esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Why the Electroencephalogram (EEG)?
### qEEG and Experiential Sellhood
Indeed, the increased activity (measured by qEEG) of 'Me' was found to be significantly linked to enhanced emotional, sensory, and bodily states in PTSD sufferers (criterion D, according to DSM-5), such as fear, stress, frozenness, shivering, shaking, trembling, palpitations, and sweating [301; 302; 303; 304; 305; 306; 307; 308; 309; 310; 311; 312]. These feelings and memories are usually reported as intrusive and unwanted (criterion B, according to DSM-5). Additionally, it was observed that the activity (measured by qEEG) of 'T' decreased and that this decrease was associated with a distinct lack of linguistic/contextual information and narrative to accompany the traumatic event (criterion C, according to DSM-5), which is a well-documented phenomenon in PTSD patients [313; 312]. A six-year longitudinal analysis of a single patient's recovery of self-consciousness (from a minimally conscious state until full self-consciousness) after a severe traumatic brain injury has revealed that the recovery of first-person agency (or \"Self'), representational-emotional agency (or 'Me'), and reflective agency (or 'I') was paralleled by restoration of functional integrity (measured by qEEG) in the three subnets of the SRN [299]. Of note, the recovery dynamic in the Self-Me-I aspects (and corresponding qEEG metrics) was not linear but followed a unique trajectory for every aspect (some recovered more quickly, while others lagged) and was tightly paralleled by (and significantly correlated with) findings from clinical exams and tests [299; 314]. Further, converging evidence for a breakdown of qEEG integrity within the SRN in non- and minimally communicative patients with severe brain injuries was found, and this breakdown was proportional to the degree of expression of clinical self-consciousness [287]. More specifically, it was demonstrated that the strength of qEEG integrity within the SRN was smallest or even absent in patients in a vegetative state (VS), intermediate in patients in a minimally conscious state (MCS), and highest in healthy, fully self-conscious subjects. Curiously the strongest decrease in strength of qEEG integrity as a function of loss of self-consciousness was found in the \"Self' aspect compared to the 'Me' and 'T' SRN modules. The central role of 'Self' was also found for the prediction of self-consciousness recovery: those VS patients who later recovered stable minimal or full self-consciousness in the course of the disease (up to six years post-injury) showed stronger 'Self' functional integrity (measured by qEEG) in the early stage (three months post-injury) compared to those patients who continued to stay in the persistent VS [315]. This brings us to another reason for the clinical and ethical importance of qEEG utility in the assessment of the neurophysiological and neuropphenomenological status of _unresponsive_ patients.
## 2 Materials and Methods  
### Data Description  
The data used for training the ML models is extracted from the SuperCon database. The 'Superconductivity2018' dataset, compiled by [PERSON] et al. [20], comprises 16414 experimental records of superconducting critical temperatures. The critical temperatures and related properties in the 'Superconductivity2018' dataset were originally collected from the SuperCon database, a comprehensive repository maintained by the National Institute for Materials Science (NIMS) in Japan [21]. The data were sourced from peer-reviewed experimental studies, spanning several decades and encompassing a wide range of superconducting materials. Tc values were determined using standard experimental techniques, such as resistivity, magnetic susceptibility, and specific heat measurements, and were validated through rigorous quality control processes to ensure consistency and reliability.  
The distribution and spread of Tc in the Superconductivity2018 dataset is shown in Figure 2. The dataset shows a wide range of Tc values, with a mean of approximately 17.97 K and a standard deviation of 26.98 K, indicating substantial variability in the superconducting properties of the materials included. The minimum recorded Tc is 0 K, while the maximum Tc reaches up to 143 K. Notably, the distribution of Tc values is heavily skewed towards lower temperatures, with 50% of the data (median) below 5 K. The 25 th percentile is at 0.095 K, and the 75 th percentile is at 22.5 K, suggesting that a significant portion of the dataset comprises materials with relatively low critical temperatures. This variability highlights the diversity of the materials in the dataset, encompassing both conventional and high-temperature superconductors, and underlines the challenge of developing predictive models.  
Figure 2: Distribution and spread of Tc in the Superconductivity2018 dataset. The histogram shows the frequency distribution of Tc values across the dataset, highlighting a significant skew towards lower temperatures, with most data points concentrated below 20 K. The Kernel Density Estimate (KDE) curve (in blue) illustrates the overall shape of the distribution.  
