esa-sceva/satcom-chunk-collection huggingface dataset:
# Iteratively Refined Multi-Channel Speech Separation
## 3 Experimental
### Model Configuration  
Here are the basic model configurations for the experiment, most of which were the same as the original settings [22]. The window settings of STFT included a 32 ms frame length and a 16 ms hop size. In the encoder section, the kernel size of the Conv2D layer was set to (7, 7) in order to extract local features, while in other sections, the kernel size of the Conv2D layer was set to (1, 1). In the separator section, the number of the time-frequency domain path scanning blocks in this paper was reduced to 3 from the original six, to simplify the model. Each block contained two BLSTM layers, with each BLSTM layer consisting of 128 hidden units. In addition, a parameter sharing strategy was adopted in this model, meaning the same parameters were used during the iteration separation. This strategy reduced the total number of parameters in the model, thus decreasing the computational requirements.
# Oxidation-Reduction of Ti-6 Al-4V in Direct Energy Deposition Subject to Minimum Argon Consumption
## 2 Material and Methods  
Based on the literature review, it was noted that the use of titanium alloys is limited to temperatures below 600 \({}^{\circ}\)C due to their oxidation behavior above that temperature [34; 35]. Hence, the threshold temperature that should be covered by the shielding gas is 600 \({}^{\circ}\)C. The minimum shielding range can be obtained by determining the temperature distribution on the substrate during the DED process. Thermal modeling of the DED process is used to predict the temperature distribution on the coupon during deposition. In thermal modeling, numerical analysis is conducted to determine the temperature profile for isotropic and homogeneous materials. The proposed model predicts the temperature distribution in laser-based metal additive manufacturing systems, such as DED and Selective Laser Melting (SLM). Additionally, numerical analysis is developed to simulate the temperature profile in the AM process.  
Complex processes like direct metal deposition (DMD) and SLM make it time-consuming and costly to conduct experiments to capture the physical characteristics of metal AM processes. Therefore, a finite element model was created to observe the temperature distribution over the coupon to determine the minimum shielding range. Several simulations were conducted with laser powers ranging from 200 to 1800 W and dwell times between 0 and 6 s. Figure 1 shows a contour plot illustrating the critical length (the length over which the substrate temperature remains above 600 \({}^{\circ}\)C) as a function of varying dwell times and laser powers during the DED process. It can be observed from the contour plot that the minimum shielding length to be covered by the nozzle is 50-100 mm for the power range of 800-1200 W with a dwell time of 0-6 s. Based on the scan speed of the equipment, which is 100-150 mm/min, and compared with the temperature distribution from the Ansys results, the critical length is determined to be 85 mm.  
Argon and helium are two gases that are generally used for shielding in additive manufacturing processes. As a heavier gas than helium, argon provides a better shield and  
Figure 1: Contour plot illustrating critical length, laser power, and dwell time.  
is robust against other disturbances [34]. Additionally, argon is less expensive than helium, offering a distinct economic advantage, especially when manufacturing large components where gas consumption is significant. For these reasons, argon was selected as the shielding gas for this study. For ASTM Grade 5 Titanium (Ti-6 Al-4V), the maximum allowable oxygen content is 2000 PPM. Hence, a requirement to achieve below 2000 PPM levels of oxygen was selected for this work.
###### Abstract  
In recent studies, self-supervised learning methods have been explored for monocular depth estimation. They minimize the reconstruction loss of images instead of depth information as a supervised signal. However, existing methods usually assume that the corresponding points in different views should have the same color, which leads to unreliable unsupervised signals and ultimately damages the reconstruction loss during the training. Meanwhile, in the low texture region, it is unable to predict the disparity value of pixels correctly because of the small number of extracted features. To solve the above issues, we propose a network--PDANet--that integrates perceptual consistency and data augmentation consistency, which are more reliable unsupervised signals, into a regular unsupervised depth estimation model. Specifically, we apply a reliable data augmentation mechanism to minimize the loss of the disparity map generated by the original image and the augmented image, respectively, which will enhance the robustness of the image in the prediction of color fluctuation. At the same time, we aggregate the features of different layers extracted by a pre-trained VGG16 network to explore the higher-level perceptual differences between the input image and the generated one. Ablation studies demonstrate the effectiveness of each components, and PDANet shows high-quality depth estimation results on the KITTI benchmark, which optimizes the state-of-the-art method from 0.114 to 0.084, measured by absolute relative error for depth estimation.  
