esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Biomarker Characteristics  
Biomarkers are organic compounds that undergo little or no change during the organic matter evolution process. They have unique and stable carbon skeletons, preserving the molecular structures of the original biological parent material. Due to their stable structural characteristics, biomarkers are widely used as indicators for evaluating parent material type, depositional environment, maturity, and other factors [53]. The chromatographic-mass spectrometry results of the Tamulangou Formation source rocks show that the distribution of alkanes is unimodal, with carbon numbers ranging from nC\({}_{13}\) to nC\({}_{35}\). The main peak  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Depth (m)** & **Lithology** & **Asphalenes** & **Saturated** & **Aromatic** & **Non** & **Non Hydrocarbon** \\  & & & & **Hydrocarbon** & **Hydrocarbon** & **Hydrocarbon** & **+ Asphaltenes** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: Compositional analysis data of the source rocks in the Jurassic Tamulangou Formation, Hongqi Depression.  
occurs at nC\({}_{19}\), with smaller peaks at nC\({}_{21}\) and nC\({}_{23}\), indicating the input of both lower aquatic organisms and terrestrial higher plants. The Pr/Ph ratio ranges from 0.69 to 1.39, and hopane characteristics show that Ts > Tm, with a notable peak in C\({}_{30}\) hopane, suggesting that the oil-generating parent material was deposited in a mixed organic matter-dominated, weakly oxidizing to weakly reducing transitional environment. The stable carbon isotope \(\delta_{13}\)C in the mudstone ranges from \(-27.52\)% to \(-21.37\)%, indicating that the organic matter is primarily derived from lacustrine planktonic algae. The Pr/nC\({}_{17}\)-Ph/nC\({}_{18}\) relationship further shows that the oil-generating parent material has mixed input characteristics from both algae and terrestrial higher plants (Figure 5).
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 3 Methodology and Results
### Operational Parameters
#### 3.2.5 Mass of Gas  
A part of the hydrogen stored (base gas or cushion gas) must always remain in the cavity in order to maintain the minimum operating pressure that ensures its stability. The rest of the stored hydrogen (working gas) can be withdrawn when required for consumption. From the base mass and total mass of stored gas, the working mass of the cavern can be calculated (Equation (10)).  
\[\mathrm{m}_{\mathrm{work}}=\mathrm{m}_{\mathrm{t}}-\mathrm{m}_{\mathrm{base} }=\mathrm{V}\times(\rho_{\mathrm{max}}-\rho_{\mathrm{min}}) \tag{10}\]  
The result obtained is 3.5 and 2.3 million kilograms of working gas and base gas, respectively, totaling 5.8 million kilograms of stored hydrogen. The obtained amount of base gas with respect to the total is 39.6%, a value that is within the range considered by other authors, i.e., 1/3-2/5 [7, 11, 28].
## 7 Discussion
The electrodes have been applied in both bipolar and tetrapolar configurations to determine the impedance of DMEM growth media. It showed a constant impedance profile through a wide range of frequencies, although there were some deviations in the low-frequency region. This shows that the system does not introduce any disturbances due to frequency variation and can be used reliably for bioimpedance measurements. A wide variety of electrode systems are used for the electrical intervention of cell cultures. Systems involving direct stimulation mechanisms use electrodes made of noble materials like gold, silver, carbon, stainless steel, etc., in the form of plates or wires. The plates or wires are submerged in the biological culture setup, where the entire surface in contact with the culture provides stimulation. As the surface in contact depends heavily upon the volume of culture media in the wells, the current density and electric field distribution through the media may vary for various volumes, although the injecting current remains the same. This work demonstrates that electrodes with smaller surface areas provide higher current density and targeted electric field distribution (when compared to larger ones) while keeping the injection current constant. A commonly used reliable electrode setup is the Ag/AgCl electrode. However, when comparing the costs of Ag/AgCl electrodes for multi-well multi-electrode stimulation, the proposed setup fabricated in PCB technology gives a low-cost alternative for such traditional setups. In comparison to the commercially available design for multi-well electro-biomodulation (C-Dish(tm)), which provides only the bipolar configuration, the proposed setup can be altered between bipolar and tetrapolar configurations, adding versatility. Moreover, as the proposed system is designed using computer-aided software for designing PCBs, the electrode separations and dimensions can be replicated with high accuracy, giving a good grasp of experimental repeatability. Hence, when using comparative analysis, the proposed system provides a low-cost, easy-to-fabricate, configurable, and scalable electrical bio-interfacing system for multi-well in vitro cell culture experiments.