To prepare the data for building a predictive model, we first filtered out all entries where the Tc values were less than or equal to 0 K, as these values are not meaningful in the context of predicting superconducting properties. Next, to handle potential outliers that could skew the model, we applied the Interquartile Range (IQR) method. By calculating the IQR (the difference between the 75 th and 25 th percentiles) and removing values that fall below the first quartile minus 1.5 times the IQR or above the third quartile plus 1.5 times the IQR, we effectively minimized the impact of extreme values on the model's performance. After filtering out records with zero Tc values, removing outliers and duplicates, and performing the feature selection process, the final dataset consists of 11,968 records, each containing 73 features.
# Wildfire Monitoring Based on Energy Efficient Clustering Approach for FANETS
## 3 Proposed Methodology
### Metrics Used by Our Proposed Algorithm
#### 3.1.6 Mobility of a Node  
Mobility is an important factor to consider when selecting a cluster head. Consider electing a cluster head who is more stationary; that would be more reliable [2]. Re-affiliation may occur if the cluster head moves rapidly, causing nodes to become detached from one another. This occurs when a node leaves an existing cluster and joins a newer one [1]. In this scenario, only a small amount of information can be sent between the node and thecluster head, so in our proposed methodology, we use the mobility of a node as a dividing factor which is being calculated by Equation (6).  
\[\text{Mi}=\frac{\frac{1}{T}\sum_{t=1}^{T}\sqrt{\left(\text{Xt}-\text{Xt}-1\right) ^{2}+\left(\text{Yt}-\text{Yt}-1\right)+\left(\text{Zt}-\text{Zt}-1\right)}}{ \text{Calculate the weight of the node $\text{W}_{\text{i}}$ using Equation (7) as follows for each node participating in the cluster head election: \[\text{W}_{\text{i}}=\text{(w1}\times\text{T}_{\text{i}}+\text{w2}\times \text{Res}_{\text{i}}+\text{w3}\times\text{D}_{\text{i}}+\text{w4}\times\text {Total Eng}_{\text{i}}+\text{w5}\times\text{Dist}_{\text{i}})/\text{Mobility of a node (M}_{\text{i}})\] (7) where w1, w2, w3, w4 and w5 are coefficients to system criteria with certain values.
## 4 Optimization of Cluster Head Selection
### Heterogeneous Node Optimization  
The SEP protocol [15] is a heterogeneous network using cluster head selection algorithm. In the two stages of heterogeneous sensor networks, containing only advanced nodes and normal nodes, we assume that \(E_{0}\) denotes the initial energy of normal nodes, \(\gamma\) denotes the proportion of advanced nodes, \(\delta\) denotes the initial energy of advanced nodes compared to the initial energy of normal nodes. The initial energy of \(\gamma N\) nodes is \(E_{0}\rho\) in the \(N\) nodes, and the initial energy of the other nodes is \(E_{0}\). SEP sets different weighted probabilities for advanced nodes and normal nodes, respectively:  
\[\left\{\begin{array}{l}p_{adv}=\dfrac{p_{opt}}{1+(\rho-1)\gamma}\\ p_{nor}=\dfrac{p_{opt}\rho}{1+(\rho-1)\gamma}\end{array}\right. \tag{20}\]  
In general, the average probability \(p_{i}\) determines the cluster head rotation cycle \(n_{i}\) and threshold \(T(s_{i})\) of node \(s_{i}\). In Equation (20), \(p_{opt}\) is the reference value of \(p_{i}\). In an isomorphic network, all nodes have the same initial energy. Therefore, nodes are using the same \(p_{opt}\) as the reference point, an each node of the cluster head rotation cycle of the reference valueis \(1/p_{opt}\). When the network is heterogeneous, nodes should be assigned different initial energies and use different reference values. In the two stage heterogeneous network, we select the weighted probability as the node reference value that is given in Equation (19), and then replace the \(p_{opt}\) of Equation (20), then the following equation will be obtained:  
\[p_{i}=\left\{\begin{array}{l}\dfrac{p_{opt}}{1+(\rho-1)\gamma}[1-\dfrac{ \tilde{E}(r)-E_{res}(i)}{\tilde{E}(r)}](1-\dfrac{1}{\eta(m)}),\;\;\text{if}\;s _{i}\;\text{is the normal node}\\ \dfrac{p_{opt}\rho}{1+(\rho-1)\gamma}[1-\dfrac{\tilde{E}(r)-E_{res}(i)}{ \tilde{E}(r)}](1-\dfrac{1}{\eta(m)}),\;\;\text{if}\;s_{i}\;\text{is the advanced node}\end{array}\right. \tag{21}\]  
We can extend this model to the case of a multi-level heterogeneous network. Through the use of the weighted probability of Equation (5) instead of the \(p_{opt}\) in Equation (8), one can obtain:  
\[p_{i}=\dfrac{p_{opt}N(1+\rho_{i})}{(N+\sum\limits_{i=1}^{N}\rho_{i})}[1-\dfrac {\tilde{E}(r)-E_{res}(i)}{\tilde{E}(r)}](1-\dfrac{1}{\eta(m)}) \tag{22}\]  
