esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring ISAC: Information-Theoretic Insights
## 8 Conclusions and Future Research Direction
In conclusion, the convergence of sensing and communication in ISAC systems promises significant advancements in both fields, but it also presents new technical challenges. As highlighted in [39], these challenges span multiple domains and require a comprehensive and interdisciplinary approach. Addressing these challenges will require innovative adaptations of existing models and the development of new schemes that harmonize communication, sensing, and security requirements in increasingly complex environments. One notable direction is the combination of artificial intelligence (AI) and ISAC, which has been extensively discussed in [125; 126]. This integration demands not only algorithmic innovation but also a rigorous theoretical foundation. The information-theoretic literature has proved extremely successful in tackling first standard ISAC models, and one can hope that it will also provide a fertile background using these advanced theoretical foundations. The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. This research is supported by the PEPR IA project FOUNDRY (ANR-23-PEIA-0003), Chaire SeqALO (ANR-20-CHIA-0020-01), ERC (101125691) and German Research Foundation (DFG) via the German-Israeli Project Cooperation (DIP) (SH 1937/1-1). No new data were created or analyzed in this study. The work of [PERSON] is supported by the PEPR IA project FOUNDRY (ANR-23-PEIA-0003), and the author would like to thank Chaire [PERSON] (ANR-20-CHIA-0020-01) for his support. [PERSON] acknowledges funding from the ERC under grant agreement 101125691. The work of [PERSON] was supported by the German Research Foundation (DFG) via the German-Israeli Project Cooperation (DIP), under Project SH 1937/1-1. The authors declare no conflicts of interest.
## 3 Results and Discussion
### Adsorption Kinetic Study  
The different factors of kinetic models for phosphate adsorption on iron-Z-A are displayed in Table 4. To investigate the mechanism behind phosphate adsorption on iron-Z-A, we performed kinetic analysis using the first-order and second-order of pseudo-kinetic models. The pseudo-second-order kinetic model showed a more desirable linear relationship as shown in Figure 7. The order of the reaction was determined by log plotting. The inverse plot obtained for second-order reaction is being shown in the Supplementary Figure S6.  
The experimental data of phosphate onto iron-Z-A fits better in the pseudo-second-order kinetic model, which is illustrated in Table 4. It is also indicated that the chemisorption process between the adsorbent and phosphate can be a rate-limiting step by sharing or exchange of electrons [25; 45; 46]. Besides, it meant that the adsorbate's amount depends on the adsorbent's surface area regardless of the concentration [35].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kinetic Models** & \(\mathbf{R}^{2}\) & \(\mathbf{Qe}\) & \(\mathbf{k_{1}\) \& \(\mathbf{k_{2}}\)} \\ \hline
**Pseudo-first-order model** & 0.90 & \(-\)42.73 & 0.023 (1/min) \\
**Pseudo-second-order model** & 0.99 & 2.03 & 0.23 (g/mg min) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Illustration of iron-Z-A’s kinetic parameters for adsorption of phosphate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**[PERSON]**} & \multicolumn{4}{c}{**[PERSON]**} \\ \hline \(\mathbf{K_{L}}\) (L/mg) & \(\mathbf{Q_{\mathsf{m}}}\) & \(\mathbf{R}^{2}\) & \(\mathbf{K_{F}}\) (mg/g)/(mg/L)\({}^{1/2}\) & \(\mathbf{n}\) & \(\mathbf{R}^{2}\) \\ \hline \(-\)2.51 & 2.16 & 0.92 & 134.62 & \(-\)0.29 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of constants of [PERSON]’s isotherm models for phosphate adsorption by iron-Z-A.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metal Ion** & **Adsorbed Phosphate/Adsorbent (mg/g)** \\ \hline
**Fe** & 382.296 mg \(\mathrm{PO}_{4}\)[3]–g Fe \\
**Al** & 56.296 mg \(\mathrm{PO}_{4}\)[3]–g Al \\ \hline \hline \end{tabular}
\end{table}
Table 3: Illustration of the amount of adsorbed phosphate on different metal compounds of adsorbent in mg/g.
