esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Open Research Issues
### Emerging I2O WBAN Issues  
#### 5.2.1 I2O WBAN Packet Design  
To incorporate the diverse QoS requirements of heterogeneous communication networks into a packet format, superframe structures are promising technologies that allow I2O devices with a particular traffic type to transmit during the period that is best suited in meeting the corresponding QoS [42]. Here, we assume that this aspect is in existence, whilst acknowledging that there is further work to be done in order to bring it into routine practice.
## 6 Simulation and Discussion
### Probability Analysis in Heterogeneous Networks  
Similar to the homogenous WSNs, the probabilities in heterogeneous WSNs are shown in Figures 24 and 25.  
Figures 24 and 25 indicate that the properties of the probability in heterogeneous networks are similar to that in homogeneous networks. The differences are the values of the probability and the optimal transmission range. In Figures 22 and 24, the minimum original transmission ranges (which the probability is 1) are consistent with the maximum neighbor distances that are shown in Figure 21. The differences between Figures 22 and 24 are due to the different boundary conditions in heterogeneous networks and homogeneous networks. These can be found in Equations (16) and (34), Figures 9 and 14. Since the feasible region in heterogeneous networks is larger than that in homogeneous networks, the probability in heterogeneous networks is also larger than that in homogeneous networks under the same network conditions. For instance, in Figure 24, when the node density is 0.01 and the original transmission range is 10, the probability is 0.897; while in Figure 23, this probability is 0.862, which is smaller than that in Figure 24. The same conclusion can be obtained when the node density and the original transmission range are changed.  
Similar to the homogenous network, in Figure 25, with the increasing of transmission range, the probability under specific transmission range increases from 0 to 0.82 and reaches the maximum value when the transmission range is 0.84\(r\); the probability decreases when the transmission range is larger than 0.84\(r\). Therefore, the maximum probability is 0.82 and the optimal transmission range is 0.84\(r\) in the homogenous network. This conclusion is interesting, since the maximum probability and the optimal transmission range are similar in homogenous WSNs and heterogeneous WSNs.
## 2 DPCC with Delay Compensation
### The Principles of One-Step Delay Compensation DPCC
\end{bmatrix}-H(k+1)\Bigg{\}} \tag{5}\]In cases of very short current control periods, the \(dq\)-axis reference current and \(\omega_{e}(k)\) can be equivalent to remain constant over adjacent sampling periods, i.e., \(i_{dq}^{*}(k+2)\approx i_{dq}^{*}(k),\omega_{e}(k+1)\approx\omega_{e}(k)\), in which case, (5) can be rewritten as the following:  
\[\left[ \begin{array}{c}u_{d}(k+1)\\ u_{q}(k+1)\end{array} \right]=G^{-1}\left\{\left[ \begin{array}{c}i_{d}^{*}(k )\\ i_{q}^{*}(k)\end{array} \right]-F(k)\left[ \begin{array}{c}i_{d}(k+1)\\ i_{q}(k+1)\end{array} \right]-H(k)\right\} \tag{6}\]  
A flowchart of delay compensation DPCC is shown in Figure 1, where the predictive currents \(i_{d}(k+1)\) and \(i_{q}(k+1)\) are calculated using (3).
## 3 Results
### Statistical Results  
The linear mixed-effects model revealed a significant overall fit (Likelihood Ratio Test: \(\chi^{2}(6)=16.13\), \(p=0.013\)), with a marginal R\({}^{2}\) of 0.229, indicating that the fixed effects accounted for 22.9% of the variance in mood scores. Omnibus tests showed a significant main effect of Time (F(2, 63) = 6.44, \(p=0.003\)), as well as a significant main effect of the MoCA group (F(1, 63) = 4.31, \(p=0.042\)). However, the Time \(\times\) MoCA interaction was not significant (F(2, 63) = 0.55, \(p=0.581\)), suggesting that mood improved over time similarly in both cognitive groups.  
Post hoc analyses revealed a significant decrease in mood scores from pre- to mid-exposure (\(\beta=-0.56\), \(p=0.007\)) and from pre- to post-exposure (\(\beta=-0.67\), \(p=0.001\)), indicating an overall improvement in mood (Figure 3). Additionally, participants in the high-MoCA group reported significantly more positive mood scores overall (\(\beta=0.34\), \(p=0.042\)). No significant interaction effects were observed between Time and MoCA group.  