In which, \(I_{i}=(\sum\limits_{i=1}^{N}\rho_{i})/p_{opt}\rho_{i}\) denotes the basic rotation cycle of \(s_{i}\), which is termed as the reference period. The corresponding \(I_{i}\) is different if the initial energy is different, \(n_{i}=1/p_{i}\). It can be inferred from Equation (22) that the rotation cycle \(n_{i}\) of each node fluctuates around the reference cycle \(I_{i}\) according to the change of residual energy. If \(\tilde{E}(r)<E_{res}(i)\), then \(n_{i}<I_{i}\). This means that nodes with higher initial and residual energy than the low energy nodes have greater chances of becoming the cluster head nodes, so that the network energy can be evenly distributed in the evolution process.
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 4 Proposed VM Placement Strategy
### Satisfying Resource Constraints
Then, the VMs are grouped using the clustering function on Line 7, as described in Section 4.2. After grouping the VMs, a match of groups to PMs is identified by the Hungarian_matching function on Line 8, as explained in Section 4.3. Using the VM groups, PMs, and \(A\), the solution \(\mathbf{X}_{g}\) is obtained on Line 9. If the resources required by any group conflict with the resource constraints of the PM matched to it, the radii for VM groups are adjusted on Line 10 according to Equation (8), as described in Section 4.4. However, if all groupsconflict resource constraints of the PMs, there may not exist any match of groups to PMs that fully satisfies the resource constraints, even radii for the groups are adjusted. In this case, the algorithm stops to find a solution on Line 12. Otherwise, the adjusted radii for VM groups are used for clustering the VMs. If there exists a solution for a given \(g\), the loop spanning Lines 7-15 stops. Then, \(g\) is reduced by one, and this procedure is repeated. Once the algorithm has finished, a set of final solutions for the VM placement has been selected from possible solutions for different values of \(g\). For each selected solution, the data rate \(D_{p}\) in the physical network is computed using Equation (1); the final solution is taken as the one that minimizes the data volume in the physical network of data center. Additionally, the traffic matrix \(W\) may change over time due to dynamic traffic fluctuation. In this case, the total data rate in physical network is computed again using Equation (1), and the VM placement is re-taken if the data rate is considerably different compared to the data rate obtained previously. After obtaining a result of the Algorithm 1, the decision of the actual placement can be made according to the new result.
## 3 Evaluation  
### Flat Spring Stiffness Function  
The curvature of the manipulator proposed in this study was significantly influenced by the characteristics of the flat springs placed between the segments. These flat springs had a complex shape. Therefore, their spring coefficients are nonlinear as opposed to constant values in the case of a general helical spring [30]. The stiffness values of these flat springs could be obtained using an analysis program. However, owing to their complex shape, the stiffness values were obtained through experiments instead. The left panel in Figure 5a schematically shows the direction in which the flat spring received the force when the segment of the manipulator was bent. Stiffness could be obtained using Equation (5) and the set. The image of the compressed flat springs was captured using a telecentric lens (Edmund, United States) with the distance being calculated in pixels. Flat springs with the following four different thicknesses were tested: 0.08, 0.1, 0.12, and 0.14 (mm). Figure 6 shows the experimental results; the relationship between the compressed distance and force based on the initial position is expressed as stiffness, and the maximum compression distance \(l_{md}\) for each thickness was measured to be 0.68, 0.58, 0.33, 0.22 (mm) when the thicknesses were 0.08, 0.1, 0.12, and 0.14 (mm), respectively. It was confirmed that as the pulled distance increased, the stiffness increased owing to the restriction posed by the segment shape or strong resistance. The distance data, from 0.2 to 1.0 mm, were extracted and a trend line in the form of a linear function was obtained by linear regression. The data were applied to the curvature prediction simulation. The trend line function for each thickness can be seen in the top left panel of Figure 6. Through an experiment, the stiffness  
Figure 4: Flow chart for predicting the bending motion of hyper-redundant manipulator with non-uniform curvature and formulas applied in each step.  
function \(k_{j}\) based on the thickness of the flat spring was obtained. All lines had a positive slope; the greater the thickness, the higher the y-intercept value (initial stiffness).  