## 6 Future Prospects and Outlook for Grain-Drying Machine Control Systems
### Advanced Technology Integration and Intelligent Automation Sector
#### 6.1.3 Improvement of High-Precision Sensors and Data Processing Technology  
To ensure the precise control of the drying process, high-precision sensors and advanced data processing technologies will become key components of future grain dryer control systems. The improvement of sensor precision allows the system to more accurately monitor changes in grain quality, while data processing techniques such as filtering and smoothing can effectively reduce noise and errors, ensuring the reliability of the data.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
###### Abstract  
Activated carbon (AC) is one of the most used air purification materials with excellent adsorption capacity for volatile organic compounds (VOCs). In this work, Bi2O2 CO3 (BOC) nanomaterials, as a photocatalysis component, are grown on the surface of the AC to construct BOC/AC composites. The as-synthesized composites were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), and N2 adsorption/desorption measurements. The results demonstrate that flower-like BOC can be grown in situ on the surface of AC. The photocatalytic properties for the removal of gaseous toluene (50.0 ppm) in air over the BOC/AC composites were investigated under simulated sunlight illumination. The results show that the BOC/AC photocatalyst can effectively degrade toluene to CO2 and H2O, with more than 90% degradation in 3 h. The excellent photocatalytic performance of the BOC/AC composite catalyst can be ascribed to the synergistic effect of the adsorption ability of AC and photocatalytic activity of both BOC nanosheets and AC. This work may provide useful guidance for indoor air purification, particularly for harmful trace gases such as VOCs.  
Bi2O2 CO3; activated carbon; in situ; toluene removal; photocatalyst +
Footnote †: journal:  
irradiation [14; 15; 16; 17; 18]. Therefore, the introduction of narrow bandgap catalytic materials is highly necessary to expand the solar absorption spectrum to the visible light region [19; 20].  
Among all the visible light driven photocatalysts, layered structured Bi\({}_{2}\)O\({}_{2}\)CO\({}_{3}\) (BOC), one of the bismuth-containing Aurivillius compounds with alternative stacking of (Bi\({}_{2}\)O\({}_{2}\))\({}^{2+}\) thin layers separated by CO\({}_{3}\)\({}^{2-}\) groups [21; 22; 23], has been explored as a promising visible light active photocatalyst [24; 25; 26]. Furthermore, the layered structure can easily lead to the formation of 2D morphologies, such as sheet-/plate-like morphologies, which may result in excellent photocatalytic performance. In our previous work, we demonstrated the effective photocatalytic removal of toluene in air utilizing (002) oriented BOC nanosheets, which led to an almost complete degradation of toluene into CO\({}_{2}\) and H\({}_{2}\)O [27; 28]. These results demonstrate the promising potential of utilizing layered BOC nanostructures as photocatalyst for removing VOCs. In this work, we aim to combine the advantages of both AC and BOC as a composite catalyst for the removal of toluene in air by fabricating AC modified with BOC nanomaterials. On one hand, the AC can adsorb toluene effectively due to its large surface area. On the other hand, BOC nanostructures can function as photocatalyst for the in-situ degradation of toluene.
# A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles +
## 4 Simulation Results
### Hyper-Parameter of Deep Reinforcement Learning Network  
The implementation of the DRL network was carried out using Tensorflow [48] and integrated with the SUMO simulation environment through a Python interface. The training was conducted in episodes, with each episode consisting of 3600 time steps of 1 s each, totaling one hour per episode. The random seed for the vehicle simulation was changed in every episode. The critical hyper-parameters are listed in Table 2, with the values determined through a process of trial and error.
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 3 Link-Level Co-Operative Simulator
### Description of the Transmitter (Tx) and Receiver (Rx) mmWave Chains  
The mmWave radio transmitter chain in Figure 9 translates the signal from baseband frequency to FR2 band (26 GHz), after the application of the baseband algorithms to the transmitted data symbols at the NR-UE. The signal in the mmWave frequency band will be transmitted through the radio channel and the received signal will be converted, through  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(\text{h}_{\text{gN}}\) & 25 m \\ \(\text{h}_{\text{RN}}\) & 19.5 m \\ \(\text{d}_{\text{BL}}^{1}\) & 67 m \\ \(\text{d}_{\text{BL}}^{2}\) & 67.3 m \\ \(\text{d}_{\text{AL}}^{1}\) & 6.88 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distance of the simulation scenario.  
the mmWave radio receiver (FR2-Rx), into a baseband digital signal at the relay node. This architecture was developed based on that described in [18], and for its implementation we used off-the shelf subsystems.  