Keywords:self-supervised; depth estimation; low texture; perceptual consistency; data augmentation
## 3 Materials and Methods
### Design and Regulatory Criteria  
For the design of the Photovoltaic Systems Connected to the Grid (On Grid-PV), the applicable standards were used, such as:  
1. Connection of photovoltaic systems to the low voltage electrical grid, with capacity up to 30 kw according to CFE specification G0100-04 [34].
2. The Mexican Official Standard NOM-001-SEDE-2012, related to electrical installations (use), especially article 690 related to photovoltaic solar systems of the same standard [39].
3. Technical Specification for grid-connected Photovoltaic Systems associated with productive agricultural and livestock projects [71], which establishes the minimum technical specifications to be met by photovoltaic systems interconnected to the grid, for use in productive agricultural or agribusiness projects promoted by the SADER (Secretary of Agriculture and Rural Development).
4. Manual for the technical-economic evaluation of \"Photovoltaic Systems Interconnected to the Electric Grid supported through the Shared Risk Trust Program\" [72; 73], in which the technical and financial criteria are established to ensure the feasibility and quality of photovoltaic projects in the agricultural sector and ensure the efficient allocation of economic resources for projects in the country.  
The technical data collection was carried out following the recommendations previously suggested for the On Grid-PV design [74] that were adapted for the study site; Figure 7 shows the phases applied during the research and their considerations, both in the field phase (1) and in the office phase (2). A non-participant observation guide and a survey with open-ended questions were used as field tools to develop the energy diagnosis.
## 4 Molecular Effects of _Cannabis sativa_ L.
### Lung Cancer  
Cannabinoids and their agonists have been proposed as complementary pharmacological agents in the treatment of lung cancer thanks to their antineoplastic, apoptotic, and anti-metastatic properties [43]. A recent work by [PERSON] et al., showed that lung tumors can be classified according to the expression of CB1 and CB2 receptors because patients with high expression levels of both receptors are associated with a better prognosis of the disease and survival [108].  
In vitro studies have largely evidenced that both CBD and THC inhibit viability as well as invasiveness of lung cancer cells [109; 110]. [PERSON] et al. demonstrated that CBD caused a profound inhibition of viability and invasion capacity of A549 and H460 lung cancer cells, accompanied by a decreased expression and secretion of PAI-I, at very low concentrations (as low as 0.1 \(\upmu\)M); these events are CB1-, CB2-, and TRPV1-dependent. The authors also found that CBD reduces in vivo the size of tumor in nude mice [109].  
Similar to CBD, THC has been found to suppresses viability and invasiveness of three different lung cancer cell lines (H1299, A549, H1975) deterring cell migration and abolishing cytoskeleton reorganization/focal adhesion assembly at a dose of 10 \(\upmu\)M [111]. The molecular mechanism of action of THC passed through the abolishment of Src-dependent cytoskeleton reorganization and focal adhesion assembly diminished, both processes deeply involved in carcinogenesis and metastasis [24].  
Recently, _Cannabis Sativa_ female flower heterogenous extracts have been found to induce death of lung cancer A549 cell line in a time-dependent manner but at very low doses (50-900 ng/mL), following induction of early apoptosis, cell cycle arrest, elevation of ROS level, and activation of caspase 3 [112]. The mechanism seems to be mediated by the binding to the CB2 receptors, since their blockage caused attenuation of _Cannabis Sativa_ effects on A549 cells [112].  
Similar to what was described in brain cancer cells, besides the use of single cannabinoids, [PERSON] et al. described cannabis extracts as antitumor agents in A549 and NCI-H460 lung carcinoma to a greater extent than THC alone [102]. The underlying molecular mechanisms and signaling pathways are mainly related to the activation of the extracellular signal-related kinase (ERK), phosphoinositide3-kinase (PI3K), p38 mitogen-activated protein kinase (p38 MAPK), and ceramide pathways [113]. Another mechanism demonstrated in CBD-treated lung cancer is the up-regulation of cyclooxygenase-2 (COX-2) and PPAR-gamma in vitro as well as in vivo [110]. Regarding the anti-metastatic activity, two mechanisms, directly correlated with the invasion process, have been proposed to be induced by cannabinoids: a) decreased secretion of plasminogen activator inhibitor-1 (PAI-1) [109]; b) an upregulation in the expression of ICAM-1 [114].
