esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios  
Three primary application scenarios have emerged in the era of 5G: ultra-reliable low-latency communication (uRLLC), massive machine-type communication (mMTC), and enhanced mobile broadband (eMBB). These scenarios are designed to meet specific needs including large connection density, high data rates and capacity, and low latency and high dependability. Various papers have outlined expectations for 6G [35; 36; 37] application scenarios, as depicted in Figure 5. The industry has proposed various 6G applications, each with unique KPIs. While these scenarios suggest the need for classification like in 5G, most studies lack detailed specifics, as shown in Table 1.  
In the evolution of 6G, there will be a continued enhancement and expansion of the existing application scenarios, aiming to achieve further enhancements in enhanced mobile broadband (feMBB), ultra-massive machine-type communication (umMTC), extremely low-power communication (ELPC), long-distance high-mobility communication (LDHMC), and enhanced ultra-reliable low-latency communication (euRLLC). In addition to meeting traditional KPIs similar to data throughput, delay in the communication systems, and connection density by 2030, these scenarios will also bring new KPIs such as intelligence levels, security capabilities, sensing, imaging, and location. Furthermore, new application scenarios for 6G are anticipated to emerge as technologies advance and integrate, combining elements of multiple scenarios. Apart from the three cases that 5G reinforced, 6G is expected to introduce new application scenarios: dense-scene communications; mobile broadband reliable and low-latency communication (MBRLLC) for scenarios with high data rates, large bandwidth, low latency, and high reliability; and applications in smart transportation, smart factories, and industrial IoT. Massive enhanced mobile broadband (meMBB) for high data rates, large bandwidth, and connection density will address extreme user demand in urban mega-cities, stadiums, and other densely populated areas. Figure 5: The overall features of 6G networks. The features are improved relative to 5G.
# A Modified Quad Q Network Algorithm for Predicting Resource Management
###### Abstract  
As the resource management systems continues to grow, the resource distribution system is expected to expand steadily. The demand response system enables producers to reduce the consumption costs of an enterprise during fluctuating periods in order balance the supply grid and resell the remaining resources of the product to generate revenue. Q-learning, a reinforcement learning algorithm based on a resource distribution compensation mechanism, is used to make optimal decisions to schedule the operation of smart factory appliances. In this paper, we proposed an effective resource management system for enterprise demand response using a Quad Q Network algorithm. The proposed algorithm is based on a Deep Q Network algorithm that directly integrates supply-demand inputs into control logic and employs fuzzy inference as a reward mechanism. In addition to using uses the Compare Optimizer method to reduce the loss value of the proposed Q Network Algorithm, Quad Q Network also maintains a high accuracy with fewer epochs. The proposed algorithm was applied to market capitalization data obtained from Google and Apple. Also, we verified that the Compare Optimizer used in Quad Q Network derives the minimum loss value through the double operation of Double Q value.  
demand response; resource management system; smart factory; smart appliances; reinforcement learning; Q-learning; fuzzy logic; Quad Q Network
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 4 Challenges and Future Directions
### Fabrication Costs  
The fabrication cost of graphene is approximately USD 1-2 per cm\({}^{2}\)[84]. The market cost, however, is much higher, often reaching USD 10-20 per cm\({}^{2}\) on the graphene supermarket. In comparison, quantum dot synthesis through colloidal methods typically ranges from USD 11 to 73 per g of material [85], although achieving a uniform particle size distribution remains a technical challenge [86]. Neuromorphic sensor components, particularly memristive devices produced via atomic layer deposition (ALD), demand advanced deposition systems and precision lithography [87], leading to quickly intractable estimates for cost of production [88].
## 3 Experimental Results and Analysis
### Normal Displacement  
Figure 4 shows the non-persistent joint normal displacement-shear displacement curve. The normal displacement curve is analyzed according to the four stages of the shear stress-shear displacement curve. In the elastic stage, the normal displacement first decreases slightly and then slowly increases with the increase of shear displacement. In the damage stage, as the shear displacement increases, the growth rate of the normal displacement increases to a peak point. In the strength recovery stage, the normal displacement reaches a peak and then suddenly reaches a certain level. In the residual stage, the normal displacement still increases or decreases, but the overall rate of change is reduced compared to the damage stage.  