## 3 Results and Discussion
It has been accepted that injection of charge carriers into PbS CQDs activates the reactions (O\({}_{2}\)+2H\({}_{2}\)O+4e\({}^{-}\)\(\leftrightarrow\)4 OH\({}^{-}\) and O\({}_{2}\)+4H\({}^{+}\)+4e\({}^{-}\)\(\leftrightarrow\)2H\({}_{2}\)O) associated with dynamic traps, causing a reduction of the mobile carrier density by depleting carriers through production of hydroxyl and hydrogen ions [51; 52; 53; 54]. As mentioned before, indeed, the time scale of the current decay in the EL measurement is consistent with a long charge retention time on the order of minutes associated with dynamic trap formation, enabling us to infer that luminescence quenching is caused by the presence of the dynamic traps on the PbS CQD surface. Figure 5: (**a**) Plots of EL intensity as a function of time wavelength for a PbS NIR QLED. (An applied voltage was 1.5 V during light emission.) (**b**) A plot of peak wavelength as a function of time. In addition to electric field-induced luminescence quenching due to dynamic trap formation, the electrical bias-stress effect is thought to decrease the EL intensity. Contribution of the bias stress effect to luminescence quenching is evidenced from the fact that the current decay in Figure 6a is well-matched with a stretched exponential function, \(I(t)=I_{o}exp(-(t/\tau)^{\beta})\), which has been extensively used to explain carrier trapping-induced bias stress effect as seen in the inset of Figure 6a. In the relation, \(I_{o}\) is the pre-exponential current, \(\tau\) and \(\beta\) are the time constant and dispersion parameter, respectively. A relatively long time constant (\(\tau\)) of 161 s is consistent with that of charge injection-induced dynamic trap formation. Importantly, the current decay during the increase in the EL intensity in Figure 2b is not well fitted to the stretched exponential function, clarifying that two different mechanisms compete to determine the EL intensity. It is noted that the contribution of the Joule-heating effect on the EL intensity is not substantial because blue-shift was suppressed. In elaborating on the spectral shift during EL intensity variation, transition in the emission zone should be considered. Temporal blue-shift until 2 min can be interpreted that electron-hole recombination zone moved from the ZnO/PbS interface with an energy difference of 0.8 eV between the LUMO of the ZnO (-4.3 eV) and the HOMO of the PbS CQD (-5.1 eV) to the PbS CQD with a band gap of 0.9 eV. To elucidate the competing mechanisms, more controlled experiments in combination with transient absorption and luminescence measurements are required.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 2 Related Work  
YOLO has proven efficient in several real-time object identification applications, including word and character recognition, owing to its high speed and precision. It has been frequently utilized in languages that pose issues comparable to Bangla, including Arabic, Chinese, and Hindi.
## 2 Materials and Methods
### Software Design for Terminal Control System
#### 2.3.1 Data Acquisition and Control  
Data acquisition and control is a key component of the corn planter variable measurement and control system software, which mainly completes the collection of various data information such as seeding quality, seed-fertilizer box level, GPS information, etc. The communication methods between external devices and the interactive terminal of the system mainly include RS232 serial communication and CAN bus communication. Seeding quality information and vehicle traveling speed information are communicated with the interactive terminal via the CAN bus. Based on the loaded prescription information, current GPS information, and implement parameters, the measurement and control system determines the desired seeding and fertilization speed and sends it to the control unit via the CAN bus to control the seeding and fertilizing motors to operate at the desired speed.