Estimated marginal means showed a consistent decline in mood scores across the three time points (Figure 4), confirming a general trend of mood improvement: pre (M = 1.51, SE = 0.14), mid (M = 0.95, SE = 0.14), and post (M = 0.85, SE = 0.14). All statistical analyses are provided in Supplementary Table S2.  
Figure 4: Mood changes over time across all participants. Estimated marginal means and standard errors of VAS mood scores at three time points: pre-, mid-, and post-video exposure. A significant improvement in mood was observed over time, with mood scores decreasing from pre (M = 1.51) to mid (M = 0.95) and post (M = 0.85) exposure (\(p<0.01\)). Lower scores indicate a more positive mood.  
Figure 3: Mood scores by MoCA cognitive group. Boxplots show the distribution of overall mood scores (VAS averaged across the three time points) for participants with a low (MoCA \(\leq\) 21) and high (MoCA \(\geq\) 22) cognitive performance. The low-MoCA group showed significantly more positive mood scores overall compared to the high-MoCA group (\(p=0.042\)).
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### Asia-Pacific
#### 4.3.1 China  
Electricity companies in China continue with an extensive deployment of _SM_s as part of a national plan that aims to improve the national electricity infrastructure and shift towards green energy supply. This situation has led China to become the largest market for _SM_s in the world [46]. The installed base of _SM_s is expected to grow from more than 139 million units in 2012 to 377 million units by 2020, reaching 74% market penetration [75]. The Smart Grid Corporation of China (SGCC) is China's sole state-owned electric utility company and the largest utility company in the world, covering power supply to 88% of China [76].  
Figure 8: Estimation of the total number of _SM_s in installed Asia-Pacific countries by 2020 (Source: Accenture [55]). Reprinted with the permission of Accenture, “Realizing the Full Potential of _Smart Metering_”, published by Accenture, 2015.  
The most important \(SM\) deployment in China corresponds to the SGCC's Smart Grid Plan, which aims at developing a modern power grid based on a strong information and communication platform, with an _Ultra High Voltage_ (_UHV_) grid backbone and subordinate grids coordinated at all levels [76]. By the end of 2011, SGCC had implemented 238 \(SG\) pilot projects ranging from connecting wind power plants to automating distribution networks to metering households. Regarding _Smart Metering_, there were three remarkable deployments: (i) 26 provinces and 2.2 million users in 2009; (ii) 33 million \(SM\)s installed in 2010; and (iii) 33 million \(SM\)s installed in 2011 [77]. Most of the installed \(SM\)s employ _PLC_; however, SGCC is also running an \(SG\) project with 86,000 premises connected to the grid using \(PON\) technology [78]. Additionally, under the Smart Grid Plan, SGCC has developed three standards related to _Smart Metering_: (i) Q/GDW 376.1 (communication protocol between master station and terminals); (ii) Q/GDW 376.2 (interface for local communication module of concentrators); and (iii) Q/GDW 377 (technical specification for security and protection).
## 5 Epigenetic Regulation of ALS  
### Studies Using Human Cell and Mouse Models  
#### 5.1.1 DNA Methylation  
DNA methyltransferase1 (Dnmt1) is a maintenance DNA methyltransferase, whereas Dnmt3 is a de novo DNA methyltransferase. Dnmt1, Dnmt3a, and 5-methylcytosineare upregulated in the brain, and notably in the spinal cord of ALS patients [45]. Mice lacking Dnmt3a exhibited hypoactivity, defects in neuromuscular function, and decreased numbers of motor neurons, similar to the mouse ALS models targeting _SOD1_[46]. Compared with wild-type mice, the level of Dnmt3a in the mitochondria was significantly reduced in the central nervous system (CNS) and skeletal muscle of these ALS model mice [47]. Overexpression of Dnmt3a, but not Dnmt1, induced the degeneration of neurons, whereas downregulation of Dnmt3a repressed apoptosis of cultured neurons. Moreover, inhibition of Dnmt activities by specific inhibitors, RG108 and procainamide, protected motor neurons from hypermethylation of DNA and apoptosis in both cell culture and ALS model mice [13; 48]. Thus, Dnms are important in the pathogenesis of ALS (Table 2).