In order to intuitively analyze the normal displacement in the shearing process, the normal displacement value at the peak point of the curve is designated as the peak normal displacement (\(\delta_{\rm p}\)), while the normal displacement associated with the maximum shear displacement is defined as the final normal displacement (\(\delta_{\rm f}\)). The peak and final normal displacements with various factors are displayed in Figure 5.  
Figure 5a indicates that as \(b\) increases from 0.5 mm to 2 mm, \(\delta_{\rm p}\) decreases by 10.89% and 21.78%, respectively, while \(\delta_{\rm f}\) changes insignificantly. Figure 5b shows that as \(k\) rises from 20% to 40%, \(\delta_{\rm p}\) changes slightly, while \(\delta_{\rm f}\) diminishes by 24.59% and 52.63%, respectively. As \(\sigma_{\rm n}\) grows from 1 MPa to 5 MPa, \(\delta_{\rm p}\) drops by 31.30%, 51.91%, and 74.05%, while \(\delta_{\rm f}\) decreases by 47.41%, 68.10%, and 111.21%, as shown in Figure 5c. According to the test results, peak normal displacement is adversely connected with joint aperture and normal stress, whereas final normal displacement is negatively correlated with joint persistency and normal stress.  
Figure 4: Normal displacement–shear displacement curve: (**a**) joint aperture; (**b**) joint persistency; (**c**) normal stress.  
The main reasons for the above phenomenon are that before peak normal displacement is reached, the rock bridge has not entirely failed, and the specimen as a whole remains relatively stable. During this period, the joint surfaces undergo normal compressive deformation due to the action of normal stress. As the joint aperture increases, the distance between the joint surfaces of the specimen increases, providing space for the normal compressive deformation of the joint surfaces. Therefore, the peak normal displacement is strongly influenced by the joint aperture and normal stress. The final normal displacement is at the end of the residual strength stage when the rock bridge has been destroyed, and the shear surfaces have been worn. The effect of joint persistency and normal stress on the final normal displacement may be that higher normal stresses make the shear surface more abrasive, while higher joint persistency makes the rock bridge section of the shear surfaces more susceptible to breakage. These would lower the final normal displacement.
# New Detection Method for Fungal Infection in Silver Fir Seeds
## 5 Discussion
### Classification Modeling Using Electronic Nose Data  
Various methods of extracting modeling features from sensors response curves can be found. The Airsense Winnmuster software, provided with the PEN3 electronic nose, uses sensor response values at given time moments, usually at the end of observation. In many other reports, more advanced methods are used [81; 82]. In our approach, we used the raw sensor response from various points in the response curve.  
Here are some advantages of this method to justify the choice of the classification algorithm. In this method, a set of weak decision tree classifiers was trained with different subsamples of the whole training dataset and subsets of modeling features, and the partial results were averaged. This approach led to improved prediction accuracy of such a composite model. It is also essential that the random forest models are not prone to overfitting and are robust to the choice of model parameters, which is particularly important when a small number of training observations are used, as found in our study. Another advantage of this approach is the extraction of the so-called out-of-bag (OOB) error. This is the average error calculated using the predictions of the trees for observations that were not included in the particular bootstrap sample. This allows an entire dataset to be fitted and validated during a single training procedure [83]. Another vital advantage of the random forest method is the natural method of evaluating of the importance of modeling features, based on the frequency with which they enter the models.  
The classification accuracy found here (63%) was similar to other results (64%) obtained for other biological samples [84], when a custom-made electronic nose was used to discriminate between two pathogenic oomycetes infecting germinated acorns. However, it was much lower than the accuracy (74-78%) obtained in our studies of odors of ash roots infected with _Hymenosyclhus fraxineus_, where we also used the PEN3 device [80].
## 3 Results and Discussion  
### Burst Pressure  
The Mullen burst test was performed to determine the normalized burst pressure. Figure 2 shows the normalized burst pressure as a function of treatment time at various NaOH concentrations and temperatures. The horizontal axis is plotted in logarithmic time. Regarding the electrochemical characteristics, when the AMX membrane was immersed in alkali at 40 \({}^{\circ}\)C for approximately 1 week, the water content increased, the electrical resistance decreased, and the ion selective permeability decreased [1].  
However, it is noteworthy that the burst pressure increased once and then decreased after 1 week of treatment. To the authors' best knowledge, studies regarding the strength of AEMs being improved by alkali contact have not been reported.  
