esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Photoionization Cross Section Calculations for C III
## 2 Computation Methods and Results
### Photoionization and Photoabsorption Cross Sections  
The numerical evaluation of photoionization and photoabsorption cross sections has been performed, accounting for the modifications suggested in refs. [37; 38; 39; 40] to include the type I damping case. According to this, we considered the inner-region damping involving radiative decay to a bound state for which all orbitals are completely contained within the \(R\)-matrix box. The damping effect is responsible for the separation between photoionization and photoabsorption cross sections. Detailed spectral features in photoionization of the ground and excited bound fine structure levels, 488 in total, were obtained. Photoionization features included Rydberg resonances formed by the 1s\({}^{2}\)2 pns (\({}^{1}\)P\({}_{1}\)\({}^{o}\)) and 1s\({}^{2}\)2 pnd (\({}^{1}\)P\({}_{1}\)\({}^{o}\)) quasi-bound autoionizing states, Seaton resonances formed at dipole allowed excitation energies of the core ion, the impact of the channel couplings in fine structure, background cross section, and interference effects among them in photoionization cross sections. Convergence of resonances is illustrated as a further indication of weakening of resonances with increasing core ion excitation energy, and their merging on the background cross section indicates that the wave function expansion has been achieved. The code uses a multi-channel quantum defect theory approach within the asymptotic \(R\)-matrix codes STGF and STGBF. The photoionization cross section on the ground level 1s\({}^{2}\)2s\({}^{2}\) is presented in Figure 3.  
The feature in Figure 3 is typical for a ground state with a strong background cross section and 1s\({}^{2}\)2\(ppp\) (\(n\leq 10\)) \(J\) = 0\({}^{\rm o}\) resonance structures converging to CIV 1s\({}^{2}\)2\(p\) thresholds. Target representations listed in Table 1 were used. The compressed set of resonances at the ionization threshold of about 0.58 Ry is formed through the coupling of fine structure channels. Resonances are delineated and resolved as follows. Figure 4 represents the _2\(ppp\)_ Rydberg series of resonances in the photoionization of the CIII 1s\({}^{2}\)2s\({}^{2}\) ground state. The length and velocity results, including the 2s and 2p open channels, are presented as a further indication of the accuracy of the calculation. Figure 5 shows the partial photoionization cross sections (in length and velocity gauge) out of the (a) 1s\({}^{2}\)2s2p \({}^{3}\)P\({}_{1}\)\({}^{o}\) and (b) 1s\({}^{2}\)2s2p\({}^{1}\)P\({}_{1}\)\({}^{o}\) levels, respectively, as a function of the photon energy above the ground state of C III.
# Generative Adversarial Networks in Business and Social Science
## 2 Generative Adversarial Networks
### Variants of GANs  
The training of GANs presents some major challenges: for example, mode collapse [14], non-convergence [11; 15; 16], and instability [17], among others [18; 19]. Furthermore, a GAN can be structured to address a specific problem. Thus, some variants of GANs have been proposed in order to overcome these challenges by redesigning the generator and discriminator in the network architecture, changing the form of loss functions, and altering optimization algorithms. Hence, the following variants of GANs are worthy of note:  
**Conditional GAN (CGAN):**: This is similar to the classic GAN but allows for the generation of data from a specific class defined within the real dataset, such as the generation of MNIST digits conditioned on class labels [20].
**Vanilla GAN:**: This is a simple type of GAN where the discriminator and the generator are simpler, multilayer perceptrons [1].  
Figure 1: Structure of a generative adversarial network model [13].  
**Deeper convolutional GAN (DCGAN):**: This is a GAN whereby the architecture of both the generator and discriminator is composed of ConvNets instead of multilayer perceptrons [21; 22; 23].
**Laplacian pyramid GAN (LAPGAN):**: This combines the CGAN model with a Laplacian pyramid representation [24].  
Other known networks include: SRGAN, which employs a deep neural network [25]; StackGAN [26; 27]; CycleGAN, as proposed in [28]; PassGAN [29]; WGAN, as seen in [30]; spatio-temporal GAN, as studied in [31]; constrained GAN, which can be seen in [32]; H-GAN [33]; pix2 pix, as proposed in [34]; Android-GAN [35]; UNIT, which was developed in [36]; RGAN and RaGAN, as introduced in [37]; and AnoGAN, as proposed in [38]. Ref. [39] introduces GANomaly and RCGAN, which are showcased in [40], and EGAN [41] and TimeGAN are seen in [42], among others.  
Another approach to addressing the limitations of GANs is through the proposal of hybrid models that combine GANs with other deep learning models such as transformers [43; 44; 45], diffusion models [46; 47; 48], and large language models [49; 50]. This line of research on the hybridisation of GANs with other architectures represents a promising field of investigation.  