The uplink 5G waveform signal was applied to the input of a Mini-Circuits Gali84+ Amplifier [23], which was placed before the Hittite HMC292 ALC3B mixer [24]. To achieve Local Oscillator (LO) signals, we employed an Analog Devices ADF5355 Phase-Locked Loop (PLL) [25] and Hittite HMC576 LC3B [26] frequency doubler. We highlight that the PLL-VCO integrated a wideband microwave VCO design, which allows frequency operation from 6.8 to 13.6 GHz at one radio frequency output. After using the mixer, Hittite HMC499 LC4 [27] and Hittite HMC944 APM5E [28], respectively, and an mmWave medium power amplifier and power amplifier were used. The last subsystem in Figure 9 is the Pasternack PE9821/2 FWR-24 [29] waveguide horn antenna with 20 dB gain, which was employed at the output of the mmWave transmitter chain. In Figure 10, its block architecture can be observed, which has the same structure as the mmWave transmitter chain presented previously and employs equivalent components, except that power amplifier has been replaced by a Low Noise Amplifier (LNA), Hittite HMC519 LC4 [30]. It should be noted that the signal in the mmWave frequency band (26 GHz) transmitted by the NR-UE through the radio channel is received by the mmWave receiver chain in Figure 7 and will be converted into a baseband digital signal at the baseband stage in the relay node strategy.  
As mentioned above, the simulator was designed with interconnection of off-the-shelf components, allowing for easy characterization and integration into the transceiver (Rx-Tx) mmWave radio link-level co-operative simulator. The electrical specifications of the different RF subsystems are described in Table 2.  
Figure 10: Implementation architecture of the mmWave receiver chain (FR2-Rx).  
Figure 9: Implementation architecture of the mmWave transmitter chain (FR2-Tx).
## 4 Proposed Smart Schemes
### Development of Algorithms  
In the sequel, for each proposed method, we first adjust and select an appropriately fixed threshold for each lane. This is not only for removing noise but also for reducing the number of edged FFT side lobes within each lane. The edged FFT main lobe of a reflected vehicle signal within each lane is distinguishable from clutter, and is far above the threshold. The threshold for each lane is chosen adequately based on the average FFT magnitudes of environmental noises observed at the road site. More details about the threshold selection process can be found in Section 5.1.  
It is observed that in a normal condition, the FFT main lobe of a vehicle signal lies within a lane since a car is normally driven within a lane. Moreover, the magnitude peak of each corresponding FFT side lobe crossing lanes is insignificant in comparison with that of any FFT main lobe so that it can be removed after being edged by an adequately selected threshold. However, when the edged FFT main lobe of the vehicle signal may partially lie on the borderline of the two adjacent lanes, it is very likely that lane-crossing edged FFT side lobes may occur, e.g., Figure 5. To mitigate some effects of lane-crossing FFT side lobes adverse to vehicle flow detection, we propose the following lane information-aided test criteria and rules for developing our methods. As such, the proposed methods consist of some combinations of the proposed criteria and rules.  
**Borderline Test Criterion 1 (BTC1)**: A portion of an edged FFT main lobe lies on the borderline of two adjacent lanes.  
**Borderline Test Criterion 2 (BTC2)**: The magnitude peak of an edged FFT main lobe satisfying the BTC1 lies on the left-hand side of the borderline.  
**Rule 1**: Take the left lane width plus its adjacent \(1/2\) right lane width as the resulting left lane width and the remaining \(1/2\) right lane width as the resulting right lane width. This is to fictitiously expand the left lane, and at the same time reduce the right lane.  
**Rule 2**: Use Rule 1 with \"left\" replaced by \"right\" and vice versa. This is to fictitiously increase the right lane, and at the same time decrease the left lane.  
**Rule 3**: Set the portion of the edged FFT main lobe on the given borderline as zero. This is to avoid the repetitive satisfaction of BTC1 by the same given borderline.  
Figure 5: An example of the fast Fourier transforms (FFT) main lobe lying within the left lane and a FFT side lobe lying within the right lane.