Figure 2: Normalized burst pressure as a function of treatment time at various NaOH concentrations and temperatures. Temperature of the treatment solutions: (**a**) 40 \({}^{\circ}\)C; (**b**) 60 \({}^{\circ}\)C; (c) 80 \({}^{\circ}\)C.  
In the alkali treatment at 40 \({}^{\circ}\)C, the maximum burst pressure was recorded at 24 h and 0.01 M, whereas the maximum was recorded at 0.1 M and 1 M at 3 h. These results suggest that under a higher NaOH concentration or temperature, the maximum burst pressure occurred at a shorter treatment time. At 0.1 M, the strength was almost double that of the pristine at 3 h and 150% at 168 h. The case of 0.1 M NaOH and 168 h was equivalent to 3 years of standard CIP conditions (1 h treatment with 0.1 M NaOH once a week).  
In the treatment at 60 \({}^{\circ}\)C, the maximum value was recorded at 3 h, even at 0.01 M, and at 1 M, it was lower than the initial value after approximately 40 h.  
At 80 \({}^{\circ}\)C, the strength increased from the initial value at treatment concentrations of 0.01 and 0.1 M for 3 h, but the strength of the samples for all the treatment concentrations decreased from the initial value after approximately 20 h.  
When a membrane is subjected to the Mullen burst test, it is assumed that the tensile stress is generated in a two-dimensional direction on the membrane surface. Therefore, the increase in burst pressure can be associated with the increase in the tensile factors.  
To clarify the reason for the burst pressure increase, a tensile test was performed.
## 4 Case Study
### Data Preprocessing for Shanghai Rail Transit Lines  
The platform screen door systems on Shanghai Rail Transit Lines 1, 5, 9, and 10 were taken as the research object, and 44 months of platform screen door fault data were collected from January 2020 to August 2023. Shanghai Metro Line 1 uses 6-car carriages with 5 doors per carriage, for a total of 28 stations and 1680 platform screen doors; Line 5 uses 6 carriages with 4 doors per carriage, totaling 19 stations and 912 platform screen doors. Line 9 uses 6 carriages with 4 doors per carriage, totaling 35 stations and 1680 platform screen doors; and Line 10 uses 6 carriages with 4 doors per carriage, totaling 37 stations and 1776 platform screen doors. The data were collected in the form of monitoring information from the command center and through manual inspection, and a total of 4708 sample data were collected. The configuration of data acquisition for screen door faults is shown in Table 2, and the original dataset is shown in Table 3. The original data were used to form a preprocessed dataset after cleaning invalid features, deduplication, sorting, conditional clustering and counting, and interval calculation. Due to the prediction target being the monthly fault rate of the platform screen door systems, the fault category or classification contained in each fault record had no significant correlation with the statistics and prediction of the monthly fault rate. Therefore, it was removed when cleaning up the dimensions of invalid data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Categories of Data** & \multicolumn{3}{c}{**Parameters**} \\ \hline Monitoring Information & 1. Fault time & 2. Fault station & \\ \hline \multirow{3}{*}{Inspection Information} & 3. Fault content & 4. Malfunctioning equipment & 5. Fault mode \\  & 6. Fault cause & 7. Door function status & 8. Maintenance content \\ \cline{1-1}  & 9. Response time & 10. Handling time & 11. Processing time \\ \cline{1-1}  & 12. Delay time & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data configuration for PSD system faults on Shanghai Rail Transit Lines 1, 5, 9, and 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Value** \\ \hline Fault Serial Number & 53020 \\ Time & 1 January 2020 12:41 \\ Station & Line 9 Songjiang Sports Center Station \\ Malfunctioning Equipment & Platform screen door \\ Fault Content & 1 January 2020 12:53:45 Line 9 ARC: Downward \\ PSD fault alarm of Songjiang Sports Center \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample data of the original dataset.  
Figure 7: Fault rate prediction process for rail transit PSD system.  
After preprocessing, the dataset (shown in Table 4) contained the monthly fault situation for 44 months, that is, the monthly fault rate and monthly MTBF data corresponding to each month from January 2020 to August 2023. In subsequent experiments, the entire processed dataset was input into the TimeGAN model for data augmentation.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.3 Projected Gradient Descent  
Projected Gradient Descent (PGD) extends the idea of BIM by incorporating projection onto a feasible region or constraint set. Proposed by [PERSON] et al. in 2018 [51], PGD is an optimization method that is used to identify the minimum of a function that is subjected to constraints. In the context of adversarial attacks, the feasible region often corresponds to a set of allowed perturbations that respect certain constraints, such as a maximum perturbation magnitude or spatial constraints.  
