esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Quantitative Results  
Comparing the embedded and embodied toxicological impact potential reveals a slightly larger toxicological footprint for both wood and concrete. However, if the impact is divided into carcinogenic and non-carcinogenic effects on human health, the embedded toxicity impacts stemming from carcinogenic effects from exposure to chemicals in wood and concrete are larger than the toxicological footprint impacts from carcinogenic effects. This suggests that the embedded toxicological impacts can be larger than the embodied chemicals. However, as presented in Figures 6 and 7, the embedded toxicological impacts are of considerable proportions and leaving these out would further hamper the uncertainty quantifying the toxicological impact potentials of the built environment.  
An interesting observation is the fact that the embodied toxicities of both wood and concrete include the chemicals identified in the embedded toxicity for which characterization factors are available. These chemicals are easily identified as elementary flows in the system inventories of the concrete and wood product systems modeled in this paper. Naphthalene appears in both inventories, where it is used to quantify the toxicological footprint of both wood and concrete. The largest contribution of naphthalene induced toxicological impacts originates from the processes \"market for sawnwood, softwood, dried (u = 10%), planed\" and \"market for cement, Portland\", respectively, and hence from processes also taking place prior to use of the concrete. Benzaldehyde, benzene, and hexane are also present in the system inventory of the concrete product system, and here the largest contribution of these chemicals stems from the \"market for cement, Portland\". Various other organic chemicals and heavy metals also appear in the inventories. However, pyridine is not present.  
Another note that can be taken from the results of this study is found in the relative values for embodied and embedded toxicological impacts. While the circular economy is typically introduced to reduce the impacts of the built environment [4], whether through a reduction in the use of natural resources or a more holistic impact assessment, the understanding garnered through the quantification of embedded toxicological impacts shows a result that can run counter to this goal. The understanding that introducing the circular economy provides a pathway for toxic chemical exposure to humans highlights a need for caution in ensuring that a holistic picture of the results of the circular economy is accounted for. For example, in systems where the circular economy is optimized to reduce greenhouse gas emissions through the incorporation of a circular economy, it is possible that there would be a resultant increase in toxicological effects from impacts on indoor environmental quality due to the introduction of embedded toxic chemicals.
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 4 The Role of lncRNAs beyond Metastasis
### Cancer Stem Cell Maintenance  
Cancer stem cells (CSCs) are a subpopulation of cancer cells characterized by their ability to self-renew, differentiate, and drive tumorigenesis and metastasis. LncRNAs are crucial in maintaining CSCs through various mechanisms. The aberrant expression of X-inactive specific transcript (XIST) is identified as a key regulator of BC CSCs, significantly impacting their spheroid/colony-forming capacity, tumor growth, and tumor-initiating potential. XIST promotes CSC self-renewal via STAT3 activation and the expression of key CSC factors by functioning as a nuclear sponge for miRNA let-7a-2-3p, thereby enhancing IL-6 production in a paracrine manner [20]. HOTAIR is essential for BC CSC self-renewal and tumor propagation by recruiting the PRC2 protein complex to inhibit \(\kappa\)B\(\alpha\) expression, thereby activating NF-\(\kappa\)B signaling and promoting c-Myc and Cyclin D1 expression [21]. Another example is the novel long noncoding RNA Inc030, which is crucial for maintaining BC CSC stemness and promoting tumorigenesis. This is achieved by stabilizing squalene epoxidase (SQLE) mRNA through interaction with poly(rC)-binding protein 2 (PCBP2). It enhances cholesterol synthesis, which in turn activates the PI3K/Akt signaling pathway, governing BC CSC properties [22].
