esa-sceva/satcom-chunk-collection huggingface dataset:
# Some Thoughts about Infrared Radiation Response Characteristics during Loading of Sandstone Samples
## 5 Discussion of Infrared Radiation Mechanisms during Loading of Rocks
### Discussion of Observed Infrared Radiation Characteristics  
The generally expected energy balance and corresponding infrared radiation characteristics (directly related to temperature change) during the uniaxial loading of rocks can be characterized as follows:  
1. During the initial (crack closure and elastic) loading phase elastic energy will be stored inside the sample, which should lead to a temperature increase. In addition, the compression of air inside isolated the pores as well as crack closure with possiblysome friction at the microscopic level may also contribute to a temperature increase. This expected temperature increase should be quite homogeneous.
2. After passing the crack initiation phase, crack propagation has to be considered as an additional factor contributing to the energy balance. Part of the stored elastic energy is used for crack propagation. This process is connected to the radiation of seismic energy and frictional heat production in the cases of Mode-II and Mode-III fracture propagation as well as local heat production at the near-zone of the crack tips due to plastications in the process zone. On the other side, cooling can take place due to gas decompression (adiabatic expansion). Mode-I fracture propagation is characterized by the much lower consumption of fracture energy, but also the lower radiation of seismic energy and more or less no frictional heat production. Due to the complex inner microstructure of rocks it is extremely difficult to evaluate the contribution of all these effects on heat production or consumption (temperature change). One should notice that fracture initiation and propagation is a very local process and consequently the heat production/consumption will appear quite inhomogeneous. Figure 7 document this local behavior.
## 4 Efficient FFT Algorithms
### Sparse FFT  
[PERSON] [78] proposed a new FFT algorithm called sparse FFT that is applicable for sparse signals which contain only a relatively small number of frequency components of significant size. In spectral analysis most of the Fourier coefficients of a signal are small or equal to zero, _i.e._, the output of the DFT is sparse. When the output of the DFT is sparse or approximately sparse, it is desired to have an output-sensitive algorithm, whose runtime depends on the number of significant coefficients computed. The disadvantage with conventional FFT is that it takes the same amount of computational time for any input signal. The proposed sparse FFT algorithm can reduce much of the computational complexity for sparse signals compared to conventional FFT, especially when the number of frequency components is a single-digit percentage of the number of samples.  
In cognitive radio systems spectrum sensing is done for a wide bandwidth typically in GHz. Sampling becomes expensive and analysis require heavy computations, when the bandwidth is large. Spectral compressive sensing techniques based on the proposed sparse FFT [79] can achieve low cost and high speed sensing of spectrum by leveraging the sparsity of the spectrum. The proposed algorithm is not iterative unlike other typical approaches in which large coefficients are first estimated and are subtracted from ori the \(k\) largest coefficients simultaneously. In addition, ginal signal and the remainder are processed recursively. The proposed algorithm identifies and estimates whether the algorithm does not need interpolation of sample points. Thus, the algorithm has been proven to be efficient by avoiding both iteration and interpolation. The algorithm is structurally simpler than other algorithms and as a consequence the algorithm is faster in computing FFT for sparse signals.  
For an input signal of length \(N\), which has \(K\) non-zero entries in the Fourier transform of the signal, sparse FFT costs \(O(K\) log \(N)\) instead of \(O(N\) log \(N)\) in FFT. In [80], a fast sparse FFT algorithm has been proposed, that costs \(O(K\) log \(K)\) averagely. In the proposed method, the original input signal is downsampled at the beginning and the Fourier transform of the downsampled signal is used to locate and estimate \(K\) non-zero entries of the Fourier transform of the original signal.