## 4 Influence of Advance Rate on Front Abtment Pressure
### Field Monitoring of Front Abutment Pressure in the 12308 Working Face  
The abutment pressure was monitored using the KJ743 system, a dynamic surrounding rock stress monitoring device commonly employed in coal mines and underground engineering. Its measurement principle is based on vibrating wire sensor technology, integrated with fiber-optic transmission and digital signal processing, enabling real-time and continuous monitoring of rock mass stress.  
Monitoring points were arranged starting 30 m outward from the open-off cut along the 12308 belt entry and return airway. Each monitoring group consisted of two boreholes with drilling depths of 8 m and 12 m. A total of 10 monitoring groups were deployed at 30 m intervals. The layout of borehole stress meters at the working face is illustrated in Figure 11.  
Figure 11: Layout plan of borehole stress meters in the 12308 working face.  
A comparative analysis reveals that under high-speed advancing conditions compared to low-speed operations, the influence range of abutment pressure decreased from 83 m to 61 m (26.5% reduction), while the peak stress magnitude increased by 1.63 MPa (18.1% enhancement). Concurrently, the distance between the peak stress location and the coal wall diminished by 3.4 m (38.6% decrease). These findings collectively demonstrate that three critical abutment pressure characteristics--influence range, peak magnitude, and peak-to-wall distance--are governed by face advancing rate effects.  
Specifically, increasing advancing rates induce progressive contraction of the pressure-affected zone, amplify stress concentration magnitudes, and shift peak stress positions toward the coal wall. Field-measured data align closely with theoretical predictions, confirming the consistency between empirical observations and analytical models.
## 4 Results
This demonstrates the superior precision of MKD-ICP in handling intricate geometrical features. As the geometric complexity increases, the error rates of rule-based and SVM-based methods rise sharply. In contrast, MKD-ICP exhibits much slower error growth, indicating better robustness and scalability to complex structures. When additional simulated noise is introduced (e.g., random distortions in model features), MKD-ICP shows minimal sensitivity to noise, with only a slight increase in error. Conversely, the performance of rule-based and SVM-based methods degrades more significantly under noisy conditions. To enhance the rigor of performance evaluation, we compared the proposed MKD-ICP method with deep learning-based methods, SVMs, rule-based heuristics, random forests, and KNN. Dimensioning accuracy (%) and execution time (s) were averaged over multiple runs, and standard deviations are shown as error bars in Figure 10.  
Figure 8: “Rear Wall Assembly” engineering drawing dimensioning results. As seen in Figure 10, MKD-ICP achieved the highest accuracy (-92%) with low variability, outperforming other methods. While deep learning achieved similar accuracy, it showed higher variance. SVM and random forest offered faster execution but lower accuracy, while rule-based and KNN methods performed the worst. MKD-ICP demonstrated a strong balance between accuracy and efficiency across different conditions.
## 1 Introduction
Univariate methods test one metabolite at a time for its ability to discriminate a dependent variable, then, top most significant metabolites are used to develop a statistical model. Multivariate methods take into consideration the synergy among metabolites. Based on different subsets of metabolites, many possible models are evaluated and the most predictive model is identified and selected [30; 31]. It was reported [32] that Genetic Algorithms (GA) are promising multivariate approach in analysis of the LC-ESI-MS metabolomics datasets. Genetic Algorithms approach is inspired by the features of real biological systems. It is, actually, a tool for feature/variable/predictor selection in data mining. The GA procedure starts from random populations of metabolites subsets of a given size [33]. Each subset is assessed for its ability to predict disease effects and has a certain level of accuracy. A new generation of subsets with higher classification accuracy is produced by mechanisms mimicking natural selection such as reproduction, selection, mutation, crossover, and migration. These new subsets replace the initial population, and the progressive improvement of the subset population is repeated enough times until a desired level of accuracy is reached. A major difference between GA and other machine learning approaches is its ability to determine relationship among feature components, providing valuable information about metabolite interactions, metabolic pathways, and clinical diagnosis. However, in order to better understand metabolic networking data, major breakthroughs are needed in the following challenging areas: high-throughput de novo identification; integrated mapping of microarray, proteomics, metabolomics, and biological knowledge base; robust and flexible computational methodologies to calculate cluster coefficients and similarities between metabolic networks in relation to subtle changes in environmental, biochemical or developmental conditions. In present study we applied GA on pattern recognition and pathway analysis among datasets generated by three complementary analytical MS based techniques in three different cases. GC-TOF-MS data were annotated prior further analysis. LC-ESI-MS data were not annotated. Unsupervised methods and supervised methods were used for data processing as well.