# EWVHunter: Grey-Box Fuzzing with Knowledge Guide on Embedded Web Front-Ends
## 5 EWVHunter
### Vulnerability Detection Strategy  
Previous research on fuzzing the protocol was to identify whether the device's program was crashing or not responding to the network when a vulnerability appeared. However, this method cannot detect command injection and authentication bypass vulnerabilities. Besides, embedded devices generally do not support local monitoring, so we cannot obtain much information (for example, file changes in the file system, CPU overhead, running process information). We make strategies through network communication to remotely detect command injection and authentication bypass vulnerabilities. The discovery of command injection vulnerabilities is mainly divided into vulnerability detection with echo results and without echo results after executing the command. The detection of authentication bypass vulnerabilities mainly relies on response status code and response content. In this paper, the vulnerability detection strategy can be divided into the following ways:  
1. **The result is displayed after the command is executed.** In this case, the result of the command execution will be displayed on the front page or the response of the request. Then it is easy to detect the corresponding vulnerability at this time;
2. **No result is displayed after executing the command.** In this case, there is no way to determine directly whether the embedded device executed our payload. Thus, we use out-of-band data transmission, time blind injection, file creation, and reboot to detect the target. The way to transfer data out-of-band is to use network operation commands such as \"wget\", \"curl\", and \"ping\" to include special characters in the request, and the OOB (Out-of-band) platform can receive the request. Afterward, we only need to determine whether these messages carry corresponding special characters to detect whether a vulnerability has occurred. The time blind injection method mainly executes commands that can trigger time delay, such as sleep. It determines whether the attack payload successfully triggered the response vulnerability by detecting whether the response corresponding to the request is delayed by the corresponding time. The way to create a file is to create a file under the website's web path, and then this file may be requested from the web request. The way to restart the embedded device using the reboot command is to verify whether the embedded device is not responding to the request;
3. **Interface response status information.** For the authentication bypass vulnerability, the test object is the interface that handles data and website management. The detection method first detects the response data and status code of the page in the unlogged state. Then, it compares the response data and status code of the page in the logged state with the unlogged state after modifying the data packet. The modification of the data packet way is to remove the login information such as cookies, and then add some payloads to the data packet. Then, we observe whether the modified data packets can take login status.
## 2 Introduction to Basic Theory  
### Wavelet Noise Reduction  
Using wavelet change threshold method to reduce raw signal noise, its advantage is that the noise can be basically suppressed, and the characteristic information of the original signal is better preserved.  
Wavelet transform is to decompose a signal into a series of superposition of wavelet functions (or fitting of wavelet functions of different scales and times) [25]. These wavelet functions are all obtained by a mother wavelet after translation and scale expansion [26; 27; 28; 29]. It takes a function called mother wavelet as the position \(\tau\), and the signal was measured \(x(t)\) as the inner product under different scales \(\alpha\), namely:  
\[WT_{x}(\alpha,\tau)=\frac{1}{\sqrt{\alpha}}\int_{-\infty}^{+\infty}x(t)\varphi \bigg{(}\frac{t-\tau}{\sigma}\bigg{)}dt \tag{1}\]  
where \(\alpha\) is called the scale factor, and \(\alpha>0\), which is to stretch the basic wavelet function. While \(\tau\) reflects the displacement, whose value can be positive or negative. Both \(\alpha\) and \(\tau\) are continuous variables. The frequency domain corresponding to Equation (1) is expressed as:  
\[WT_{x}(\alpha,\tau)=\frac{\sqrt{\alpha}}{2\pi}\int_{-\infty}^{+\infty}x( \omega)\psi(\alpha\omega)e^{+j\omega\tau}d\omega \tag{2}\]  
The obtained original single fault signal is obtained by wavelet transformation to obtain \(\omega_{j,k}\), then, \(\overset{\wedge}{\omega}_{j,k}\) is obtained through the process of threshold transformation, and finally, the noise-reduced fault signal is obtained by the method of wavelet reconstruction.  
