esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Yeast Genome Haploinusfficiency Screening
Table 1 represents the biological/ molecular functions of the hypersensitive mutants are presented. Figure 5 shows growth curves for the treated and untreated wildtype and mutant strains with allicin that inhibited the growth of three yeast mutant strains mainly related to chromatid cohesion, double-strand break repair and mitochondrial translation. Each yeast mutant is inhibited to a greater extent as compared to the wildtype strain. The yeast strain \(\Delta mcm5\), which expresses the reduced level of double-strand repair via break-induced replication, was found to be the most sensitive strain to allicin, and the corresponding MCM5p protein, along with other MCMp proteins, is essential for DNA replication [58]. The second hypersensitive mutant was \(\Delta npa3\), which carries deletion for mitotic sister chromatid cohesion. Sister chromatid cohesion and separation is essential for DNA double-strand break repair and replication [59]. The third one was \(\Delta hts1\), the mutant with gene deleted for mitochondrial translation. The respective HTS1 gene encodes mitochondrial synthetases and the mutations in the HTS1 gene may result in lethality [60]. Allicin has been reported to cause mitochondrial dysfunction leading to cell necrosis and apoptosis in _Leslmenia_[61]. Previously, allicin treatment resulted in the inhibition of bacterial cell growth by inhibiting DNA, RNA and protein synthesis [45]. One study has demonstrated the action of allicin on several different yeast transcription factors [44]. Allicin also affected the signal transduction and resulted in the apoptosis [62]. Similarly, allicin inhibited the proliferation and induced apoptosis of various cancers by DNA fragmentation and activation of  
Figure 6: Graph showing the effects of 50 \(\upmu\)M of allicin after 15 h on the growth of wildtype BY4743 and \(\Delta npa3\), \(\Delta mcm5\) and \(\Delta hts1\) mutants in CSM. Values represent mean \(\pm\) S.D. *** \(p<0.001\). caspases and poly (ADP-ribose) polymerase cleavage [63; 64]. Garlic extracts and allicin have been reported to inhibit the cholesterol synthesis in mammalian cells [65; 66; 67]. Moreover, allicin caused the lowering of cholesterol in hypercholesterol- emic mice [68; 69]. Allicin has also been shown to induce cell cycle arrest of gastric cancer cells in the M phase [70]. In addition, allicin induced cell cycle arrest, particularly in the G2/M phase [71]. It has also been confirmed that allicin induces apoptosis in yeast [8] and in mammalian cells [72]. Thus, allicin resulted in the growth inhibition of mutants with specific deletions for genes coding for vital proteins, required for important cellular processes, i.e., cell viability and growth. These findings require further investigations to find out the sole mechanism responsible for the antimicrobial activities of allicin.
# Research on Ultra-Fast Transient Overvoltage Characteristics of Electric Locomotive
## 4 Analysis and Discussion
### Hazard of Ultra-Fast Transient Overvoltage to Locomotive High-Voltage Equipment  
For winding equipment, the inductance and capacitance of the winding form a complex distributed parameter circuit, so the complex electromagnetic oscillations occur with the ultra-fast transient overvoltage. The main insulation and longitudinal insulation will suffer very high overvoltage and potential gradients which can lead to turn-to-turn insulation breakdown [21]. The larger the steepness, the more severe the oscillation. The winding potential gradient is derived as:  
\[\left.\frac{du}{dx}\right|_{x=0}=-U_{0}\alpha=-(\frac{U_{0}}{l})\alpha l \tag{17}\]  
\(U_{0}\) is the initial voltage on the winding, \(U_{0}/l\) is the initial average potential gradient of the winding and \(\alpha\) is the steepness of the invasion wave. When \(U_{0}/l\) is constant, the potential gradient of the winding is proportional to the steepness \(\alpha\) of the invasion wave. The tested overvoltage steepness is \(10^{6}\) times higher than existing tests while the potential gradient on the winding is greatly increased. For example, the voltage difference on the two-turn winding is greater than the insulation tolerance between the windings, and the inter-turn breakdown phenomenon will occur in the winding, which will aggravate the partial discharge inside the winding, thus accelerating the aging of the equipment and shortening the service life of the equipment.  