The shape of a flat spring with a thickness of 0.1 mm is shown in the right panel in Figure 5a. First, the design of the flat spring to be manufactured was traced on the thin metal plate, which was then cut. The cut metal plate was held between a jig for molding by compression and heat. The manufactured flat springs (SUS 302--material used for general springs with high rigidity) were placed between two segments and fixed by an adhesive to the indentation of the lower segment. After applying 100 g pre-tension using the driving part, the cables were pulled and released in steps of 0.1 mm to bend the segment.  
Figure 5: Schematic diagram of external force and displacement measured to calculate stiffness and a fabricated flat spring; (**a**) Actual experiment to measure external force and displacement; (**b**) Fabricated flat spring.  
Figure 6: Stiffness graph and linearized trend of the flat springs obtained experimentally.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Image Pre-Processing
#### 3.1.2 Uneven Illumination Correction  
According to Retinex theory [30], a visible image consists of two components: illumination and reflectance. That is, the visible image \(I(x,y)\) can be described as the product of illumination component and reflectance component as follows [30]  
\[I(x,y)=L(x,y)\bullet R(x,y) \tag{2}\]  
where \((x,y)\) denote the pixel coordinates. \(L(x,y)\) and \(R(x,y)\) represent the illumination and reflectance, respectively. In general, the illumination components exist in the low-frequency part of the image and varies smoothly. Therefore, to correct the uneven illumination phenomenon, it is crucial to extract the illumination component efficiently from the observed image.  
There are several filter based methods for extracting illumination component [31; 32; 33]. In this paper, given the outstanding edge-preserving capabilities and the low computation complexity, the rolling guidance filter (RGF) [28] is used to extract the illumination. It is a new type of edge-preserving filter based on a scale structure, which can be formulated as follows  
\[J^{t+1}(p)=\frac{1}{K_{p}}\sum_{q\in N(p)}\exp(-\frac{(\|p-q\|)^{2}}{2\sigma_{ s}^{2}}-\frac{(\|J^{t}(p)-J^{t}(q)\|)^{2}}{2\sigma_{r}^{2}})I(q) \tag{3}\]where \(I^{t+1}\) denotes the \(t+1\) iteration result which is the extracted illumination component of the input image \(I\) (also the \(J^{1}\)). \(K_{p}\) stands for the normalization function. Other parameters are the same as the one mentioned above.  
After that, an uneven illumination correction function is constructed. In the light of the parameter adaptive gamma function (PEGF) mentioned in References [29; 34], the constructed correction function is given by  
\[O(x,y)=I(x,y)^{\gamma},\gamma=2^{\frac{m-1(x,y)}{m}} \tag{4}\]  
where \(O(x,y)\) stands for the enhanced intensity value. \(\gamma\) denotes the exponential value including the dynamic local characteristic of the intensity enhancement. \(m\) represents the mean value of the intensity for illumination component. The whole procedure of the two-stage pre-processing method is shown as in Figure 2.
## 5 Conclusions  
To enhance the reliability and security of modern power transmission systems, protections are commonly utilized for fault detection/location. It is a well-recognized fact that current differential protection schemes provide sensitive protection with crisp demarcation of the protection zones. Since differential comparison of the local and remote end currents must correspond to the same time instant, inaccuracies in a current differential protection scheme would be inevitable due to communication issues, such as time delays and bit errors. Existing methods for studying the effects of inconsistent communication delays, consistent communication delay, and bit errors on current differential protection are mainly qualitative or experimental methods. This paper provides a quantitative method.  
This study presents three probability models to assess the impact of communication delays and bit errors on differential protection. Analysis of the mechanisms of malfunction of relay protection caused by communication delays and bit errors is one of the contributions of this paper. Typically, the analysis  
Figure 19: Bit error rate sequence.  