## 3 Network and Energy Models
### Energy Model  
In this study, the well-established radio-energy dissipation model, as referenced in [36; 37; 40; 41; 45; 54], is utilised to calculate energy consumption and constantly update the residual energy of the network nodes. In this model, \(E_{elce}\) represents the energy used by transmitter or receiver electronics in the transceiver circuit. The model accounts for power loss in both free space (\(\varepsilon_{fs}\)) as well as multi-path (\(\varepsilon_{mp}\)) channel models. The adopted model is described as follows, adapted from [15]:  
The energy spent by the radio at each node to transmit an \(l\)-bit message over a distance \(d\) is denoted as \(E_{Trans}(l,d)\), where:  
* \(l\) is the number of bits in the transmitted packet;
* \(d\) is the distance between the transmitter and receiver;
* \(\varepsilon_{fs}\) is the free space energy dissipation coefficient;
* \(\varepsilon_{mp}\) is the multi-path fading energy dissipation coefficient.  
The transmission energy, \(E_{Trans}(l,d)\), is calculated as:  
\[E_{Trans}(l,d)=\begin{cases}l\times E_{elce}+l\times\varepsilon_{fs}\times d^{ 2};&d<d_{o}\\ l\times E_{elce}+l\times\varepsilon_{mp}\times d^{4};&d\geq d_{o}\end{cases}\]  
where \(d_{o}\) is the threshold distance that determines the switch between the free space and multi-path models and is calculated as:  
\[d_{o}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\]  
Similarly, the energy spent by the radio to receive the message is denoted by \(E_{elce}(l)\), which is calculated as:  
\[E_{rec}(l)=l\times E_{elce}\]
## 2 Materials and Methods
### Histogram Normalization
Obviously, \(\alpha=\gamma_{0}\) and \(\beta=\gamma_{1}\). To compute the two coefficients of the linear transform applied to the intensities from a certain data channel of any record, first we identify the 25 th-percentile and 75 th-percentile value of the original intensities and denote them by \(p_{25}\) and \(p_{75}\), respectively. Then, we define the \(I\to aI+b\) transform of the intensities in such a way that \(p_{25}\) is mapped onto \(\gamma_{0.4}\) and \(p_{75}\) becomes \(\gamma_{0.6}\). This assumption leads to the coefficients:  
\[a=\frac{\gamma_{0.6}-\gamma_{0.4}}{p_{75}-p_{25}}\text{ and }b=\frac{\gamma_{0.4 }p_{75}-\gamma_{0.6}p_{25}}{p_{75}-p_{25}}\enspace. \tag{1}\]  
Finally, all pixel intensities from the given data channel of the current MRI record are transformed according to the formula:  
\[\hat{I}=\min\{\max\{\alpha,aI+b\},\beta\}\enspace, \tag{2}\]  
using the values \(a\) and \(b\) given by Equation (1), where \(I\) and \(\hat{I}\) stand for the original and transformed intensities, respectively. This formula assures that the transformed intensities are always situated within the target interval \([\alpha,\beta]\). Figure 3: Slice Number 100 from the T1 volume of twenty MRI records from the BraTS 2015 LGG data, without histogram normalization. These pixel intensities are obviously not suitable for any supervised classification.
## 1 Introduction
Therefore, this study is focused on the quantification and detection of HEs in soils [25] using a QCL source to enable remote sensing [42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. The limit of detection (LOD) was calculated for QCL spectroscopy based on the calibration curves of one HE in solid mixtures. The statistical figure of merit (FM) was achieved using the partial least squares (PLS) multivariate algorithm. Other FMs were also calculated. Furthermore, AI methods were employed to achieve HE detection. In the future, two main applications can be developed based on the outcomes of this research. First, the proposed methods can be used to detect explosives on ordinary, non-ideal, low-reflective, real-world solid matrices. Second, the methods can be employed to detect HEs on natural solid matrices; for example, they can be used tolocate landmines in sites affected by war and conflicts throughout the world, both for military purposes and implementing humanitarian demining applications.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### XFEM/GFEM Enrichment Functions for LEFM
#### 3.2.5 Ill-Conditioning  
An additional problem related to enrichment is the linear dependence between the enriched and standard part of the approximation. As far as jump enrichment is concerned, linear dependence may arise if the crack either intersects, or lies very close to a node. Then, the enriched shape function of this node is identical or very close to its standard FE shape function leading to linear dependence problems. A commonly used technique to avoid this problem is \"snapping\", which consists of not enriching nodes if they are very close to the crack surface [103]. Other approaches involve pre-conditioning [125; 126] and stabilization in the element [127] or global equilibrium equations [128] level.  