For insulation-protection equipment, external insulation will flashover under overvoltage while the internal insulation may completely break down. GB/T 11032 [22] stipulates that the power frequency discharge voltage of the arrester is generally 2.5 to 4 times the rated voltage, and the ultra-fast transient overvoltage multiple tested in this paper reaches up to 7.8 times the rated voltage of the arrester, which is much higher than the specified value in the standard. As the crucial protection device for overvoltage, a single flashover or breakdown of the arrester generally does not cause immediate failure. However, repeated flashover or local breakdown will accelerate the aging process of insulating materials and lead to severe faults ultimately. Especially when there are undetectable flaws within the insulator, these flaws will develop under repeated overvoltage, further exacerbating the risk of insulation failure.  
When the waveform steepness becomes larger, the residual voltage on the nonlinear resistor of the arrester will increase at the same time. When the front time of the invasion current wave changes from 8 us to 0.5 us, the residual voltage on the metal oxide nonlinear resistor increases by 0.15-37.93% [23]. As the residual voltage on the arrester increases, the risk of insulation breakdown of high-voltage equipment which is in parallel with the arrester also increases significantly.  
Figure 15: Overvoltage waveform of point 2, 3, and 4 under a certain operation.
## 3 Numerical Simulation of UDRC Spallation
### Comparison of Free Surface Velocity and Failure Modes  
The simulated free surface velocity histories for plain concrete, plain UDRC, and deformed UDRC specimens are compared in Figure 10. A key observation is the remarkable similarity in the initial rising part of the velocity curve and the peak velocity (\(v_{peak}\)) among all specimen types. Furthermore, the pull-back velocity (\(\Delta v_{pb}\)) derived from these curves shows minimal differences, typically within 1%. This finding is crucial as it suggests that the presence of the central reinforcing bar does not significantly alter the macroscopic free surface response used to calculate the spall strength via Equation (4) (defined in Section 2), strongly supporting the method's applicability to UDRC.  
Figure 9: Comparison of (**a**) stress triaxiality and (**b**) Lode parameter distributions between shared-node and cohesive zone models at the cross-section 535.5 mm from the impact end, demonstrating improved uniaxial stress conditions with cohesive modeling.  
Failure modes and crack evolution were also compared. Figure 11 shows the typical spallation process in a plain concrete specimen, characterized by the formation of one or more spall layers that tend to separate completely from the main body of the specimen. In contrast, Figure 12 illustrates the behavior of a deformed UDRC specimen. While spall cracks also form in the concrete matrix, the continuous steel reinforcement often prevents the complete separation of spalled fragments. The reinforcement can bridge the cracks and maintain a degree of structural integrity even after concrete failure. However, the interaction between the stress waves and the reinforcement may sometimes lead to more complex crack patterns or secondary damage in UDRC compared to plain concrete, as stress waves can be guided or reflected by the rebar. These simulations help visualize the internal fracture process and the role of reinforcement, which is consistent with experimental observations of differing post-failure behavior.  
Figure 11: Simulated strain field evolution and spall fracture process in a plain concrete specimen.  
Figure 10: Comparison of simulated free surface velocity histories for plain concrete and UDRC specimens (plain and deformed reinforcement) subjected to identical input loading.
## 4 Discussion  
The changes in parameters such as dissolved oxygen, chlorophyll-a, pH, results of a microscopic analysis of cells, assessment of population growth and biomass growth rate have shown that industrial wastewater with high concentrations of N-NH4+ can be used as a growth medium for cultivation of _C. vulgaris_ and _S. armatus_. Oxygen is an important parameter for the growth of microalgae and can be used as an indicator of ongoing photosynthesis. Like other photosynthetic organisms, _C. vulgaris_ and _S. armatus_ release oxygen into the environment [10; 11; 12]. Measures of the metabolic processes in microalgae cells include both the ability to produce oxygen and the level of its production. Low quantity of CO2 supplied during 6 h/d influenced the low production of biomass, and as a result, low production of oxygen by algae. Oxygen which was generated during photosynthesis indicated the possibility of using wastewater with high concentrations of N-NH4+ as a growth medium for _C. vulgaris_ and _S. armatus_, and also promoted wastewater treatment processes. The photosynthetic activity of microalgae, measured by the volume of released oxygen, increased when the pH value decreased. [PERSON] et al. [13], showed a similar effect of pH on the photosynthetic activity of _Chorella vulgaris_ and _Dunialella maritima_ species. Different forms of nitrogen may be present in wastewater, such as nitrates (V), nitrates (III) and ammonium nitrogen. Algae can assimilate ammonium nitrogen from