The process of wavelet threshold noise reduction is shown in Figure 2.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions  
### Slump  
As shown in Table 4 and Figure 3, with GGBFS replacing 20% of the volume, the slumps of cement mortar with different proportions of added waste PE (0%, 1%, 2%, 3%, and 4%) were 2.8 cm, 2.6 cm, 2.5 cm, 2.3 cm, and 2.1 cm. Since the waste PE had a high water-containing ability and could accommodate the free water of mortar in PE pores, the slump decreased when the added volume of waste PE increased. The slumps were 2.2 cm, 2.5 cm, and 2.8 cm when the GGBFS replaced cement proportions were 0%, 10%, and 20%, respectively. As seen, with the increase in the replaced volume of GGBFS, the slump of cement mortar increased. Due to the slower hydration effect of GGBFS in the early stage, the initial setting time of mortar was longer. The free water of the cement mortar increased, resulting in the slump of the mortar increasing. When the replaced volume of GGBFS was higher, the mortar's workability improved. In conclusion, the increase in waste PE addition resulted in a decrease in the mortar slump; the mortar workability increased with the increase in the amount of GGBFS replacement.
## 3 Results and Discussion
### Profile Surface Texture Analysis  
The profile surface texture parameters _Ra_, _Ra_, and _Rz_ were calculated using Equations (1)-(3) as specified in Section 4.2 of ISO 4287. ISO 4288 specifies a \(\lambda_{c}\) of 8 mm and \(\mathrm{l_{n}}\) of 40 mm to measure a surface with a roughness of _Ra_ 10 \(\upmu\)m and above. Due to stringent design strategies, in most cases, the components do not have sufficient evaluation length (Equation (1)) or transverse length (\(\mathrm{l_{t}}\)) due to stylus pre and post-travel lengths (Equation (2)). Hence, a \(\lambda_{c}\) and \(\mathrm{l_{n}}\) of 2.5 mm and 12.5 mm, respectively, are selected. Therefore, in this study, a \(\lambda_{c}\) of 2.5 mm and \(\mathrm{l_{n}}\) of 12.5 mm are taken as the reference values and compared to the percentage deviation of roughness values at other sampling and evaluation lengths. Ten repeated readings are taken along the surface, and the roughness results with the standard deviation in the measurement are provided in Table 3.  
\[\mathrm{l_{n}}=\lambda_{c}*5 \tag{1}\]  
Figure 5: Particle size distribution in various AM surfaces: (**a**) SLM; (**b**) DMLS; (**c**) EBM; (**d**) LENS.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Asperity Type** & **Loose Particles** & **Partial Melts** & **Balling Melts/Semi-Welds** \\ \hline
**Technique** & \multicolumn{3}{c}{**Arithmetic Mean \(\pm\) SD (\(\upmu\)m)**} \\ \hline SLM & 17.17 \(\pm\) 4.11 & 135.75 \(\pm\) 22.24 & 283.85 \(\pm\) 53.10 \\ DMLS & 20.26 \(\pm\) 6.74 & 66.11 \(\pm\) 16.51 & 119.88 \(\pm\) 17.35 \\ EBM & NA & NA & 78.10 \(\pm\) 14.69 \\ LENS & NA & 86.54 \(\pm\) 11.14 & NA \\ \hline \hline \end{tabular} Note 1: The large standard deviations in the observed values are inherent due to the following reasons (a) large size variations in the similar type of asperities present on the AM surface and (b) method used to measure the size of asperities. Note 2: The flexibility to apply the absolute \(\upmu\)m value as cut-off (\(\lambda_{c}\)) was limited due to the machine’s measurement software constraints. E.g., in surface roughness measurements, for \(\upmu\)90\(\upmu\)m in a Gaussian filter, \(2^{\circ}\upmu_{m}=34.3\) \(\upmu\)m should be applied to analyze loose particles in SLM, \(2^{\circ}\upmu_{m}=271.5\) \(\upmu\)m should be applied to analyze partial melts. Hence, the values are rounded-off. The next possible \(\lambda_{c}\) value available in the measurement software was applied \(2^{\circ}\upmu_{m}=34.3\) \(\upmu\)m, was rounded off to 80 \(\upmu\)m as (next closest option available in the software). The results to be discussed in upcoming sections are limited to the conditions above. Nevertheless, if advanced measurement analysis software allows the user to select \(\lambda_{c}\) values specifically, this limitation can be eliminated.  