## 3 Results and Discussion
### PCA Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Source of** & \multirow{2}{*}{**AP**} & \multirow{2}{*}{**CRC**} & \multirow{2}{*}{**BC**} & \multirow{2}{*}{**FLX**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**CC**} & \multirow{2}{*}{**T**} & \multirow{2}{*}{**SM**} & \multirow{2}{*}{**OA**} \\
**Variation** & & & & & & & & & & \\ \hline
**Year** & ** & ** & **n.s.** & ** & ** & ** & **n.s.** & **n.s.** & ** \\ \hline
2019 & \(2.5\pm 1.23\)* & \(2.6\pm 1.14\)* & \(3.3\pm 0.88\)* & \(2.9\pm 1.19\)* & \(2.4\pm 1.69\)* & \(2.6\pm 1.02\)* & \(2.4\pm 1.21\)* & \(2.6\pm 1.18\)* & \(2.6\pm 0.64\)* \\ \hline
2020 & \(3.4\pm 1.16\)* & \(3.4\pm 1.01\)* & \(3.2\pm 0.76\)* & \(3.3\pm 1.06\)* & \(3.1\pm 1.28\)* & \(3.1\pm 1.42\)* & \(2.5\pm 1.69\)* & \(2.2\pm 1.23\)* & \(3.0\pm 0.80\)* \\ \hline
2021 & \(2.9\pm 1.22\)* & \(3.2\pm 1.46\)* & \(3.2\pm 1.28\)* & \(3.3\pm 1.51\)* & \(2.3\pm 1.18\)* & \(3.0\pm 1.44\)* & \(2.6\pm 1.55\)* & \(2.4\pm 1.4\)* & \(2.9\pm 0.97\)* \\ \hline
**Cultivar** & **n.s.** & **n.s.** & ** & ** & **n.s.** & **n.s.** & **n.s.** & **n.s.** \\ \hline Haernda & \(2.7\pm 1.10\)* & \(3.0\pm 1.48\)* & \(2.9\pm 0.78\)* & \(3.0\pm 1.15\)* & \(2.3\pm 1.66\)* & \(2.8\pm 1.38\)* & \(2.4\pm 1.55\)* & \(2.1\pm 1.27\)* & \(2.7\pm 0.86\)* \\ \hline Mandaryna & \(2.9\pm 1.55\)* & \(3.0\pm 1.54\)* & \(3.0\pm 0.82\)* & \(3.1\pm 1.02\)* & \(2.4\pm 1.45\)* & \(3.2\pm 1.74\)* & \(2.5\pm 1.45\)* & \(2.5\pm 1.17\)* & \(2.8\pm 0.89\)* \\ \hline Kandela & \(2.8\pm 1.09\)* & \(2.9\pm 0.75\)* & \(3.4\pm 0.80\)* & \(3.0\pm 1.40\)* & \(2.6\pm 1.33\)* & \(2.6\pm 1.01\)* & \(2.5\pm 1.34\)* & \(2.4\pm 1.15\)* & \(2.8\pm 0.61\)* \\ \hline
**Serenada** & \(3.2\pm 1.01\)* & \(3.4\pm 0.85\)* & \(3.6\pm 1.43\)* & \(3.5\pm 1.47\)* & \(3.1\pm 1.05\)* & \(3.0\pm 1.00\)* & \(2.6\pm 1.62\)* & \(2.6\pm 1.49\)* & \(3.1\pm 0.87\)* \\ \hline
**Farming system** & **n.s.** & **n.s.** & **n.s.** & **n.s.** & **n.s.** & **n.s.** & **n.s.** \\ \hline ORG & \(2.9\pm 1.08\)* & \(3.0\pm 1.13\)* & \(3.2\pm 1.36\)* & \(3.1\pm 1.3\)* & \(2.6\pm 1.11\)* & \(3.0\pm 1.05\)* & \(2.5\pm 1.36\)* & \(2.5\pm 1.34\)* & \(2.8\pm 0.86\)* \\ \hline INT & \(3.0\pm 1.48\)* & \(3.2\pm 1.48\)* & \(3.2\pm 0.81\)* & \(3.3\pm 1.05\)* & \(2.6\pm 1.54\)* & \(2.9\pm 1.68\)* & \(2.4\pm 1.69\)* & \(2.4\pm 1.35\)* & \(2.9\pm 0.86\)* \\ \hline CONV & \(3.0\pm 0.85\)* & \(3.1\pm 1.06\)* & \(3.3\pm 0.68\)* & \(3.2\pm 1.39\)* & \(2.6\pm 1.43\)* & \(2.9\pm 1.06\)* & \(2.6\pm 1.38\)* & \(2.4\pm 1.06\)* & \(2.9\pm 0.67\)* \\ \hline \hline \end{tabular}  
* Data are presented as means \(\pm\) standard deviations; n.s.—not significant, **—**–significant (different letters on the top of data: *.b* are significantly different at \(\alpha\) = 0.05). Abbreviations: AP—appearance; CRC—crust colour; BC—bread crust; FLX—flexibility; P—porosity; CC—crumb colour; T—taste; SM—smeil; OA—overall acceptability; ORG—organic; INT—integrated, CONV—conventional.  