the environment [14]. The initial stage of assimilation of ammonium nitrogen is biosorption on the surface of microalgal cells [7]. The effect of oxygen on the transformation of nitrogen forms has been broadly described in the literature. [PERSON] and [PERSON] [15] showed that at least 3.4 g of oxygen is needed to oxidize 1 g of ammonium nitrogen to nitrates (III), and 1.1 g of oxygen is used to oxidize 1 g of ammonium nitrogen to nitrates (V). Nitrification, for which favourable pH values range from 7.2 to 9.0 [16], is of little importance in processes leading to photosynthetic assimilation of nitrogen compounds by microalgae biomass [17]. According to the literature, the minimum oxygen concentration necessary for the nitrification process should be 1.5-2.0 mg/L. Lower values of oxygen may cause incomplete oxidation of ammonium nitrogen to nitrates (V) [16]. During the present research, oxygen concentrations below these values were found only on days 10 and 20 in wastewater, regardless of the microalgae concentration. In wastewater with the addition of microalgae, after the adaptation phase, the amount of oxygen produced by microalgae gradually increased to 4.2 mg/L and at the same time the ammonium nitrogen concentration decreased. Pollutants in the environment (wastewater, effluents from fertilized areas) strongly affect the content of photosynthetic pigments in algae cells, and therefore, chlorophylls-a and b are the best markers of water quality [18]. [PERSON] [19] analyzed the concentration of chlorophyll-a of planktonic algae of the Szczecin Lagoon. According to his research, the average annual concentration of chlorophyll-a in Lake Nowowarpienskie
###### Abstract  
Phase angle (Pha) and bioelectrical impedance vector analysis (BIVA) have emerged as valuable tools for assessing nutritional status and prognosis in various patient populations, but there is a lack of studies in rare neurodegenerative diseases. The purpose of this cross-sectional study was to investigate these bioelectrical parameters in patients with amyotrophic lateral sclerosis (ALS), compared with healthy peers. The tetrapolar impedance method was applied and bioimpedance analysis (BIA) was performed. Bioelectrical parameters were obtained (Resistance--R; Reactance--Xc) or calculated (Pha; BIVA). For BIVA, bivariate vectors and confidence ellipses were graphically represented. In addition, R and Xc were used to determine body composition (BC) (Fat Mass--FM; Fat-Free Mass--FFM; and Total Body Water--TBW). In this study, 40 participants were divided into two groups: case group (\(n=20\), ALS patients) and control group (\(n=20\), healthy subjects. Our main results showed that ALS patients presented low levels of BMI, FFM, R, and Xc adjusted by height (R/H, Xc/H), hydration, and cellularity, compared to the healthy subjects. Our findings highlight BC and bioelectrical parameters, including Pha and BIVA, as valuable indicators of nutrition status, which should be implemented in the nutrition care process of ALS patients during the disease course.  
anyotrophic lateral sclerosis; body composition; electric impedance +
Footnote †: journal:  
Article
# A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests
###### Abstract  
Mapping the seafloor in the deep ocean is currently performed using sonar systems on surface vessels (low-resolution maps) or undersea vessels (high-resolution maps). Surface-based mapping can cover a much wider search area and is not burdened by the complex logistics required for deploying undersea vessels. However, practical size constraints for a towbody or hull-mounted sonar array result in limits in beamforming and imaging resolution. For cost-effective high-resolution mapping of the deep ocean floor from the surface, a mobile wide-aperture sparse array with subarrays distributed across multiple autonomous surface vessels (ASVs) has been designed. Such a system could enable a surface-based sensor to cover a wide area while achieving high-resolution bathymetry, with resolution cells on the order of 1 m\({}^{2}\) at a 6 km depth. For coherent 3D imaging, such a system must dynamically track the precise relative position of each boat's sonar subarray through ocean-induced motions, estimate water column and bottom reflection properties, and mitigate interference from the array sidelobes. Sea testing of this core sparse acoustic array technology has been conducted, and planning is underway for relative navigation testing with ASVs capable of hosting an acoustic subarray.  
sparse aperture MIMO sonar; deep ocean bathymetry; precise relative navigation; distributed sensing; ocean modeling; path planning
## 2 Materials and Methods  
UAS as traffic participants in controlled airspace are currently the exception rather than the rule. As a result, operating procedures in such dense airspace are not yet standardised and unmanned flights must therefore be approved in advance. The automation of this process is slowed down, among other things, by the fact that flight clearances are primarily transmitted via voice-based communication. Given the predicted increase in the number of UAVs movements in the coming decades, this type of communication may soon reach its limits, so that an alternative digital data link between ATC and UAS will be required. New communication interfaces will also be needed for future UTM systems.