\end{table}
Table 2: Maximum diameter of asperities in AM surface.
## 3 Results and Discussion
### Morphology of the Laser-Processed Surfaces
Along the valleys of the LIPSS in the C3 and C4 regions, threadlike nanostructures are being formed, which are in a perpendicular direction to the LIPSS. In the C4 region, threadlike nanostructures along the valleys are more evident than in the C3 region. The differences in the morphologies of the surfaces as depicted in Figure 3 suggest that the scanning velocity and/or hatching distance affect each irradiation process on each region. The POs, due to the different scanning velocities used on A1/C1, A2/C2, A3/C3, and A4/C4 regions, were 93, 90, 87, and 73%, respectively, while N\({}_{\text{eff}}\), which facilitates the ablation process, were 15, 10, 8, and 4 pulses, respectively [32]. These changes in POs or N\({}_{\text{eff}}\) led to varying heat energy deposition on each surface of the region, which in turn produced varying surface nanostructures with varying morphologies in each region [44]. From the discussion, it has been established that laser fluence, scanning velocity, and hatching distance influenced the formation of LIPSSs and affected their morphology [9]. Figure 3: SEM micrographs of ps laser-structured CP Ti surfaces of regions shown on each micrograph. The laser processing parameters indicated in Table 1 were used to process these regions. We find that the periodicity of the LIPSS is directly dependent on the laser fluence and scanning velocity. The LSFLs are formed as a result of the interference of incident laser with scattered or excited tangential surface waves or surface plasmons which results in a spatially periodic energy distribution on the surface [45; 46; 47]. The formation mechanism of the HSFL has not been agreed upon by researchers and several theories have been put forward such as self-organization, and a second and third harmonic generation (SHG, THG), among others [18; 40; 46]. After immersion of the laser-structured sample in seawater, the morphology of the sample was determined again using SEM. The aim was to check if there were possible morphological changes after immersion in seawater. It can be noted from Figure S2 (Supplementary Information), that there were no major changes in the morphology of the A1, B1, C1, D1, and E1 regions. The LIPSS as observed have become clearer in comparison to the ones observed in Figure 2. This might be a result of the removal of some ablation debris particles by seawater after immersion. The dimensions of the LIPSS remained almost the same and this shows that the LIPSS formed has good structural stability when exposed to a salty environment caused by seawater.
## 5 Conclusions
### Future Research  
This study focuses on a specific geographical region. Future research could assess the effectiveness of a similar model in different cities, regions, or countries. Examining the impact of various geographical conditions, cultural factors, and traffic regulations can provide a more comprehensive understanding of the model's general applicability. Additionally, comparing changes in traffic incident locations over time with ongoing traffic incidents in that area can be useful. Periodic factors should be analyzed in addition to feature selection to comprehensively address the factors affecting traffic incidents.  
Our study introduced an innovative approach based on geohash, utilizing ArcGIS and machine learning algorithms. Future research could develop a more comprehensive approach by integrating different variables and various data sources not included in the current study into the model. In addition to the research conducted, gathering and consolidating real-time data from the various analyzed regions could potentially lead to significant advancements in future studies. This approach provides the advantage of predicting traffic incidents instantaneously, thus allowing for a swift response by emergency response teams.  
Conceptualization, M.U.; methodology, M.U.; software, M.U.; validation, Y.S.T. and M.U.; formal analysis, M.U.; investigation, M.U.; resources, E.K.; data curation, M.U. and Y.S.T.; writing--original draft preparation, M.U.; writing--review and editing, Y.S.T.; visualization, M.U. and Y.S.T.; supervision, M.U. and Y.S.T.; project administration, Y.S.T. and E.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Contact the corresponding author if interested in using data. The data are not publicly available due to privacy.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Sample Flow Preparation  
The fluid sample was prepared to mimic the viscosity and composition of normal human whole blood, providing a relevant medium for studying the sorting of microparticles analogous to blood cells [34]. The solution was composed of 59.98 vol% water, 40 vol% glycerol, 0.01 wt% xanthan gum, and 0.01 wt% starch. This specific mixture was chosen to replicate the shear-thinning properties of blood, ensuring that the experimental conditions closely simulated physiological environments.