\end{table} 
Table 5: Sensory evaluation of bread depending on the harvest year, cultivar, and farming system.
## 4 IoT Applications and Services in SG  
IoT can support technologies in SG. Comprehensive sensing and processing abilities of IoT can improve SG abilities such as processing, warning, self-healing, disaster recovery, and reliability. Combining IoT and SG can greatly promote the development of smart terminals, meters and sensors, information equipment, and communication devices. IoT can be used to accomplish reliable data transmission in wire and wireless communication infrastructures in different parts of SG (electricity generation, transmission lines, distribution, and consumption/utilization) as follows:  
1. In electricity generation, IoT can be used to monitor electricity generation of different kinds of power plants (such as coal, wind, solar, biomass), gas emissions, energy storage, energy consumption, and predict necessary power to supply consumers.  
2. IoT can be used to acquire electricity consumption, dispatch, monitor and protect transmission lines, substations, and towers, manage and control equipment.  
3. IoT can be used in customer side in smart meters to measure different types of parameters, intelligent power consumption, interoperability between different networks, charging and discharging of electric vehicles, manage energy efficiency and power demand.  
The main IoT application scenarios are as follows:  
1. AMI with high reliability: AMI is a key component in SG. IoT can be used in AMI to collect data, measure abnormality in SG, exchange information between smart meters, monitor electricity quality and distributed energy, analyze user consumption pattern.  
2. Smart home: A smart home can be used to interact with users and SG, enhance SG services, meet marketing demand, improve QoS, control smart appliances, read power consumption information which is gathered by smart meters, and monitor renewable energy.  
3. Transmission line monitoring: By using wireless broadband communication technologies, the transmission lines can be monitored to discover fault issues and eliminate them.  
4. Electric Vehicle (EV) assistant management system: EV assistant management systems comprise of charging station, EV, and monitoring center. With GPS, users can inspect nearby charging stations and their parking information. The GPS will automatically guide drivers to the most suitable charging station. The monitoring center manages car batteries, charging equipment, charging stations and optimize resources.
## 2 Data and Methods
### Methods
#### 2.2.3 FLUS Model  
[PERSON] et al. [36] put forth a future land use simulation (FLUS) approach by integrating a geographic modeling system with a neural network-based module. The FLUS method is able to accurately simulate land use evolution and project future changes. Given the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Maximum Threat Range (Kilometer)** & **Weight** & **Distance Decay** \\ \hline Cropland & 1 & 0.6 & Linear \\ Urban land & 10 & 1 & Exponential \\ Rural land & 5 & 0.6 & Exponential \\ Other construction & 7 & 1 & Exponential \\ Expressway & 5 & 0.7 & Linear \\ Railway & 5 & 0.7 & Linear \\ Road & 3 & 0.5 & Linear \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features of threat factors.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Class**} & **Habitat** & \multicolumn{5}{c}{**Susceptibility**} \\ \cline{2-9}  & **Resilience** & **Cropland** & **Urban Land** & **Rural Land** & **Other Construction** & **Expressway** & **Railway** & **Road** \\ \hline Cropland & 0.4 & 0 & 0.5 & 0.4 & 0.2 & 0.3 & 0.2 & 0.2 \\ Forest & 1 & 0.5 & 0.7 & 0.6 & 0.4 & 0.6 & 0.6 & 0.7 \\ Grassland & 0.7 & 0.4 & 0.6 & 0.5 & 0.4 & 0.3 & 0.2 & 0.2 \\ Water & 0.9 & 0.6 & 0.9 & 0.7 & 0.6 & 0.6 & 0.5 & 0.6 \\ Construction land & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ Unused land & 0.2 & 0.2 & 0.3 & 0.3 & 0.3 & 0.2 & 0.2 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Habitat resilience and threat factor susceptibility.  
significant role of natural and socioeconomic variables in the land use evolution of China, key spatial factors were adopted based on related studies (Table 1) [15; 32; 34; 40; 48; 63; 64; 65]. First, the probability of land use suitability was calculated on the basis of historical land use information. Then, the well-calibrated model was used for projecting future land use evolution in China by 2050.