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 7 Results
### Ablation Tests on Augmentation Methods  
In order to better understand the influence of different data augmentation methods on the overall performance, we execute ablation tests. We report system performance on all evaluation criteria for each of the methods in Table 5. Results suggest that each of these data augmentation methods contributes to overall performance. The time shift augmentation produces the most significant performance increment, and the Band Mask algorithm is  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Params (M)**} & \multicolumn{6}{c}{**Metric**} \\ \cline{3-8}  & & PESQ & CSIG & CBAK & COVL & SSNR & STOI \\ \hline Noisy & — & 1.97 & 3.35 & 2.44 & 2.63 & 1.69 & 92.10 \\ \hline SEGAN [19] & 294 & 2.16 & 3.48 & 2.94 & 2.79 & 7.66 & 93.12 \\ ISEGAN [30] & 294 & 2.24 & 3.23 & 2.93 & 2.68 & 8.19 & 93.29 \\ DSEGAN [30] & 513 & 2.35 & 3.56 & 3.10 & 2.94 & 8.70 & 93.25 \\ SASEGAN-avg [31] & 295 & 2.33 & 3.52 & 3.05 & 2.90 & 8.08 & 93.33 \\ SASEGAN-all [31] & 310 & 2.35 & 3.55 & 3.10 & 2.91 & 8.30 & 93.49 \\ \hline Sinc–SEGAN-sub & 91 & **2.37** & 3.55 & **3.13** & **2.97** & 8.68 & 93.40 \\ Sinc–SEGAN-add & 210 & **2.39** & **3.69** & **3.23** & **3.00** & **8.71** & **93.53** \\ Sinc–SEGAN-add & 210 & **2.86** & **3.87** & **3.66** & **3.15** & **8.87** & **94.96** \\ +augment & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on objective metrics of the proposed systems (Sinc–SEGANs) against previous SEGAN variants using the Valentini benchmark [39]. The unit of the number of parameters (Params) is million (M).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experiment** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **SSNR** & **STOI** \\ \hline A & 2.37 & 3.55 & 3.13 & 2.97 & 8.68 & 93.40 \\ B & 2.32 & 3.49 & 2.84 & 2.91 & 5.51 & 92.99 \\ C & 2.40 & 3.46 & 3.07 & 2.89 & 8.66 & 93.39 \\ D & 2.36 & 3.57 & 3.07 & 2.94 & 8.70 & 93.37 \\ E & 2.39 & 3.69 & 3.23 & 3.00 & 8.71 & 93 the second. Surprisingly, ReMix augmentation only shows a limited contribution to the overall performance.
## 3 Results and Discussion
### Effects of Domestic Cooking on Changes in the Contents of VOCs of Faba Leaves
#### 3.4.1 Alcohols  
Alcohols (72.02%), the representative VOCs in fresh faba leaves, were significantly reduced by domestic cooking: by 10.37% for roasting, by 5.24% for microwaving, by 4.50% for steaming, and by 1.65% for boiling (Figure 5). Of the alcohols (Figure 4, Table S2), 1-octen-3-ol (23.55%), 1-penten-3-ol (13.43%), 3-hexen-1-ol (9.10%), 2-penten-1-ol (7.29%), 2-hexen-1-ol (5.54%), benzyl alcohol (3.15%), 1-hexanol (2.52%), and ethanol (2.11%) contributed more than 90% of the total alcohols content, which are compounds that are widely reported to contribute to the faba leaf aroma [33; 34; 35]. Moreover, 1-octen-3-ol, which is known to contribute to the smell of mushrooms, has been reported as a key volatile compound in faba flours [35]. Additionally, 1-penten-3-ol, 3-hexen-1-ol, 2-penten-1-ol, and 2-hexen-1-ol have been suggested as being pungent, as well as possessing the flavors of grass, greens, leaves, and so on [36], which are traits commonly known to unfavorable volatiles. The removal of these VOCs by domestic cooking can be considered to be an important method for improving the flavor of faba leaves. In particular, the content of a synthetic compound, butylated hydroxytoluene (BHT), detected in our analysis (Figure 4, Table S2), increased in the cooked samples of faba leaves. It is therefore important to point out the two sides of the HS-SPME technology: its convenience for analyzing VOCs, in addition to its potential for contaminating samples caused by the fiber coatings [37].
# Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review
## 4 Results
### Artificial Intelligence/Machine Learning
#### 4.1.4 Artificial Immunity--Rule-Based Machine Learning  
A quantitative, immune-based dynamic risk control model for network security, consisting of three different levels (user, processing, hardware), was introduced by [PERSON] et al. [37]. The risk assessment module, the intrusion detection module, and the dynamic risk control module were located at the processing layer. The first module was responsible for estimating damage in a computer network or a system according to the threat level and the vulnerabilities of the system. It was based on artificial immune theory, according to which each attack category is simulated as an immune memory cell, and the antibody concentration value of the immune memory cells is calculated according to the intrusion detection results. The risk assessment relies on the antibody concentration of immunological memory cells, which, in turn, is influenced by detected data packages. When the system is under attack, the concentration value is increasing and when the detection results are normal, the concentration value is decreasing in real time. Experiments validated that the antibody concentration and risk values increased while the attack was continuing.  
Another immune-based DRA method for digital virtual assets was proposed by [PERSON] et al. [38]. It comprises four stages: data collection and pre-processing, immune detector training, antibody cell concentration, attack hazard value evaluation and threat risk assessment. Based on the hierarchical division negative selection algorithm (HD-NSA), the immune detector component identifies attacks or illegal behaviours on digital virtual assets. The detected attacks are then categorised according to the taxonomy of digital virtual asset attacks. By simulating the mechanism of antibody concentration alteration, the threat to digital virtual assets is assessed upon the discovery of an attack. Immune memory cell formation increases during active attacks and decreases as the attack intensity diminishes. The final risk value is determined based on both antibody cell concentration and the evaluation of the attack. The authors used data from a Bitcoin dusting attack in order to show that the proposed method had the capability to rapidly and precisely detect attacks, while concurrently evaluating the real-time risk of various users being attacked.