## 3 Results
### Spectral Composition Strategies with Tunable Spectral Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{B:Y Ratio \textgreater{} 1} & \multicolumn{6}{c}{B:Y Ratio \textless{} 1} \\ \hline
**CCT (K)** & 3000 & 4000 & 5000 & 6000 & 7000 & 3000 & 4000 & 5000 & 6000 & 7000 \\ \(\lambda_{2}\) **(nm)** & 578 & 564 & 551 & 538 & 529 & 588 & 575 & 557 & 546 & 537 \\ \(\Delta\lambda_{2}\) **(nm)** & 105 & 108 & 101 & 103 & 100 & 100 & 100 & 105 & 100 & 102 \\
**B:Y ratio** & 1:0.98 & 1:0.94 & 1:0.96 & 1:1 & 1:1 & 0.3:1 & 0.3:1 & 0.3:1 & 0.32:1 & 0.34:1 \\
**MER (blm/W)** & 146.9 & 190.4 & 239.4 & 299.1 & 346.5 & 140.8 & 207.9 & 280.6 & 337.8 & 385.0 \\
**LER (lm/W)** & 331.4 & 362.1 & 389.6 & 390.0 & 382.4 & 414.2 & 443.4 & 444.8 & 439.0 & 412.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spectral characteristics under LER-maximizing strategy from 3000 to 7000 K.  
Figure 4: Spectra under different strategies: LER-maximizing strategy with (**a**) B:Y ratio \(>1\) and (**b**) B:Y ratio \(<1\).
# Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning
## 2 Related Work  
### Intelligent Vulnerability Detection  
To detect vulnerability automatically, researchers have proposed several approaches. [PERSON] [18] proposed an anomaly detection method for taint-style vulnerabilities. It clusters the initialization of variables that can be propagated to security-sensitive functions. Then, the violation is reported as potential vulnerabilities by anomaly detection. This approach is suitable for taint-style vulnerability but not for universal vulnerability. [PERSON] [19] proposed a vulnerability detection method based on similarity. However, this method is limited to vulnerabilities caused by code cloning. [PERSON] [20] proposed a representation learning method with file-level granularity. It extracts three types of nodes from the abstract syntax tree (AST) of a file: declaration, control-flow and invocation. A deep belief network (DBN) is used to learn advanced features. The information extracted by this method is too coarse to detect vulnerabilities caused by improper use of variables. [PERSON] [21,22] and [PERSON] [23] proposed an AST-based intra-procedural representation learning method to detect vulnerabilities. However, they both suffer from coarse detection granularity. In addition, their performances are compromised by the presence of irrelevant codes. [PERSON] [24] proposed an anomaly detection method based on static analysis. He converts the program slice to an AST and uses a hash algorithm to encode the AST. [PERSON] [25,26] proposed a detection method that learns vulnerable programming patterns with token granularity. A classifier is trained using the embedded samples directly without representation learning. Furthermore, its embedding and classification model are learned from the same dataset. This limits its performance. To solve the problem of information loss in the process of representation learning, [PERSON] [27] proposed a method that used the graph neural network for vulnerability detection with function-level granularity. The samples are converted into the form of code property graph. Then a graph neural network that is composed of a gated graph recurrent layer and a convolutional layer is trained to learn the vulnerable programming pattern. This approach improves the detection of intra-procedural vulnerabilities. However, it fails to cover inter-procedural vulnerabilities.
## 2 Method
### Multichannel Microphone Array and Its Functionalities Post Processing  
Among different configurations of arrays (planar, linear, etc.), the spherical type has been used for this case study and identified in the em32 Eigenmike(r), which is equipped with 32 channels, uniformly distributed on the surface of a sphere. The advantage of the spherical array is that the capsules are equally sensitive to the arrival of sound waves from all direction due to their physical arrangement [20; 21]. This is the methodology that different manufacturers have adopted for their production. Additionally, the number of capsules is the main characteristic that determines the synthesis of directivity; the larger the number of capsules that make up the array, the better the reconstruction of the sound field, since it covers as many angles as possible [22]. In technical language, this process is called beamforming [23].  