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 6 Mechanical Model
### Simply Supported Beam without Axial Force  
The natural frequencies of a simply supported beam without axial force are [24; 25]:  
\[f_{n}=\frac{1}{T_{n}}\qquad\qquad T_{n}=\frac{2\pi}{\omega_{n}}=\frac{2L^{2}}{ \pi n^{2}}\sqrt{\frac{m}{EI}} \tag{8}\]  
where: \(T_{n}\) is the period of the n-th mode, L is the beam length, \(n\) is the mode number (\(n=1,2,3,\,\dots\) ), \(m\) is the mass per unit length (\(m=1.0125\) kg/m), \(E\) is the Young modulus of alluminium, and \(I\) is the inertia of the cross section related to vertical bending (\(I=1.953\cdot 10^{-8}\) m\({}^{4}\)).  
These frequencies should also be lower than the real frequencies because the bending stiffness is taken into account, but the stiffening effect of the axial load is neglected.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 3 WSN Architecture
### Latency and Packet Loss  
The total end-to-end latency \(L\) is calculated using Equation (4) as follows [40; 41].  
\[L=h\cdot\left(t_{tx}+t_{proc}+t_{queue}\right)+t_{reroute} \tag{4}\]  
With \(h\): number of hops, \(t_{tx}\): transmission time per hop, \(t_{proc}\) : processing time per node, \(t_{queue}\): queuing delay per node, and \(t_{reroute}\) : additional delay due to adaptive rerouting.  
For the hybrid dense topology, vertical rerouting enables direct jumps between tiers (concentric circles), eliminating intra-tier detours. Each tier has a radius increment of 100 m (distance from the sink). Therefore, the diameter increment per tier is 2 \(\times\) 100 m = 200 m. The number of hops is given by Equation (5).  
\[h=\sqrt{\frac{Diameter}{Tier\ diameter\ increment}}=\sqrt{\frac{Diameter}{200}} \tag{5}\]  
In fact, since hybrid dense topology allows data to jump vertically between tiers, bypassing horizontal obstacles, the number of hops is reduced compared to purely horizontal routing. The square root reflects the multi-dimensional optimization of the network.  
For the hybrid non-dense topology, there is no vertical rerouting, and the data travels intra-tier to axis nodes, then sequentially to the sink. The number of hops is given by Equation (6).  
\[h=\frac{Diameter}{200}+log_{2}\left(\frac{Diameter}{200}\right) \tag{6}\]  
In fact, in non-dense topology, vertical rerouting between tiers is disabled. Instead, the data must travel horizontally within the same tier until it reaches an axis node. This introduces two components to the total hop count (tier-to-tier hops, basic linear scaling with diameter (\(\frac{Diameter}{200}\)), and intra-tier detours). Intra-Tier detours are additional hops required to navigate within a tier to reach the nearest axis node. The logarithmic term approximates the average number of detour hops needed to reach an axis node within a tier as the network scales. It ensures the hybrid topology remains efficient in non-dense forests, even without vertical rerouting. This approach balances realism and simplicity, reflecting observed routing behavior in sparse deployments.  
The above equations show that the proposed topology requires fewer hops than 2D Mesh, which suffers from linear hop growth and no vertical rerouting, causing higher latency. The number of hops in the case of the 2D Mesh topology is given by Equation (7).  
\[h=\frac{Diameter}{Average\ hop\ distance}=\frac{Diameter}{50} \tag{7}\]  
In fact, in the 2D Mesh topology, nodes are placed in a grid where each node communicates directly with its immediate neighbors (up, down, left, and right). So, the average hop distance (50 m) ensures reliable communication between nodes while balancing signal strength.  
The Packet Loss Rate (PLR) depends on the topology number of hops h as shown in Equation (8) [42; 43]. So, packet PLR varies significantly across the proposed topology and the 2D Mesh topology due to differences in routing efficiency and hop count scaling.  
\[PLR=1-\prod_{i}^{h}\left(1-P_{loss,\ i}\right) \tag{8}\]
## 2 Materials and Methods
#### 2.4.2 Effective Dose Estimation in Occupational Exposure: Algorithms by ICRP 60 and ICRP 103  
To evaluate the estimated effective dose (\(E\)) in this study, we employed algorithms recommended by ICRP as outlined in ICRP 60 [31] and ICRP 103 [32].  