## 3 Results and Discussion
### Correlation between Material Properties and Breakage Rate Parameters
#### 3.5.2 Multiple Regression Analysis  
Multiple regression analysis is an extension of simple regression analysis and involves one dependent and two or more independent variables [66]. In our study, multiple regression analysis was performed, using two independent variables, to identify correlations between breakage rate parameters and \(V_{p}\), \(R_{L}\), \(UCS\) and \(E_{t}\). The correlation matrix for breakage rate parameters (\(S_{i}\), \(\alpha_{T}\), \(\alpha\) and \(S_{m}\)) and \(V_{p}\), \(R_{L}\), \(UCS\) and \(E_{t}\) is shown in Table 6. For each dependent variable the two independent variables with the highest correlation coefficients, as shown in the correlation matrix, were used to perform the analysis. Furthermore, because of the weak correlations between optimum size \(x_{m}\) and other variables for all tested materials (Figure 14), this dependent variable was not taken into account in our study.  
The equations obtained for each dependent variable are the following:  
\[S_{i}=1.440-0.007\cdot R_{L}-0.002\cdot UCS,\ \ \ R^{2}=0.991 \tag{7}\]  
\[\alpha_{T}=2.018-0.006\cdot_{T}-0.011\cdot R_{L},\ \ \ R^{2}=0.995 \tag{8}\]  
\[\alpha=1.346-0.004\cdot R_{L}-0.036\cdot V_{p},\ \ \ R^{2}=0.838 \tag{9}\]  
\[S_{m}=3.977-0.054\cdot R_{L}-0.012\cdot UCS,\ \ \ R^{2}=0.998 \tag{10}\]  
The results of multiple regression analysis indicate that when specific material properties are considered the breakage rate parameters of silica rich materials, namely quartz, quartzite and metasandstone, can be well determined. An exception is shown only for parameter \(x_{m}\) for marble.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Property** & \(S_{i}\) & \(\alpha_{T}\) & \(\alpha\) & \(S_{m}\) & \(V_{p}\) & \(E_{t}\) & \(R_{L}\) & **UCS** \\ \hline \(S_{i}\) & 1 & & & & & & & \\ \(\alpha_{T}\) & 0.982 & 1 & & & & & & \\ \(\alpha\) & 0.856 & 0.938 & 1 & & & & & \\ \(S_{m}\) & 0.950 & 0.983 & 0.947 & 1 & & & & \\ \(V_{p}\) & \(-0.922\) & \(-0.938\) & \(-0.886\) & \(-0.865\) & 1 & & & \\ \(E_{t}\) & \(-0.923\) & \(-0.939\) & \(-0.886\) & \(-0.866\) & 1.000 & 1 & & \\ \(R_{L}\) & \(-0.990\) & \(-0.991\) & \(-0.896\) & \(-0.983\) & 0.893 & 0.895 & 1 & \\ \(UCS\) & \(-0.977\) & \(-0.923\) & \(-0.733\) & \(-0.888\) & 0.842 & 0.843 & 0.956 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation matrix for study variables.
## 5 Simulation Analysis of Support Effectiveness  
### Modelling  
To assess the damage status of the roadway and determine an appropriate support scheme for the Makeng Iron Ore Mine, an engineering geological model was developed. This model considers key factors, such as roadway location, surrounding rock lithology, and fault distribution. The model dimensions are 36 m \(\times\) 30 m \(\times\) 34 m, comprising 144,600 units and 153,078 nodes. The relevant parameters required for numerical simulation are shown in Tables 4 and 5.  