As with any other microphone array, the beamforming can be achieved by the convolution of the numerical inversion of the IR measured in an anechoic chamber and the use of finite impulse response (FIR) filters in a time domain that synthesizes the real microphone into a virtual microphone [24]. Specifically, each virtual microphone must sum the results of the convolution of 32 input channels with 32 FIR filters. A 32 \(\times\) 32 filter matrix can be used to convert the signal coming from the 32 transducers into 32 spatial PCM sampling (SPS) signals, whilst a 32 \(\times\) 16 filter matrix produces signals of 3 rd order ambisonics (3 OA) [24]. The number of directions determined for the use of the em32 Eigenmike(r) has been established to be 362, which is considered an adequate oversampling for the spherical array microphone used for the acoustic measurements [25].  
To encode an audio signal in the time domain, pulse code modulation (PCM) can be used to convert the waveform of the analog signal into spatial pulses in a 2D plane characterized by the amplitude (\(y\)-axis) as a function of time (\(x\)-axis) [26]. Similarly, in the spherical spatial domain, the SPS decomposes the sound arriving at the microphone array into 32 virtual super-directive microphones and generates spatial pulses that homogeneously cover the sphere in a 3D space [27]. This parallelism is useful to also understand the calculation of the sum of products given in the time domain (2D) by sinusoidal oscillations through the application of FIR digital filters, while, in the spatial domain (3D), the spherical sound wave can be expressed as a sum of spherical harmonics though the pursuit of high-order ambisonics (HOA) [27].  
The discrete Fourier transform (DFT) can be employed to transform the overdetermined system into the frequency domain and can easily elaborate it. After this step, the matrix can be transformed again into the time domain to obtain a temporal structure of the sound.
# Cooperative Friendly Jamming Techniques for Drone-Based Mobile Secure Zone
## 5 Experiment
### Evaluation  
The results obtained from the simulations and the field experiments are as follows. Based on the ILR of eve1 and eve3 in Field Experiment Figure 4a, it was found that as the distance between the source node and the eavesdropper increases, the communication quality of the eavesdropper deteriorates.  
Furthermore, based on the ILR of eve5 and eve6 in Figure 4b, it was found that as the eavesdropper reaches closer to the friendly jamming drones, the communication quality of the eavesdropper deteriorates.  
A comparison of the average ILR of the eavesdroppers between Figure 4a-d shows the effect of the device mobility. It can be seen that when the source node moves to the receiving node to reduce the distance between the two devices and decreases the transmission signal strength, the communication quality of the eavesdroppers deteriorates.  
In Figure 4d, where the mobility of the devices and the friendly jamming technique are used together, the average ILR of the eavesdroppers is 0.03 in the simulation results and zero in the field experiment results. This means that the CFJ-DMZ model can reduce the communication quality of eavesdroppers to reduce the eavesdropping probability in the zone where the possibility of eavesdropping is high. Therefore, the communication security in the secure zone improves.
# Design and Optimization of Modular Solid Rocket Grain Matching Multi-Thrust Performance Curve
## 2 Design Methods  
### Grain Template  
Figure 1 shows a typical triple-modular grain, illustrating the concept of multi-modular grain. This triple-modular grain consisting of three individual segments, wherein each segment consists of a star grain and a slot grain. After ignition, all segments start to burn simultaneously. As the first segment burns out, the thrust or chamber pressure diminishes, transitioning to the second phase. Ultimately, the second segment burns out, resulting in a further reduction in thrust or chamber pressure, marking the transition to the third phase. In our design, all propellant segments form a single grain, which is housed within a single motor case and ignited simultaneously by a single igniter. Once ignition occurs, the entire burning surface is activated, and there is no need for any isolation mechanisms.  