Despite the continuous advancements and rapid growth that GANs have experienced since their inception, several challenges remain to be addressed. These include the consistency, the convergence rate, improving training stability and diversity, enhancing resources and real-time scalability, and the need to establish robust ethical regulations.
## 5 Breast Cancer and Current Treatment Strategies
The development of multitarget agents, particularly those targeting the colchicine binding site, is a promising strategy for overcoming drug resistance and improving therapeutic efficacy [223]. Fosbretabulin, an FDA-approved drug, targets the colchicine site, offer ing a potential treatment for thyroid cancer while being less susceptible to multi-drug resistance [224]. Epigenetic therapy aims to modify gene expression without changing the genetic code itself. It involves altering gene activity through mechanisms that do not affect the DNA sequence [225]. Key epigenetic processes include DNA methylation, histone modifications (such as acetylation and methylation), and non-coding RNAs. Among these, DNA methylation and histone tail acetylation/methylation are some of the most studied epigenetic mechanisms [226; 227]. DNA methylation is an important epigenetic modification that regulates gene expression and plays a role in normal cell function and embryonic development [228]. Abnormal DNA methylation, such as hypermethylation of tumor suppressor genes or hypomethylidation of oncogenes, can lead to tumor development [229]. DNA methylation is mediated by DNA methyltransferases (DNMTs), and inhibiting these enzymes shows potential for cancer therapy. Several DNMT inhibitors (DNMTis), such as Azacitidine and Decitabine, have been approved for clinical use in treating myelodysplastic syndrome and hematological cancers. Histone methylation is regulated by histone methyltransferases (HMTs), with enzymes like histone lysine-specific demethyllases, LSD1 and LSD2, responsible for removing methyl marks [230]. LSD1 is a key regulator of transcription, and research has focused on developing LSD1 inhibitors for cancer treatment [231]. Nine LSD1 inhibitors are currently in clinical trials [232]. Another important epigenetic modification is histone acetylation. Abnormal activity of histone deacetylases (HDACs) has been linked to diseases such as neurodegenerative and cardiovascular disorders [233]. HDAC inhibitors, including Vorinostat, Romidepsin, and Panobinostat, are FDA-approved for cancer treatment and are used primarily for hematologic cancers. These inhibitors have shown strong anticancer effects, particularly for cutaneous T-cell lymphoma and peripheral T-cell lymphoma [234]. Inhibiting signaling pathways involved in tumor growth and proliferation, such as through the use of kinase inhibitors, represents a cornerstone of targeted cancer therapies [235]. These inhibitors function by blocking the activity of kinases--enzymes responsible for transferring phosphate groups from ATP to specific substrates. This phosphorylation process is essential for regulating cell signaling pathways that govern critical cellular functions, including growth, proliferation, differentiation, and apoptosis. Promising targets for breast cancer therapy include the vascular endothelial growth factor (VEGF) and its receptor VEGFR [236; 237], the PIK3/PDPK1/AKT/mTOR kinases pathway [238; 239], the Rho Kinase (ROCK) pathway [240; 241], the Aurora kinase pathway [242; 243; 244], and the JAK/STAT pathway [245; 246].
## 5 Future Work  
This study provides a foundation for future exploration into enhancing the prediction accuracy of machine learning models using VIS-NIR HSI for apple quality assessment. It identifies numerous promising avenues for future research.  
Broadening the sample size is a key opportunity for improving the models' robustness and generalizability. More comprehensive data could enhance the accuracy of the machine learning algorithms in recognizing and predicting patterns. Along with this, expanding the application of these predictive models to different apple varieties could be insightful, given the characteristic variation across cultivars. Further, advancements can be made in feature selection techniques. The exploration of methods such as genetic algorithms or principal component analysis might reveal more meaningful predictors within HSI data. The examination of state-of-the-art machine learning models and ensemble learning approaches, which employ multiple models for predictions, also promises potential improvements.  