With respect to tip enrichment, ill-conditioning can arise when geometrical enrichment is used due to the fact that away from the singularity the tip enrichment functions tend to become linearly dependent both with respect to the FE part of the approximation and each other [101; 129]. To overcome this issue several alternatives have been proposed such as altering the partition of unity basis used to pre-multiply the tip enrichment functions [105; 121; 130], preconditioners [106; 125], stabilization [127] and enrichment function orthogonalization [101; 129]. Moreover, vector enrichment functions have been shown to lead to improved conditioning [114], and if further combined to the stable GFEM [111; 112] they can lead to optimal growth rates of the scaled condition number.
## 4 Experiment and Result Analysis
### Supervised Feature Selection  
#### 4.2.1 Dataset  
To evaluate the effectiveness of Algorithm 2, we conducted experiments on synthetic data sets and real data sets, respectively.  
The synthetic data set contained 500 instances and 100-dimensional features. Five-hundred instances in 100-dimensional space were generated, in which two features defined an XOR function, whereas the remaining 98 features were irrelevant, sampled independently from a zero-mean and one-standard-deviation normal distribution.  
We also present the results obtained on six real data sets, called DLBCL, Mnist, Splice, Wybc, USPS, and Arcene. Since the Splice and Wybc datasets had fewer features, we artificially added 2000 irrelevant features to them, and the feature values of the irrelevant features were all obtained by sampling from the normal distribution \(\mathbb{N}(0,1)\). The detailed information on the data sets is shown in Table 4.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code
### Data Encoding Storage and Recovery
#### 3.2.4 Data Decoding  
Data decoding is the inverse process of data encoding. It is necessary to decode multiple data blocks retrieved from the storage nodes and merge them into the data file that the user needs.  
Suppose that some data blocks are lost during transmission. We need to recover the original data from the remaining data blocks. Since any submatrix of the Cauchy matrix is invertible, as long as we receive \(k\) linearly independent data blocks, we can recover the original data through the decoding matrix \(T\) (Equation (7)).  
Assume that some data blocks are lost during transmission, but we still receive \(k\) data blocks \((R_{1},R_{2},\ldots,R_{k})\).These data blocks correspond to certain rows in the generator matrix \(G\). We need to extract the corresponding \(k\) rows from the generator matrix \(G\) to form a \(k\times k\) submatrix \(G^{\prime}\).  
Let the row index corresponding to the received data block be \(r_{1},r_{2},\ldots,r_{k}\) and \(G_{r_{i},j}\) be the element of row \(r_{1}\) and column \(j\) of the generation m atrix \(G\). Then, submatrix \(G^{\prime}\) can be expressed as shown in Equation (6):  
\[G^{\prime}=\begin{bmatrix}G_{r_{1},1}&G_{r_{1},2}&\cdots&G_{r_{1},k}\\ G_{r_{2},1}&G_{r_{2},2}&\cdots&G_{r_{2},k}\\ \vdots&\vdots&\ddots&\vdots\\ G_{r_{k},1}&G_{r_{k},2}&\cdots&G_{r_{k},k}\\ \end{bmatrix} \tag{6}\]  
Since any submatrix of the Cauchy matrix is invertible, submatrix \(G^{\prime}\) is invertible. We can construct the decoding matrix \(T\) by calculating the inverse matrix of \(G^{\prime}\):  
\[T=\left(G^{\prime}\right)^{-1} \tag{7}\]  
The decoding matrix \(T\) is a \(k\times k\) matrix, and its element \(T_{i,j}\) satisfies Equation (8):  
\[T\times G^{\prime}=I \tag{8}\]  
By decoding the matrix \(T\), we can recover the original data block \((F_{1},F_{2},\ldots,F_{k})\) from the received data block \((R_{1},R_{2},\ldots,R_{k})\). The decoding process can be expressed as shown in Equation (9):  
\[(F_{1},F_{2},\ldots,F_{k})^{T}=T\times(R_{1},R_{2},\ldots,R_{k})^{T} \tag{9}\]  
Here, \(T_{i,j}\) is the element of row \(i\) and column \(j\) of the decoding matrix \(T\), and \(R_{j}\) is the \(j\)th received data block. Each raw data block \(F_{i}\) can be calculated using Equation (10):  
\[F_{i}=\sum_{j=1}^{k}T_{i,j}\times R_{j} \tag{10}\]
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 3 Mechanical Passive Constant Flow Regulators
### Pillar Valves in Parallel
#### 3.1.2 Devices with Compliant Elastomeric Membrane(s)
Saturation was obtained for pushup valve width of 120, 130, and 140 \(\upmu\)m, with saturation flow rate and pressure of 0.0027 mL/min in the range \([350;620]\) mbar, 0.0018 mL/min in the range \([280;620]\) mbar, and 0.0015 mL/min in the range \([210;620]\) mbar, respectively. As the valve width increases, the saturated flow rate and pressure decrease, as expected [39; 40]. The authors reported that valves having widths of 150 \(\upmu\)m and more would irreversibly stick shut during either fabrication or operation [38]. The saturation point of this passive constant flow regulator for Newtonian fluids can thus be tuned by adjusting the detour channel ratio and the valve dimensions. These 3D structures in PDMS enable the miniaturization of autoregulatory devices for biochemical and biomedical applications.