## Appendix A Derivation of Plasmon Frequencies for Finite Electrolyte System  
### Volume Ionic Plasmons  
In order to determine self-frequencies of the volume ionic plasmon in the sphere, we must solve Equation (18) with the form of the relevant solution given by Equation (20). For initial conditions listed below Equation (20) we assume \(F(\mathbf{r},t)=F_{\omega}(\mathbf{r})sin(\omega t)\) and by substitution of this function into Equation (18) we get,  
\[\Delta F_{\omega}(\mathbf{r})+k^{2}F_{\omega}(\mathbf{r})=0, \tag{19}\]  
where \(k^{2}=\frac{(\omega^{2}-\omega_{p}^{2})m}{k_{B}T}\). This is a well-known Helmholtz differential equation of which solutions (finite in origin) can be expressed by the spherical Bessel functions (for the radial dependence of \(F_{\omega}(\mathbf{r})\)),  
\[F_{\omega}(\mathbf{r})=Aj_{l}(kr)Y_{lm}(\Omega), \tag{20}\]  
where \(j_{l}(x)=\sqrt{\frac{\pi}{2\lambda}}I_{l+1/2}(x)\) is the the \(l\)-th spherical Bessel function linked to the Bessel function of first kind. The boundary condition, \(F(a)=0\), gives quantization of \(k\), \(k_{li}=\frac{x_{li}}{a}\), where \(x_{li}\) is the \(i\)-th zero of the \(l\)-th Bessel function (cf. Figure 2 left). Following this quantization one arrives at the corresponding self-frequency quantization,  
\[\omega_{li}^{2}=\omega_{p}^{2}\left(1+\frac{k_{B}Tx_{li}}{\omega_{p}^{2}ma^{ 2}}\right). \tag{21}\]Thus the volume ionic plasmons in the sphere are described by functions,  
\[\delta\rho_{1}(\mathbf{r},t)=n\sum\limits_{l=1}^{\infty}\sum\limits_{m=-l}^{m=l} \sum\limits_{i=1}^{\infty}A_{lmi}j_{l}(k_{li}r)Y_{lm}(\Omega)sin(\omega_{li}t), \tag{10}\]  
where \(A_{lmi}\) are arbitrary constants. The component with \(l=0\) vanishes because of neutrality condition, \(\int\limits_{0}^{a}r^{2}drd\Omega r(\mathbf{r},t)=0\) (as \(\int d\Omega Y_{lm}(\Omega)=\sqrt{4\pi}\delta_{l0}\delta_{m0}\), \(d\Omega=sin\Theta d\Theta d\phi\)). Note that in ionic systems self-frequencies of volume plasmons in the sphere are temperature dependent--cf. Equation (11) and Figure 2 right.
## 2 Related Works
### Effects of Visual Content Characteristics on Comprehension
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Sample** & **Education** & **Scope/Results** & **Format** \\ \hline
[25] [PERSON] and Al-Hawamde & 60 & University and graduate students. Foreign language. & Listening comprehension in foreign language & Audio and video & Audio and video \\ Al-Hawamde & 60 & students. Foreign language. & affected by flat audio and audio + video. & on computer. \\  & & & Listening comprehension in foreign language & affected by flat audio, audio + relevant visuals & \\
[33] [PERSON] & 73 & Foreign language students. & No statistical difference in second language & Audio and video \\  & & & test-kakers’ performance on an academic & on computer. \\  & & & Listening test in an audio-only mode versus & \\  & & & an audio video video mode. & \\  & & & Listening comprehension in foreign language & \\  & & & affected by flat audio, audio + relevant visuals & \\
[88] [PERSON], [PERSON] and [PERSON] & 80 & Foreign language students. & The animation type of visualization was more & \\  & & & effective than the static one and embedding & \\  & & & animations with question advance organizers & \\  & & & improved reading comprehension significantly. & \\  & & & Listening comprehension in noise and silence & \\  & & & conditions by audio and audiovisual & \\
[39] [PERSON] et al. & 55 & Children aged 8 to 9 Swedish elementary schools. & The results are inconclusive regarding how & \\  & & & seeing a virtual speaker affects & \\  & & & listening comprehension. & \\  & & & Reading comprehension for traditional picture & \\  & & & books and for AR picture books. & \\
[42] [PERSON] & 21 & Children aged 6 to 7 & No statistical difference in the number of & \\  & & (primary school students). & interactive lessons read between high and low & \\  & & & reading comprehension groups. Readers with & \\  & & & high reading comprehension remembered & \\  & & & more events. & \\  & & & Listening comprehension affected by visual & Audio, video on \\  & & & elements of video and visual elements of AR & \\  & & & See Section 4. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of studies that explore the relationship between comprehension and visual support elements.