# Concrete Gas Permeability: Implications for Hydrogen Storage Applications
## 2 Experimental Development
### Permeability Calculation  
The permeability coefficient for each specimen tested was calculated. With these values, an adjustment was made, and Equation (4) shows the fit equation obtained:  
\[k=k_{\infty}\bigg{(}1+\frac{b}{\frac{(p+p_{a})}{2}}\bigg{)}=k_{\infty}\bigg{(} 1+\frac{b}{p_{av}}\bigg{)} \tag{4}\]  
where:  
\(k\infty\) is the permeability when the pressure is sufficiently high (m\({}^{2}\));  
\(b\) is an adjustment parameter, expressed as a non-dimensional parameter;  
\(p\) is the pressure applied during the test (N/m\({}^{2}\));  
\(p_{a}\) is the atmospheric pressure (N/m\({}^{2}\));  
\(Pav\) is the average pressure (N/m\({}^{2}\)).  
To predict the behavior of a tank wall based on the permeability coefficients obtained with the test, a Finite Element Model (FEM) has been created using the Comsol Multiphysics v. 6.1 program, and the simulation considers the flow of hydrogen in concrete following [PERSON]'s law. Equations (5) and (6) are used in the FEM model:  
\[\frac{\partial}{\partial t}(\epsilon pp)+\
abla(pu)=Qm \tag{5}\]  
\[u=-\frac{k}{\mu}\
abla p \tag{6}\]  
where:  
\(\epsilon_{\rm p}\) is the porosity of the concrete, expressed as a non-dimensional parameter;  
\(\rho\) is the gas density (kg/m\({}^{3}\));  
\(k\) is the permeability, experimentally obtained (m\({}^{2}\));  
\(u\) is the flow velocity of the gas (m/s);\(\mu\) is the gas viscosity (N\(\cdot\)s/m\({}^{2}\)).  
Since one of the main input parameters in the model is permeability, the higher permeability value has been selected for each of the studied conditions. The results of both concretes have been grouped together since their behavioral difference is not significant compared to their moisture content.  
A one-dimensional (1D) model has been constructed in order to take advantage of the system's symmetry, with the objective of replicating the behavior of a wall. The material data incorporated into the Darcy equation have been derived from the results of the tests, as illustrated below. The domain has been divided into elements of approximately 1 mm in size, with quadratic discretization. A parametric study was conducted for each simulated material, in which both the wall thickness (ranging from 5 cm to 100 cm, with a step size of 5 cm) and the internal pressure (ranging from 5 bar to 100 bar, with a step size of 5 bar) were varied. The calculations were performed in a steady state using the PARDISO solver. The model's output data included the gas flows and the pressure profile within the domain.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 3 System Model and Mechanism
### Decoding Algorithm of LLM-SC
Beam search offers a feasible, though suboptimal, approach leveraging the capabilities of LLMs. The specific decoding process is illustrated in Figure 1. Algorithm 1 details beam search decoding, where  
* Beam size (\(K\)): Controls the trade-off between accuracy (higher \(K\)) and complexity;
* Target tokens (\(t\)): Predefined based on transmitted sequence length;
* Noise PSD (\(N_{0}\)): Scales the semantic prior term in Equation (20). The output \(W\) is the highest-scoring token sequence after \(t\) iterations. ```
Input:\(O\): Received symbols from wireless channel \(K\): beam size \(t\): the number of target decoding tokens \(N_{0}\): PSD of noise Output:\(W\) : Decoding tokens
1. \(B_{0}\leftarrow\{<0,>\}\)
2. for\(i\in\{0,\ldots,t-1\}\) :
3. \(B\leftarrow\infty\)
4. for\(<\text{\em socre},\textbf{y}>\in B_{i}-1\)
5. for\(s\in\mathcal{S}\)
6. \(\text{\em socre}\gets N_{0}ln(P(s|\textbf{y}))-||o_{i}-h_{i}\otimes s||^{2}\)
7. \(B.add(<\text{\em socre},\textbf{y}\circ s>)\)
8. \(B_{t}\gets B.top(K)\)
9. return\(B.max()\)
```  
**Algorithm 1** Beam search for text decoding