## 3 Discussion  
### Light Quality Involved in Alfafla Improvement  
Most plant species absorb light within the 400-500 nm range for blue light and within the 600-700 nm range for red light, aligning with the peak absorption ranges of photoreceptors such as phototropins, phytochromes, and cryptochromes [19]. Red light is a critical factor that enhances seed germination and overall plant development [9]. In addition, red light stimulates plant growth regulators that promote increased cell division, elongation, stem length, and plant development [20]. In this study, we found that a combination of red and blue light (650 nm:450 nm; 1:1) significantly improves seed germination percentages (%), suggesting that combined light positively affects alfalfa seed germination. A previous study showed that blue light enhances stevia seed germination [21]. Similarly, our findings indicate that the combined light treatment used in this study is even more beneficial for alfalfa seed germination. Chlorophylls with carotenoids bind to particular apoproteins, forming light-harvesting and energy-transforming pigments in plants and other photosynthetic organisms [22]. Carotenoids play a key role in the photosynthesis process, and also assist as light-harvesting pigments and defend chlorophylls from photodestruction [22]. These findings suggest that the coordination of photosynthetic pigments is vital to the photosynthesis process. In our current study, we observed that combined red and blue light enhances germi  
Figure 7: Regulation of mineral nutrients in alfalfa seedlings under different light qualities. Concentrations of P (**A**), Cu (**B**), Zn (**C**), Ca (**D**), K (**E**), and Mg (**F**) in alfalfa seedlings grown under white, red, blue, and red combined with blue light (red+blue). Different letters on the bar columns indicate significant (\(p\leq 0.05\)) differences among the treatment means. At least three independent biological replications were considered during statistical analyses.  
nation, photosynthetic efficiency, and plant improvement of alfalfa plants. These findings have the potential to benefit low-germination plant species and support light-quality-based improvements in the growth and development of alfalfa and other forage legume species.
###### Abstract  
The eastern region of India, including the coastal state of Odisha, is a moderately seismic-prone area under seismic zones II and III. However, no major studies have been conducted on earthquake probability (EPA) and hazard assessment (EHA) in Odisha. This paper had two main objectives: (1) to assess the susceptibility of seismic wave amplification (SSA) and (2) to estimate EPA in Odisha. In total, 12 indicators were employed to assess the SSA and EPA. Firstly, using the historical earthquake catalog, the peak ground acceleration (PGA) and intensity variation was observed for the Indian subcontinent. We identified high amplitude and frequency locations for estimated PGA and the periodograms were plotted. Secondly, several indicators such as slope, elevation, curvature, and amplification values of rocks were used to generate SSA using predefined weights of layers. Thirdly, 10 indicators were implemented in a developed recurrent neural network (RNN) model to create an earthquake probability map (EPM). According to the results, recent to quaternary unconsolidated sedimentary rocks and alluvial deposits have great potential to amplify earthquake intensity and consequently lead to acute ground motion. High intensity was observed in coastal and central parts of the state. Complicated morphometric structures along with high intensity variation could be other parameters that influence deposits in the Mahanadi River and its delta with high potential. The RNN model was employed to create a probability map (EPM) for the state. Results show that the Mahanadi basin has dominant structural control on earthquakes that could be found in the western parts of the state. Major faults were pointed towards a direction of WNW-ESE, NE-SW, and NNW-SSE, which may lead to isoseismic patterns. Results also show that the western part is highly probable for events while the eastern coastal part is highly susceptible to seismic amplification. The RNN model achieved an accuracy of 0.94, precision (0.94), recall (0.97), F1 score (0.96), critical success index (CSI) (0.92), and a Fowlkes-Mallows index (FM) (0.95).  
susceptibility to seismic amplification; earthquake probability; RNN; GIS; deep learning; Odisha +
Footnote †: journal: Applied Sciences