1. ICRP 60 Algorithm: the effective dose estimation based on ICRP 60 recommendations is calculated using the following equations: * With thyroid protection: \[E=\big{[}(0.64\pm 0.15)H_{p,\mu}(10)\big{]}+\big{[}(0.016\pm 0.005)H_{p,n,\rho}(10) \big{]}\] (10) * Without thyroid protection: \[E=\big{[}(0.64\pm 0.15)H_{p,\mu}(10)\big{]}+\big{[}(0.073\pm 0.009)H_{p,n,\rho}(1 0)\big{]}\] (11)where \(H_{p,\epsilon,u}(10)\) represents the personal dose equivalent at a depth of 10 mm, Hp(10), was measured using a single dosimeter positioned in the anterior thoracic region (chest) beneath the protective lead apron. A second dosimeter was worn in the anterior neck position, over the protective garment, to measure the dose for organs and areas not fully covered by the apron. This measurement, denoted as \(H_{p,n,\rho}(10)\), represents the personal dose recorded by the \"over-apron dosimeter.\"
2. ICRP 103 Algorithm: following the updated recommendations in ICRP 103, the effective dose is calculated as follows: \(\bullet\) With thyroid protection: \[E=\left[(0.60\pm 0.11)H_{p,\epsilon,u}(10)\right]+\left[(0.047\pm 0.010)H_{p,n, \rho}(10)\right]\] (12) \(\bullet\) Without thyroid protection: \[E=\left[(0.60\pm 0.11)H_{p,\epsilon,u}(10)\right]+\left[(0.094\pm 0.014)H_{p,n, \rho}(10)\right]\] (13)  
4.3 Effective Dose Estimation in Occupational Exposure: Algorithm by National Council on Radiation Protection and Measurements (NCRP)  
The effective doses were calculated using the National Council on Radiation Protection and Measurements (NCRP) double dosimetry algorithm [33]. The doses measured outside and under the lead aprons were analyzed pairwise for double dosimetry. Dose pairs where the dose under the lead apron exceeded the outside dose or where data from either the outside or under the apron were missing, the data were excluded from the statistical analysis to maintain the integrity of the results. Doses reported as <0.01 mSv were treated as 0 mSv in the dataset. The collected dose data were used to estimate the participants' effective doses, calculated using the NCRP double dosimetry algorithm, Equation (14):  
The effective dose estimation algorithm according to the NCRP is  
\[E=(0.5\times H_{under})+(0.025\times H_{outside}) \tag{14}\]
## 2 Applications for VANETs
### Private Vehicular Cloud  
The storage, connectivity, and computing resources of the vehicle can be utilized by the owner as a private cloud, _i.e._, the user connects to the internet and uploads their data to their \"private cloud\". This not only makes their data available to them whenever they want, but it also gives them physical ownership of their data.
# Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering
## Appendix A Non-Linear Robot Motion Models
### Odometry Model  
In this case \(u_{t}=\left[\tilde{x}_{t+1}^{\top},\tilde{x}_{t}^{\top}\right]^{\top}\), where \(\tilde{x}_{t}=\left[\tilde{a}_{t},\tilde{b}_{t},\tilde{\theta}_{t}\right]^{\top}\) denotes the odometry measurement at time \(t\). Using \(u_{t}\) we compute  
\[\delta_{\text{rot1}} =\arctan\frac{\tilde{b}_{t+1}-\tilde{b}_{t}}{\tilde{a}_{t+1}- \tilde{a}_{t}}-\tilde{\theta}_{t},\] \[\delta_{\text{trans}} =\sqrt{\left(\tilde{a}_{t+1}-\tilde{a}_{t}\right)^{2}+\left( \tilde{b}_{t+1}-\tilde{b}_{t}\right)^{2}},\] \[\delta_{\text{rot1}} =\tilde{\theta}_{t+1}-\tilde{\theta}_{t}-\delta_{\text{rot1}}.\]  
Then, \(w_{t}=\left[\tilde{\delta}_{\text{rot1}},\tilde{\delta}_{\text{trans}},\tilde {\delta}_{\text{rot2}}\right]\) and \(Q(u_{t})=\text{diag}\big{(}\sigma_{\text{rot1}}^{2},\sigma_{\text{trans}}^{2},\sigma_{\text{rot2}}^{2}\big{)}\) with  
