumed, to calculate the reduced values of parameters. The factor of safety is then calculated using the reduced values. The procedure is repeated until the value \(\beta\) for which FS = 1 is found. In the second step, the change of FS for a given change in each set of random variables is determined. Each variable value from the set of values obtained in the final iteration of the first step is increased or decreased by 10%. The variable values related to capacity are increased, while the ones related to demand are reduced [1]. The gradients obtained are then used to calculate the coefficient \(\alpha\), which is the base for modifying the variable values used in step 3. The final, third step is an iterative procedure similar to the first step, with the intention of finding \(\beta\) for which FS = 1 applies. The difference from the first step is that the input parameter values were calculated using the \(\alpha\) value calculated in the previous step. The resulting \(\beta\) represents the reliability index. Iterative procedures from steps one and three stop when the following criteria are satisfied: \(\left|\text{FS}-1<10^{-4}\right|\).
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 3 Results
### Validation the FGO DOP Formula in a Fixed Constellation Case  
When considering several consecutive epochs at a high rate (typically higher than 0.1 Hz), the constellation geometry can be considered as fixed over a window of a few tens of epochs. In the case of an open sky receiver, the snapshot DOP will typically remain constant, except when one satellite appear or disappear in the antenna's field of view.  
The considered scenario is the nominal GPS constellation from IS-GPS-200M seen by a fixed receiver in Toulouse on the 1 st of January 2023 at 00:00. Figure 1(a,b) shows the batch HDOP for all epochs for a mix of factor graph length \(K\) and motion to observation uncertainty ratio \(q/\sigma\). The HDOP value computed with the formula is close to the exact HDOP value, especially for low values of \(q/\sigma\).  
On the general shape of the batch DOP, we can observe that the DOP is lower in the middle of the graph. This is due to the fact that all surrounding epochs contribute to the accuracy at a particular epoch. Secondly, the overall accuracy depends on the value of \(q/\sigma\): the lower \(q/\sigma\) is, the better the accuracy is. Finally, for larger \(q/\sigma\), the DOP usually reaches a floor value for the central part of the trajectory that is common for different values of \(K\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Factor Graph Length**} & \multicolumn{2}{c}{**Motion to**} & \multirow{2}{*}{**Max HDOP**} & \multirow{2}{*}{**Max HDOP Ratio**} \\  & \(K\) & & & \\  & & & \(q/\sigma\) & \\ \hline fixed geometry & \([10,50]\) & \([0.01,0.1,0.5,1]\) & 0.008 & 1 \% \\ \hline varying geometry & 50 & \([0.01,0.1,0.5,1]\) & 0.02 & 3 \% \\ \hline urban scenario & 1009 & \([0.01,0.1,0.5]\) & 0.15 & 22 \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: HDOP difference and ratio between proposed formula and exact computation.  
Figure 2: HDOP comparison for fixed (**a,b**) and time-varying (**c**) constellation geometry.  
This points towards the fact that increasing the factor graph length may not result in better accuracy once the floor has been reached.