The algorithm works by iteratively taking steps following the negative gradient direction of the function, but with an added step of projecting the new point onto the feasible region defined by the constraints. This ensures that the solution found by the algorithm always satisfies the constraints. The formula can be summarized by the Expression (3).  
\[\prod_{C_{\varepsilon}}(X^{\prime})=\ Argmin_{z\in C_{\varepsilon}}\left\|Z-X^{ \prime}\right\|,\ \ \ X_{N+1}^{adv}=\prod_{C_{\varepsilon}}\left\{X_{N}^{adv}+\alpha. Sign\left(\
abla x\ J\left(X_{N}^{adv},Y\right)\right)\right\} \tag{3}\]  
here \(C_{\varepsilon}\) is constraint set where \(C_{\varepsilon}=\{z:d(x,z)<\varepsilon\}\), \(\prod_{C_{\varepsilon}}\) denotes projection onto the set \(C_{\varepsilon}\), and \(\alpha\) is the step size. For example, the projection \(\prod_{C_{\varepsilon}}(z)\)_for \(d(x,z)=\left\|x-z\right\|_{\infty}\)_ is given by clipping \(z\) to \([x-\varepsilon,x+\varepsilon]\). \(J\) dedenotes the loss function of the model, \(X\) is the original input data (i.e., image), \(Y\) is the original input class label, \(N\) denotes the iteration count and \(\alpha\) is constant to regulate the perturbation magnitude.  
PGD ensures that the solution falls within the feasible space, making it suitable for solving constrained optimization problems. However, the projection step can be computationally expensive, particularly for complex constraint sets.
## 3 Principles of Operation
### Hydrogen and Oxygen Production  
Equation (3) represents the percent \(f_{wf}\) of the wind power being used for water electrolysis where the power consumed is \(p_{e}\).  
\[p_{e}=p_{wind}.f_{wf} \tag{3}\]  
In general, the volume ratio between hydrogen and oxygen by the water electrolytic process is around 2:1. Thus the flow rate of the production can be calculated by:  
\[V_{H_{2}}=2.V_{O_{2}}=418.p_{e}.\eta_{ave}/U_{n} \tag{4}\]  
where \(V_{H_{2}}\), \(V_{O_{2}}\), \(\eta_{ave}\), \(U_{n}\) refer to the hydrogen flow rate (liter/hour), oxygen flow rate (liter/hour), transformation efficiency, and the voltage of the electrolytic bath, respectively.  
Hydrogen and oxygen production should be within the minimum and maximum capacity of the FC equipment. Moreover, the storage levels are limited by the device capacity. The constraints are expressed as  
\[V_{H_{2 min}}\leq V_{H_{2}}\leq V_{H_{2 max}} \tag{5}\]  
\[0<\eta_{ave}<1 \tag{6}\]  
\[\omega_{O_{2}}\leq\Omega_{Omax} \tag{7}\]  
\[\omega_{H_{2}}\leq\Omega_{Hmax} \tag{8}\]  
where \(V_{H_{2 min}}\), \(V_{H_{2 max}}\), \(\omega_{O_{2}}\), \(\omega_{H_{2}}\), \(\Omega_{Omax}\), and \(\Omega_{Hmax}\) refer to the minimum and maximum capacity of the hydrogen production, oxygen storage volume, hydrogen storage volume, maximum capacity of the oxygen storage, and the maximum capacity of the hydrogen storage, respectively.
## 3 Results and Discussion
### Prediction Results of the CNN-FSPA Model
#### 3.2.1 Testing of Different Parameters  
The CNN-FSPA model was trained using 40 earthquakes for training and 4 earthquakes for testing. The average test errors of the CNN-FSPA model for different CNN parameters are listed in Table 7.  
Table 7 shows that by varying the models with and without padding, it was found that the models with more than one convolutional layer showed lower error rates and better predictions than their counterparts without padding. The prediction results of the CNNs with different convolutional layers and convolutional kernels are also listed in Table 7. For the CNNs with one convolutional layer, the errors of the test results were 18.3-19.7%. For the CNNs with two convolutional layers, the error rate was 12.6-13.0%. For the CNNs with three convolutional layers, the error rate was slightly higher at 12.8-13.3%. Overall, the testing error could be reduced by about 1% using the padding function of the CNN models with two or three convolutional layers.  