\[\sigma_{\text{rot1}}^{2} =\alpha_{1}\big{|}\delta_{\text{rot1}}\big{|}+\alpha_{2}\big{|} \delta_{\text{trans}}\big{|},\] \[\sigma_{\text{trans}}^{2} =\alpha_{3}\big{|}\delta_{\text{trans}}\big{|}+\alpha_{4}\big{|} \delta_{\text{rot1}}+\delta_{\text{rot2}}\big{|},\] \[\sigma_{\text{rot2}}^{2} =\alpha_{5}\big{|}\delta_{\text{rot2}}\big{|}+\alpha_{6}\big{|} \delta_{\text{trans}}\big{|},\]and \(\alpha_{1},\ldots,\alpha_{6}\) being system parameters. The motion model is given by  
\[a_{t+1} =a_{t}+\delta_{\text{trans}}\cos(\theta_{t}+\delta_{\text{rot1}}),\] \[b_{t+1} =b_{t}+\delta_{\text{trans}}\sin(\theta_{t}+\delta_{\text{rot1}}),\] \[\theta_{t+1} =\theta_{t}+\delta_{\text{rot1}}+\delta_{\text{rot2}},\]  
where  
\[\delta_{\text{rot1}} =\delta_{\text{rot1}}+\delta_{\text{rot1}},\] \[\delta_{\text{trans}} =\delta_{\text{trans}}+\delta_{\text{trans}},\] \[\delta_{\text{rot2}} =\delta_{\text{rot2}}+\delta_{\text{rot2}}.\]
# New Insights into the Neuromyogenic Spectrum of a Gain of Function Mutation in SPTLC1
## 3 Results
### Proteomic Findings in Lymphoblastoid Cells Derived from the p.(A20S)-SPTLC1-Patient  
After confirming expression of SPTLC1 in blood and muscle by in silico studies (Supplementary Figure S1), proteomic profiling was carried out on immortalized lymphoblastoid cells. This approach has been used in one of our previous studies, highlighting that identified protein changes in immortalized lymphoblastoid cells may provide novel insights into pathophysiological processes [15] and the previous examination of lymphoblasts derived from SPTLC1-related HSAN cases [2]. Proteomics-based quantification of 4486 proteins revealed a statistically significant increase of 13 proteins with protein tyrosine kinase 2 (PTK2; also known as focal adhesion kinase FAK1) as being the protein with, out of the decreased proteins, 11 were already linked to the manifestation of neurological diseases based on pathogenic variants in the corresponding genes (Figure 2 and Table 1).  
Figure 1: Clinical findings in the p.(A20S)-SPTLC1-patient: MRI of brain (**A**) and spinal cord (**B**) revealed normal findings. Edematous alterations in musculus vastus medius, lateralis, and intermedius (black arrows in **C**). No fatty degenerations were detected, and MRI of lower legs revealed no pathological alterations (**D**). The limited quality of the MRI images is based on motion artifacts of the child during the investigation.  
Figure 2: Proteomic findings in immortalized lymphoblastoid cells derived from the p.(A20S)-SPTLC1-patient: (**A**) Protein enrichment plot along the entire abundance range. Proteins highlighted in black represent the control results, sorted by abundance. Proteins highlighted in red represent the abundance of the patient proteins, left in the same order as the control proteins to highlight differences in their expression. (**B**) Volcano-plot of proteomic findings. Red dots represent proteins demonstrating a significant decrease whereas green dots represent proteins with a significant increase in abundance. Red lines indicate the significance thresholds of \(p\)-value 0.05 (\(y\)-axis) and 2-fold increase or 0.5-fold decrease in abundance (\(x\)-axis). (**C**) Boxplot depicting the relative abundance of the SPTLC1 protein comparing control- and patient-derived samples. (**D**) GO-term based pathway analysis for up- and downregulated biological processes (green), molecular functions (blue), and cellular compartments (red) which are significantly regulated by comparing control and patient samples. Terms are sorted from top to bottom with decreasing level of significance. To achieve a better representation, the \(x\)-axis is given as 0.1 \(p\)-value (* = statistically significant).
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 3 Results and Discussion
### Surface Material Characterization
However, with the piezoresistive sensing properties as a focus, rGO is an affordable alternative for strain sensing applications even on large scale applications [49; 50].