# A Distributed Automatic Video Annotation Platform
## 4 Spatiotemporal Based Video Annotation
### Local Directional Ternary Pattern-Three Orthogonal Plane (LDTP-TOP)  
LBP-TOP [44] can capture the dynamic features from the video data; however, it suffers from noise and is sensitive to lighting illumination as it uses LBP for its computation. In order to overcome the issues of LBP-TOP, we proposed a new descriptor, namely, local directional ternary pattern-three orthogonal plane (LDTP-TOP), which includes shape, texture, and motion information. LDTP captures the spatial features that are used for texture analysis and we adopt LDTP for the temporal feature extraction by using three orthogonal planes, XY, XT, and YT. We take three consecutive frames \(I_{t-1}\), \(I_{t}\), \(I_{t+1}\) at the same time interval. In order to preserve temporal information, we used space-time information YT and XT. Figure 6 shows an example of XY, YT, and XT computation. The resultant output feature size \(6\times 2^{P}\) is much smaller than the feature produced by the VLBP: feature size \(2^{3P+2}\). Therefore the calculation becomes comparatively much easier. In this approach, we get the three orthogonal planes information from three consecutive frames. Then, we use the modified Frei-Chen mask represented in Figure 4 for calculating edge response for each XY, XT, and YT plane.  
After deriving the edge response (ER) for the neighbor \((ER_{P})\) pixels and center \((ER_{C})\) pixel, we use Equations (8) and (9) to calculate the ternary information, LDTP-TOP upper and LDTP-TOP lower respectively. Figure 7 illustrates the details of proposed LDTP-TOP algorithm.  
\[LDTP-TOPU_{P,R}=2^{8}.\delta(\alpha,ER_{C})+\sum_{p=0}^{7}2^{p}.\delta(\beta_{ p},ER_{p}) \tag{8}\]  
\[LDTP-TOPL_{P,R}=2^{8}.\eta(\alpha,ER_{C})+\sum_{p=0}^{7}2^{p}.\eta(\beta_{p}, ER_{p}) \tag{9}\]  
Here, we also compute the gradient magnitude \(\beta\) by subtracting the neighbor pixel from its center pixel value using Equation (5). Furthermore, we calculate mean \((\mu)\) value of 3 \(\times\) 3 neighbor patch in order to remove noise through Equation (3); then computation of center gradient magnitude \(\alpha\) is done using Equation (4). Afterward, both the edge response and gradient of center-pixel \(\alpha\), and neighbor-pixel \(\beta\) are fed into the Equations (8) and (9) for LDTP-TOP upper and LDTP-TOP lower computation respectively. Finally three LDTP-TOP upper and three LDTP-TOP lower from the XY, YT, and XT are concatenated for final feature generation using Equation (10).  
Figure 5: Illustration of local directional ternary pattern (LDTP).  
\[\begin{split} LDTP-TOP=LDTP-TOPU_{P,R}(XY)\,||\,LDTP-TOPU_{P,R}(YT) \,||\,LDTP-TOPU_{P,R}(XT)\\ ||\,LDTP-TOPL_{P,R}(XY)\,||\,LDTP-TOPL_{P,R}(YT)\,||\,LDTP-TOPL_{P,R}(XT) \end{split} \tag{10}\]