The test with the lowest error was selected to give the training parameters for determining the CNN structure. The specific structural parameters of the CNNs are shown in Table 8. There were five layers with two convolutional layers in this test. In the first convolutional layer, 190 convolutional kernels with a size of \(2\times 1\) were used to process the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**No Padding**} & \multicolumn{2}{c}{**Padding**} \\ \hline \multirow{2}{*}{**Kernel Size**} & **Kernel** & \multirow{2}{*}{**Error**} & \multirow{2}{*}{**Kernel Size**} & **Kernel** & **Error** \\  & **Number** & & & & **Number** & \\ \hline
[2 1] & 192 & 18.3\% & [2 1] & 192 & 19.7\% \\ \hline
[2 1] & 190 & 13.0\% & [2 1] & 190 & 12.6\% \\
[3 1] & 320 & 13.0\% & [3 1] & 320 & 12.6\% \\ \hline
[2 1] & 190 & 13.0\% & [2 1] & 190 & 190 & 12.8\% \\
[3 1] & 320 & 13.3\% & [3 1] & 320 & 12.8\% \\
[4 1] & 330 & & [4 1] & 330 & 12.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: No padding and padding of the CNN models.  
input data. In the second convolutional layer, the number of convolutional kernels was 320 with a size of \(3\times 1\).  
Selection of the appropriate parameters according to the actual data had a significant effect on the training of the CNN model.
# Weather Radar in Complex Orography
## 3 Challenges
### Planning and Installation  
The first is related to planning and installation. The choice of a suitable site in itself is often not trivial. The radar has to be high enough to limit shielding and have good visibility over the area of interest, but as low as possible to observe the weather close to the terrain. Long access routes to remote radar sites on mountains and harsh weather conditions pose many engineering problems for the construction of buildings, the installation of the radar, the protection of the infrastructure from hazards caused by lightning, icing, wind and snow storms, and the provision of electricity and data lines. The time window that is suitable for civil work in the summer is often short, and, in the absence of a road, people and the necessary materials must be transported by cable and helicopter.
# Structural and Biochemical Characterization of Thioredoxin-2 from _Deinococcus radiotrans_
###### Abstract  
Thioredoxin (Trx), a ubiquitous protein showing disulfide reductase activity, plays critical roles in cellular redox control and oxidative stress response. Trx is a member of the Trx system, comprising Trx, Trx reductase (TrxR), and a cognate reductant (generally reduced nicotinamide adenine dinucleotide phosphate, NADPH). Bacterial Trx1 contains only the Trx-fold domain, in which the active site CXXC motif that is critical for the disulfide reduction activity is located. Bacterial Trx2 contains an N-terminal extension, which forms a zinc-finger domain, including two additional CXXC motifs. The multi-stress resistant bacterium _Deinococcus radiotrans_ encodes both Trx1 (DrTrx1) and Trx2 (DrTrx2), which act as members of the enzymatic antioxidant systems. In this study, we constructed _\(\Delta\)drtrx1_ and _\(\Delta\)drtrx2_ mutants and examined their survival rates under H2O2 treated conditions. Both _drtrx1_ and _drtrx2_ genes were induced following H2O2 treatment, and the _\(\Delta\)drtrx1_ and _\(\Delta\)drtrx2_ mutants showed a decrease in resistance toward H2O2, compared to the wild-type. Native DrTrx1 and DrTrx2 clearly displayed insulin and DTNB reduction activity, whereas mutant DrTrx1 and DrTrx2, which harbors the substitution of conserved cysteine to serine in its active site CXXC motif, showed almost no reduction activity. Mutations in the zinc binding cysteines did not fully eliminate the reduction activities of DrTrx2. Furthermore, we solved the crystal structure of full-length DrTrx2 at 1.96 A resolution. The N-terminal zinc-finger domain of Trx2 is thought to be involved in Trx-target interaction and, from our DrTrx2 structure, the orientation of the zinc-finger domain of DrTrx2 and its interdomain interaction, between the Trx-fold domain and the zinc-finger domain, is clearly distinguished from those of the other Trx2 structures.  
thioredoxin; Trx2; _D. radiotrans_; crystal structure; disulfide reduction