Boundary conditions are defined as follows: the bottom boundary is subject to vertical constraints, the four side boundaries are constrained horizontally, and the upper boundary is a free surface. Stress boundary conditions are applied based on measured in situ stress data. The Mohr-Coulomb model is adopted as the yield criterion. The numerical simulation model is illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Grouping Name** & \begin{tabular}{c} **Soil Natural** \\ **Density (g/cm\({}^{-3}\))** \\ \end{tabular} & \begin{tabular}{c} **Elasticity** \\ **Modulus (GPa)** \\ \end{tabular} & **Poisson’s Ratio** & \
\begin{tabular}{c} **Cohesive Force** \\ **(MPa)** \\ \end{tabular} &
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} \\ \hline Marbled limestone & 2.69 & 41.51 & 0.27 & 18.28 & 41.89 \\ mine & 3.82 & 47.70 & 0.27 & 27.65 & 47.88 \\ Quartzized limestone & 2.54 & 36.83 & 0.28 & 27.14 & 41.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rock mechanical parameter.  
Figure 5: Numerical simulation model.  
To ensure that the numerical calculations accurately reflect the actual engineering geological conditions of the Makeng Iron Ore Mine, the following assumptions are made in the modeling process:  
1. The ore-bearing rock mass is considered an ideal elastic-plastic material. After reaching the yield point, neither the material strength nor volume changes with plastic flow, and strain hardening or softening effects are not taken into account.
2. Both the ore body and surrounding rock are assumed to be locally homogeneous and isotropic. The plastic flow does not alter the isotropic nature of the materials.
3. All physical parameters involved in the analysis are considered time-independent, reflecting the brittle nature of the rock.
4. Given the limitations of finite difference calculations, tectonic activity is disregarded. The in situ stress field is assumed to be geostatic, rock layers are in full contact with each other, and the interior of each rock layer is treated as a continuous medium. Groundwater effects are also excluded from the model.
5. The calculation considers only the effects of geostress, neglecting the impact of seismic waves, explosive shock waves, and groundwater on the stability of the rock mass.
## 2 Data and Methods
### Methodology
We also used a city-stratified, randomly selected 80/10/10 split for our training/validation/testing datasets. The model output resolution is limited by the spatial resolution of the target data we have for validating the model. LUR models have the finest spatial resolution for this purpose. The aim of this study is to leverage the meter-scale land surface information to map pollution. However, instead of pixel-to-pixel mapping, we used a broader land surface area that will allow us to accurately understand the pollution distribution in the area. To achieve this, we designed a patch sampling to collect a 40 \(\times\) 40-pixel image centered over the LUR pollution point (Figure 1). The input to the model is a single patch of satellite imagery, i.e., 40 \(\times\) 40 pixels of 2.5 m spatial-resolution imagery, which corresponds to a real-world image patch of 100 m \(\times\) 100 m in size (for PM\({}_{2.5}\); NO\({}_{2}\) patches are twice as big). Each patch corresponds to one LUR value from a continuous LUR surface. The model is learning to engineer features from the imagery and associate those features with the corresponding AQ measurement. The dataset of imagery patches for London, LA, and Vancouver was split into three portions: 80% of the patches were used to train the model, 10% of the patches were used to examine performance between models, adjust model hyperparameters, and evaluate over-fitting, and the last 10% were held out until the end and only used to evaluate performance of the final model. All numbers in the results section used the latter. * Validation Stage 1: We used the trained model to predict on NYC--a location completely unseen by the model during training--to examine generalizability. Stage 2: We examined the association between the model predictions and available ground monitoring site concentrations in all study regions.
## 4 Experiment and Results
### Experimental Setup  
We implement DEPANet using PyTorch version 2.3.1 and conduct experiments on a system equipped with an NVIDIA A100 80 GB PCIe GPU. The backbone is initialized with the parameters of ResNet34 [11] pretrained on ImageNet, while the remaining parameters are initialized using default settings. [PERSON] is selected as the optimizer, with an initial learning rate of \(lr=0.001\) and a batch size of 16. The training process is completed over 600 epochs.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 1 Introduction
The framework includes two parts that cooperate with the controller, namely, traffic analysis and external application. 2. To provide an identification mechanism to distinguish flows as mice and elephants based on K-means clustering after a phase of dimensionality reduction using principal component analysis. 3. To develop a flow routing algorithm that selects the shortest paths for mice and the widest paths for elephants. Hence, a more balanced network results and a higher throughput is obtained. 4. To enable more efficient calculation of route cost and provide consistency with real-time constraint. Route update is accomplished based on a recursive process. The remainder of this paper is organized as follows: In Section 2, the literature review is presented. Afterward, the methodology is provided in Section 3. Section 4 shows the experimental results and analysis. The conclusions and directions for future works are provided in Section 5.