Figure 20: Real time refuse-operation probability.  
of malfunctions of differential protection is mainly experiment-based, which can't fully reveal the underlying causes. In general, a channel's consistent delay or bit error results in refuse-operation incidents, while a channel's inconsistent delay normally causes false trips. In this paper, based on the assumption that communication delays follows a normal distribution and bit error follows a Poisson distribution, probabilistic models of false trips and refuse-operation faults are proposed. In these models, the probabilistic relationships between communication issues and protection malfunctions are established. With the proposed models, typical data and parameters are adopted to demonstrate the effects of communication delays and bit errors on differential protection. The simulation results show that more severe communication problems result in a larger probability of malfunction of differential protection.
## 2 Materials and Methods
### Volatile Compound Analysis  
The volatile compounds from egg yolk of Hy-Line Brown, Xueyu White, and Xinyang Blue were detected by GC-MS. The detection settings were proposed and modified according to [PERSON] et al. [34]. Five grams of cooked egg yolk sample were weighed and placed in the sample vial. A total of 30 \(\mu\)L internal standard solution (cyclohexanone: methanol = 1:1000) was added to the sample vial. Then, in the GC-MS port, the volatile compounds were desorbed instantly by SPME equipped with a divinylbenzene/carboxen/polymethylsiloxane 50/30 mm fiber (2 cm, DVB/CAR/PDMS, gray, Supelco, Atlanta, GA, USA) at 250 \({}^{\circ}\)C for 5 min and then extracted at 60 \({}^{\circ}\)C for 40 min. The volatile compounds for analysis were performed on a GC-MS (model 7890B-5977B, Agilent Technologies, Santa Clara, CA, USA). DB-5 column (30 m \(\times\) 0.320 mm \(\times\) 0.25\(\mu\)m, Agilent Technologies, Santa Clara, CA, USA) was used in split-less mode to separate headspace volatiles with flow rates of 2.0 mL/min of helium (99.999%). The above procedures were operated under the following conditions: 40 \({}^{\circ}\)C oven temperature for 3 min, 2.5 \({}^{\circ}\)C/min ramp to 130 \({}^{\circ}\)C for 3 min, and 9 \({}^{\circ}\)C/min ramp to 250 \({}^{\circ}\)C for 3 min. An electron impact mass spectrometer was used to detect mass spectra between 35 and 400 m/z using electron energy of 70 eV and 230 \({}^{\circ}\)C source temperature. Mass spectra from the NIST 17 library (National Institute of Standards and Technology 14.L, Gaithersburg, MD, USA) and Wiley library were compared to tentatively identify volatile compounds. A match of at least 45% was considered tentative identification, and the relative concentration (ng/g) of each volatile compound was quantified. We presented the results on volatile compounds as micrograms of volatile compound per gram of yolk (micrograms/g).
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 3 Results
### Dryland Areas in America
#### 3.2.4 Drivers for Green and Desertification  
In this section, we analyzed the drivers for green and desertification of American dryland. Most areas show positive trend. CO\({}_{2}\) always shows positive effects (Figure 13a). For the whole dryland, CO\({}_{2}\) contributes the most, accounting for 42.9%. LU is a little less than CO\({}_{2}\) (39.3%). CC accounts for 14.8%. Among the three regions, the West shows apparently positive trend. Here, CO\({}_{2}\) is the primary driver in 53.7%. CV notably has negative effects on vegetation growth. For the Midwest, LU (46.4%) is the largest driver of change. We also observed some part of desertification areas. For the whole regions that experienced desertification, we found that LU is the largest attributed driver, followed by CC and CV. For the West, the positive effects of CO\({}_{2}\) almost offset the negative effects of LU. Even though the effects of CC and CV are a little smaller than LU and CO\({}_{2}\), climate remains a significant driver of land degradation. In the West dryland, the land degradation expanded due to the temporal precipitation distribution characterized by a decrease in summer rains and the concomitant increase in winter rain [11].  
Figure 12: The regional mean and magnitude (mean absolute value) of the different drivers of change in NDVI\({}_{m}\) of American dryland areas. The error bars show the SD of grid cells.  
Figure 13: (**a**) The regional mean and magnitude (mean absolute value) of the different drivers of positive change in NDVI\({}_{m}\) of American dryland areas. The error bars show the SD of grid cells. (**b**) The same as (**a**) for negative change.