Figure 1: Illustration of modular grain.  
The reason why we chose star grain and slot grain as the fundamental templates of the modular grain lies in their burning surface behavior. As shown in Figure 2, star grain exhibits a progressive burn behavior in most cases, whereas slot grain demonstrates a regressive burn behavior. By combining these two types of grain, a neutral burning behavior may be achieved. Notably, the burning surface of star grain exhibits convex functions over time, while slot grain exhibits concave functions. This implies that their combination has the potential to ensure a very consistent and smooth thrust or chamber pressure throughout each phase. For some circumstances, end-burning grain is also used to produce the long duration thrust phase.  
The recent progress in grain reverse design also reveals the advantages of star-slot combination [12]. This combination is also found in the boost stage of a multi-thrust motor [7]. Therefore, the geometry of this combination is very simple, making it a competitive candidate as the template of modular grains.  
It is also important to know that in the context of tactical missile, a multi-segmented grain sharing a single chamber is highly advantageous because it enables a more compact motor design, eliminates complex separation mechanisms, and avoids the need for multiple nozzles [7].
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 5 Renyi Entropy for 5G Signal Analysis
### Rationale Behind the Use of the Renyi Entropy for Drone Detection
In the case where calibration with the use of terminals is not possible, one should constantly verify the received signal and analyze the maximum value of entropy on an ongoing basis, indicating the use of a large number of resources. 2. Signal reception--the signal is received continuously in the same way as a typical passive radar. 3. Useful signal extraction--the signal is analyzed in terms of resource allocation by the 5G network. Only those fragments of the signal that meet the adopted condition for the Renyi entropy level are selected (e.g., signal frames with a filling exceeding 90% of the maximum entropy value). 4. Passive radar processing--classical target detection, as shown in Figure 2.
## 3 Methodology
### Malmquist Productivity Index (MPI)  
The Malmquist Productivity Index (MPI) [42] was developed to measure the changes in technological productivity and the improvement. To measure the change in technological productivity, several other indexes were introduced. An explanation of the method can be found in reference [10]. Equation (2) shows the output-based MPI defined in [PERSON] et al. [41].  
\[MPI=\left[\frac{d_{o}^{s}(x_{t},y_{t})}{d_{o}^{s}(x_{s},y_{s})}\times\frac{d_{ o}^{t}(x_{t},y_{t})}{d_{o}^{t}(x_{s},y_{s})}\right]^{1/2} \tag{2}\]  
where \(d_{o}^{s}\) is a distance function measuring the efficiency of conversion of inputs \(x_{s}\) to outputs \(y_{s}\) in the period \(s\). DEA efficiency is considered a distance measure because it reflects the efficiency of converting inputs to outputs. If there is a technological change in period \(t\), then,  
\[d_{o}^{t}(x_{s},y_{s})\text{= Efficiency of conversion of input in period s to output in period }s\
eq d_{o}^{s}(x_{s},y_{s})\]  
MPI is defined as a geometric average of the efficiency change and technological change in the two periods \(s\) and \(t\). According to [PERSON] et al. (1994) [43], the MPI in Equation (2) can be written as Equation (3).  
\[MPI=\frac{d_{o}^{t}(x_{t},y_{t})}{d_{o}^{s}(x_{s},y_{s})}\left[\frac{d_{o}^{s} (x_{s},y_{s})}{d_{o}^{t}(x_{s},y_{s})}\times\frac{d_{o}^{s}(x_{t},y_{t})}{d_{o }^{s}(x_{t},y_{t})}\right]^{1/2}\text{= Efficiency change }\times\text{Technical change} \tag{3}\]  
MPI can be used to measure overall productivity of DMU over time. If MPI \(>1\), it indicates productivity improvement for the DMU; if MPI \(=1\), it indicates no productivity change; and if MPI \(<1\), it means productivity recession. As MPI productivity change is a multiplicative composite of efficiency and technological changes, the change in productivity comes from either an efficiency change, a technological change, or both.