Another potential area for improvement lies in further fine-tuning the model hyperparameters. Optimization techniques, including grid search or random search, could be used to enhance predictive performance. Simultaneously, the wavelength range used in HSI can be expanded. A broader or different range of wavelengths could capture more spectral information, thereby better representing the properties of interest, such as pH levels. Advanced data augmentation techniques, like generative adversarial networks (GANs), also hold promise [49; 50]. While traditional methods have served their purpose, GANs could enrich the dataset by generating synthetic yet realistic spectral data, proving particularly valuable in scenarios with limited original data or larger, more complex datasets. Another targeted focus for future research could be a holistic evaluation of various quality metrics of apples, such as MC, pH, firmness, SSC, starch, and acidity. In addition, to expedite the adoption of HSI technology within the fruit industry, it is crucial to conduct field tests that ascertain the practical performance of the developed technology.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 4 Criteria Considering Stress Conditions
### Discussions for the Influence of Stress State  
Although existing studies have considered the impact of the stress state on the internal stability of soil, the stress levels are relatively low. Taking the soil element on the dam axis as an example, the stress level increases with depth. For landslide dams, the dam heights commonly range from tens of meters to over a hundred meters [1], with stress levels in some areas possibly exceeding 1 MPa. Therefore, the stress levels used in the afore-mentioned tests are insufficient for the actual conditions of a landslide dam. Furthermore, existing methods for assessing the seepage stability do not involve triaxial compression stress states, and thus cannot fully reflect the true conditions of landslide dam soil.  
To compensate for the limitation of indoor experiments in observing the microscopic structural changes in soils, scholars [80; 81; 82] have used numerical methods to simulate the continuous changes in the microstructure within a sample during seepage. Currently, mesoscopic simulations are mainly conducted to verify the geometric(gradation) and hydraulic criteria. Some scholars [83; 84; 85] have simulated the internal seepage erosion process in soil samples by changing the soil gradation and fine particle content. Some scholars [86; 87; 88] have simulated the internal erosion process in samples. However, most studies focus on isotropic stress states, and fewer studies focus on complex stress states such as triaxial compression stress states.  
Furthermore, stress anisotropy in dam soil has an impact on the seepage erosion in soil. It is necessary to conduct studies on the influence of stress anisotropy on internal erosion.  
Figure 5: Critical starting gradient under different stress ratios [70].  
Studies have shown that under the anisotropic stress state, the internal erosion behavior of soil differs from that under the isotropic stress state. For instance, when the hydraulic gradient is large and the cumulative erosion reaches a critical value, significant volumetric deformation and collapse occur in the sample under anisotropic stress. While under isotropic stress, the sample remains stable without structural failure [89]. Additionally, anisotropic stress state may lead to stress concentration in the direction of the deviatoric stress state, but may also cause more deformation in that direction [90]. Furthermore, when the seepage direction is parallel to the maximum principal stress, the sample is more prone to seepage erosion, with a significant loss of fine particles and volume shrinkage [65].
# Using IoT for Cistern and Water Tank Level Monitoring
## 2 Related Work
### Wireless Sensor Networks for Water Monitoring  
[PERSON] et al. studied different water monitoring systems based on wireless sensor networks, analyzing water threats like physical, biological, and chemical pollution. The research identifies gaps, such as improved energy efficiency and underwater communication, that must be addressed to improve these monitoring systems [13]. [PERSON] et al. performed holistic and low-cost work on the problem of water quality monitoring in drinking water distribution systems. The sensor node consists of several electrochemical and optical sensors installed in the pipes; low cost, lightweight implementation, and reliable long-term operation are highlighted [14].
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 4 Performance Indicators
### Fairness
```
Input: Performance UPI values (throughput and latency) measured at IP level Output: Null hypothesis, P-value, Dmax
1 Compute UPI ECDF of the different flows of background traffic through the WiFi network of the operator B, when it coexists with the WiFi network of the operator A, and use it as a baseline (W+L scenario)
2 Perform step 1 for the case when the WiFi network of the Operator B coexists with the LBT network of the Operator A (L+W)
3 Run a two-sided sample Kolgomorov-Smirnov test
4 Calculate P and Dmax values
5 Accept or reject the null hypothesis (both distributions are similar or fair) based on P-value
6 In case of null hypothesis rejection compare Dmax wih \(D_{\alpha}\) value
```  
**Algorithm 1**Fairness evaluation  
It is important to highlight in this context that, even if this condition is not met, the value of Dmax can be used to assess the degree of fairness as long as its value approaches \(D_{\alpha}\)[33]. In this context, a nearby Dmax value will therefore be considered fairer than one whose difference with \(D_{\alpha}\) is greater
# Constructing Condition Monitoring Model of Harmonic Drive
## 1 Introduction  
As the world moves towards Industrial Revolution 4.0, integrating information technology into the manufacturing industry will promote industrial upgrading and allow industry to maintain competitiveness. The development of intelligent manufacturing technology is vital for the manufacturing industry; enabling machinery to have smart functions such as failure prediction is the current focus of industrial development.  
A harmonic drive is a special gearbox device consisting of only three essential components: the circular spline, flex spline, and wave generator. Harmonic drives are often used in the aerospace field, medical equipment, and industrial robots, and have the advantages of small size, high transmission efficiency, and low noise. They are crucial components of the six-axis robot, which shows the importance of harmonic drives for industrial development.  