## 3 Design Consideration for g-C\({}_{3}\)N\({}_{4}\)/CQDs-Based Nanocomposites
### _g_-_C\({}_{3}\)N\({}_{4}\)/CQDs-Based Nanocomposites for CO\({}_{2}\) Reduction  
Photocatalytic CO\({}_{2}\) conversion into solar fuels using sunlight offers a tempting pathway to lessen environmental issues (such as climate change) and global energy [74]. Amid the greenhouse gases, CO\({}_{2}\) has been represented as the most prominent member of the group; hence, offering treatment towards resolving this issue seems to be of supreme importance [75; 76]. As a result, many researchers have focused in the most recent years on the reduction in inorganics, specifically the CO\({}_{2}\) molecule because of the aforementioned environmental problems [77]. Ultimately, hydrocarbon production from CO\({}_{2}\) conversion has been considered the potential solution to the problems of environmental catastrophes. Hence, many researchers have found metal-free photocatalysts, such as g-C\({}_{3}\)N\({}_{4}\), photosensitized by CQDs because of their sensitizing feature and excellent conducting nature. Very recently, [PERSON] et al., reported g-C\({}_{3}\)N\({}_{4}\) CQDs deposited mesoporous CeO\({}_{2}\) nanocomposite synthesized through simple hydrothermal technique [78]. The diffuse reflectance spectrum revealed that mesoporous CeO\({}_{2}\) exhibited light absorption up to range of 410 nm while with the deposition of g-C\({}_{3}\)N\({}_{4}\)-CQDs to the CeO\({}_{2}\) surface converted the absorptivity region to 422 nm. The surface area of the (0.75 wt%) g-C\({}_{3}\)N\({}_{4}\)-CQDs/CeO\({}_{2}\) (147.15 m\({}^{2}\)/g) was found to be lower than CeO\({}_{2}\) (150.79 m\({}^{2}\)/g) due to the presence of quantum dots. The photoactivity was determined for CO\({}_{2}\) conversion into CH\({}_{4}\) and CO, yielding 15.81 umol/g and 22.48 umol/g, respectively, under UV-light illumination for 10 h. In other work, [PERSON] and co-workers synthesized g-C\({}_{3}\)N\({}_{4}\)/CQDs/Au composite through a facile one-step photochemical approach and employed further for the photoconversion of CO\({}_{2}\)[79]. This composite possessed a significant CO facadic efficacy of 79.8% at potential of 0.5 V. This enhanced photoactivity of the photocatalyst was owed to the synergistic relation between the Au, CQDs, and g-C\({}_{3}\)N\({}_{4}\) nanomaterials. Additionally, the combination of g-C\({}_{3}\)N\({}_{4}\) with CQDs could simplify the CO\({}_{2}\) conversion into CO because of excellent conducting nature of CQDs, CO\({}_{2}\) adsorption, and superior capability of H\({}^{+}\). The ultra-high surface area of the hybrid photocatalyst, i.e., 117 m\({}^{2}\) g\({}^{-1}\) was the actual reason for the extreme activeness of the photocatalyst towards CO production.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.6 Legacy Devices  
Smart grid infrastructures continue to utilize legacy devices, which often lack the firmware architecture or processing power to handle contemporary security protocols [126]. The system is vulnerable due to this backwards compatibility, particularly when these devices are unpatched or unencrypted, requiring isolation, gateway-level security, or low-power cryptography [127].