When using a machine, it is often necessary to stop the machine so that an inspector can check the mechanical condition, resulting in a cost of human resources and time. The current mainstream inspection method still relies on the inspector's experience; therefore, the detection accuracy is not high. In terms of harmonic drive fault detection, [PERSON] et al. [1] proposed the use of multiple acceleration sensors for data fusion and Fast Fourier transform to process the samples, and finally inputting these data into the neural network constructed by the Convolutional Neural Network (CNN). Through this process, a 96.79% accuracy of fault detection could be achieved, but the research did not include noise processing.  
However, a fault detection model must possess characteristics such as high accuracy and noise processing in the real industrial environment. Therefore, there is a need for an efficient and accurate method to solve this problem.  
To solve the above-discussed problems, this paper proposes a method that uses machine learning methods and signal processing to deal with sound samples. The performance of the proposed model was up to 0.93 AUC of the fault detection.  
The proposed model will allow factory managers to swiftly detect any possible issue in the harmonic drive without stopping the machine, and arrange personnel for maintenance and repair in time to avoid subsequent accidents or losses.  
This paper is organized as follows: Section 2 discusses discrete wavelet transform (DWT), Log Mel spectrogram, neural network architecture, and the literature on anomaly detection. Section 3 introduces the entire architecture of the proposed monitoring model. Section 4 introduces the experimental results. Finally, Section 5 concludes this research.
# Security Audit of a Blockchain-Based Industrial Application Platform
## 4 Attacks and Countermeasures
### Attacks
#### 4.1.3 Communication Channel  
In addition to the systems themselves, the communication between them can also be attacked directly.  
Routing
* _Condition monitoring and legacy machine_: messages are redirected to generate traffic at the network gateway and to keep the network busy. The redirected traffic can also be intercepted and stored by an attacker. _These two targets are explained together because of their similarities._
* _Network gateway_: attacks in which an attacker forges or modifies routing information causes annoyances such as delivery error messages. Sending a large amount of messages to the blockchain exhausts all resources by responding to the spoofed traffic, making them unable to process legitimate service requests.
* _OPC UA server and sensor_: data messages are redirected to either access them from outside or to try to overload the network with them. _These two targets are explained together because of their similarities._  
Replay
* _Client to peers_: when sending a captured transaction twice, it is theoretically possible to execute the same chaincode twice and thus to change the state of the ledger (double spending problem). Fabric requires users to send a generated nonce value that has been deterministically hashed with the previous nonce value.
* _Peers in blockchain_: since the validators track the nonce value for each registered user, a repeated transaction can be detected and will be rejected on the network.
* _Sensor_: an attacker could record a signed packet and send the packet to the target multiple times to manipulate the system.  
Selective Forwarding
* _Client to peers_: an attacker can selectively discard transactions to the nodes. Therefore, the information that reaches the destination is incomplete. Later, when another transaction reaches the node, Fabric detects that transactions are missing.
# Hybrid GA-SOCP Approach for Placement and
## 3 Proposed Hybrid GA-SOCP Optimization Approach
### Pseudocode of the Proposed Hybrid Optimization Algorithm  
Algorithm 1 summarized the main aspects of the proposed hybrid GA-SOCP method for optimal placement and sizing of distributed generators in DC distribution networks.  
```
1:Inputs:
2: Read the data of the DC network;
3: Define the number of generators available, i.e., \(N_{dg}^{raw}\);
4: Define the percentage of penetration of distributed generation i.e., \(\alpha\);
5: Generate the initial population \(W^{0}\);
6: Evaluate the fitness function for each individual in the population (see the slave stage);
7: Make \(t=0\);
8:while\(t\leq t_{\text{max}}\)do
9: Select two parents from the population;
10: Make recombinations for generating two offspring individuals;
11: Apply the mutation operator for each offspring population;
12: Evaluate the fitness function of the offspring individuals, i.e., slave stage (see optimization model (14)-(18));
13: Select the offspring with the minimum fitness function;
14: Replace the worst individual in the population with the selected offspring if the offspring has the best objective function and fulfills the diversity criterion;
15:if\(\tau\geq\tau_{\text{max}}\)then
16: Select as the solution of the problem, the individual with the best objective function in the current population \(W^{t}\);
17: Return the optimal solution concerning the location and sizing of DGs (see the slave stage);
18:break;
19:endif
20:\(t=t+1\);
21:endwhile
22:Output:
23: The best solution is found for the MINLP model;
```  
**Algorithm 1**Proposed master-slave optimization approach for the optimal location and sizing of distributed generators (DGs) in DC distribution networks