esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Induction of Differentiation  
The phenolic extracts were tested for differentiating activity at a range of concentrations on HL-60 cells. Exponentially growing cells (50 \(\mu\)L) were seeded at a concentration of \(1\times 10^{5}\) cells/mL into separate wells of a 96-well plate and treated with the crude EVOO phenolic extract at 100 ppm, 10 ppm and 1 ppm. Within the 100 ppm crude phenolic extract that was found to be effective at inducing differentiation (Supplementary Figure S1), tyrosol constituted a peak area of 9.23% or a concentration of 9.23 ppm. This concentration of the pure tyrosol purchased from the chemical suppliers was therefore used for all subsequent experiments outlined in this section. Exponentially growing cells (50 \(\upmu\)L) were again seeded at a concentration of \(1\times 10^{5}\) cells/mL into separate wells of a 96-well plate and treated with 50 \(\upmu\)L test reagent (9.23 ppm tyrosol), positive controls phorbol myristate acetate (PMA) at a concentration of 10 nM for monocytic differentiation, and 1.6% dimethyl sulfoxide (DMSO) for granulocyte differentiation, as well as 0.5% ethanol as the vehicle control. The negative control consisted of untreated cells in RPMI medium. All conditions were prepared in complete RPMI. All tests were carried out in triplicate and repeated as three separate experiments (\(n=9\)).
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 3 Coordinated Control Strategy
### Modified Efficient Modal Control
#### 3.2.3 Gain Modulation  
The maximum applicable gains must be modulated as follows:  
\[g_{i,j}=w_{h_{i,j}}\cdot w_{Y_{i,j}}\cdot g_{max,j,j}\cdot\gamma \tag{12}\]  
where \(w_{h_{i,j}}\) and \(w_{Y_{i,j}}\) are the introduce damping and displacement weights respectively. The parameter \(g_{max,i,j}\) is the maximum gain which can be applied to avoid saturation and instability phenomena.  
This consideration is only valid if the \(j\)-th actuator just works on the \(i\)-th mode and neither other modes are controlled nor other actuators work. As described before, the EMC allows to reduce the number of DOF related to the gains setting. The only parameter which must be defined is \(\gamma\). The parameter \(\gamma\) has the same value for all \(g_{i,j}\). This additional multiplication is necessary since the overall combination of gain could lead the system to instability, as shown in [15; 16]. In this case, \(\gamma\) is initially set to 1 and it must be reduced till stability is reached or if some actuator saturates. The evaluation of the overall degree of saturation \(S_{j}\) is provided by the sum of each degree of saturation \(S_{i,j}\) related to each controlled mode. The derivation of the degree of saturation will be carried out in the next sections.
# Experimental Study on the Damage of Optical Materials by out of Band Composite Laser
## 3 Experimental Study of Composite Laser Damaged Flat Window  
A composite laser damage effect experimental platform was set up, as shown in Figure 1. The continuous and pulsed laser triggering was controlled by a digital delay pulse generator, and the laser parameters were monitored online by a spectroscope. Before starting the experiment, we used the coaxial indicating light to fix the spot position on the flat window. When ready, we used the digital delay pulse generator to control laser delay and trigger. During the experiment, the laser status was monitored with a beam quality analyzer and a power meter. The laser irradiation time was 5 s, and the average value was recorded by repeating the experiment ten times for each power and energy value. The experimental observation system consisted of a visible high-speed camera, an infrared thermal imager, and a plasma spectrometer. We determined whether the window was damaged according to the temporal and spatial distribution of the spot collected by the plasma spectrometer. We used the photodetector to monitor the window mirror for body damage. The model of the used CW laser was of the fundamental mode with a Gaussian distribution. The model of the used pulsed laser was dominated by the fundamental mode that was mixed with some higher-order modes. The laser parameters are shown in Table 1. The targets used in the experiments were silicon-based and germanium-based flat window. The substrate material parameters are shown in Table 2.  
First, two kinds of window were used to measure the absorptivity. We used a 1.06 um continuous laser to irradiate the flat window and measuring the reflected power with a power meter, since the two substrates were not transparent to the Near-Infrared band; as such, the absorptivity shown in Table 3 could be obtained.
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 4 Experiments, Results and Discussion
### Results and Analysis
For the same goal, another set of five images was randomly chosen from the test set and the extracted segmentation maps were overlaid on top of the ground truth. The green pixels (TP) represent the accurately detected road pixels while the blue lines (FP) denote the pixels mistakenly assigned as road pixels. Finally, the red lines (FN) illustrate areas where the models could not detect the roads and classified them as the background. In general, the models can distinguish roads from areas such as building roofs, rivers, or the parts of roads occluded with trees. However, the models treat parking lots or pavements alongside buildings or houses as roads since the context information of these parts of images is similar to the structure of roads. Moreover, the models struggle with the images where roads are occluded by the shadow of buildings and the ones which are small in width and light grey. On top of that, when the images and their ground truth labels were examined thoroughly, it was detected that not all roads were labeled but were, instead, classified as the background. This might be one of the reasons why the models are struggling to perform properly on those aforementioned areas. Figure 9: Error analysis of models: visual comparison of road extraction results by the baselines and the proposed models on randomly chosen images from the test dataset. The first two rows-(**a**,**b**) represent the original image and the ground truth, respectively. The order of models is as follows: (**c**) FCN, (**d**) U-Net, (**e**) ResU-Net, (**f**) DeepLabV3, (**g**) Mixer U-Net (ours), and (**h**) MixerU-Net with PN (ours). The green, red, and blue lines represent true positives (TP), false negatives (FN), and false positives (FP) for road pixels.
# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 3 Methodology
### Model Training
#### XAI Integration Module  
The XAI Integration step employs the LIME technique to offer lucid explanations for the outputs produced by our proposed model. LIME is a technique used to understand the relationship between input parameters and the output of a pre-trained model, regardless of its complexity [52]. Using a linear model or another smaller, more interpretable model trained on a portion of the original data centered on the instance of interest, this strategy approximates the behavior of a complicated model near a given data point. This subset is created by altering the attributes of the instance while maintaining the label unchanged. By analyzing the response of the simplified model to these altered examples, we can deduce the behavior of the original model. Mathematically, LIME explanations for a specific observation \(x\) are formulated as follows:  
\[\psi(x)=\arg\min_{\lambda\in\Lambda}\mathcal{J}(f,\lambda,\sigma_{x})+\Gamma(\lambda) \tag{6}\]  
In this equation, the following is true:  
* \(\Lambda\) is the set of interpretable models and \(\lambda\in\Lambda\) denotes the explanation model.
* The function \(f\) maps from \(\mathbb{R}^{d}\) to \(\mathbb{R}\).
* \(\sigma_{x}(z)\) measures the proximity between \(z\) and \(x\).
* \(\Gamma(\lambda)\) quantifies the complexity of \(\lambda\).
* The loss function \(\mathcal{J}\) represents the difference between the approximation of \(f\) by \(\lambda\) within the region defined by \(\sigma_{x}\), measuring how well the approximation fits.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Activation & Relu \\ Alpha & 0.005 \\ Hidden layer size & (59, 94) \\ Learning rate & 0.01 \\ Iteration & 100 \\ Momentum & 0.3 \\ Random state & 42 \\ [PERSON] & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters and their values used to develop the MLP model.  
Figure 5: A multilayer perceptron model with two hidden layers.  
LIME operates by minimizing the loss function \(\mathcal{J}\) without making assumptions about \(f\), which underscores the model-agnostic nature.  
The XAI Integration phase comprises six primary steps, as illustrated in the Algorithm 2. To begin, we import the pre-trained CKD model and choose a specific sample for both prediction and explanation. Subsequently, we create variations of this sample and proceed to train a regression model using the modified dataset. Subsequently, we compute the magnitude of each characteristic in the localized model and produce an elucidation by emphasizing the most impactful aspects.  
```
1:Pre-trained CKD prediction model
2:
3:Explanation of the model prediction for a sample
4:procedureLIME_Explanation
5:Load the CKD prediction model
6:Select a sample
7:Generate perturbations of the selected sample
8:Fit a regression model on these perturbations
9:Compute feature importance in the local model
10: Provide an explanation by emphasizing the most influential features
11:endprocedure
```  
**Algorithm 2** LIME for CKD prediction model
## 2 Materials and Methods
### The aBCI System
#### 2.2.2 The Stimulation Trials Using Audio Story  
Based on the Shadowing Tasks, the study needed sound stimuli in the form of a story. We designed twelve audio stories to be the sound stimuli to help participants test the aBCI module using Shadowing Tasks. The twelve audio stories, six recorded by male pronunciation and six by female, were all recorded in mono sound channel. These stories consisted of approximately 117-138 Chinese characters, with playtimes of 45-60 s. Figure 3 shows two of the twelve audio stories (original text written in Chinese).  
Each audio story includes one keyword (words rendered in boldface in Figure 3). The keyword appears seven times in the story, following the principles of the oddball paradigm [35; 43], and the inter-stimulus interval (ISI) between the two keywords was 5-8 s. Thus, the user can easily focus on the target stimuli (the keyword appears seven times) in the story he wants to hear by mentally repeating the keyword, not all the words. The unequal ISI avoids anticipatory psychology from the user. The onset times of the keywords in the different stories are all different, to prevent mutual interference.
# A Multi-Dimensional Matrix Product--A Natural Tool for Parameterized Graph Algorithms
## 5 \(k\)-Dominating Set  
The problem of the \(k\)-dominating set is to determine, for a graph \(G\) and a fixed natural number \(k\), if there is a set \(S\) of \(k\) vertices in \(G\) such that each vertex in \(G\) either is adjacent to at least one vertex in \(S\) or belongs to \(S\). It is a basic \(W[2]\) complete problem equivalent to the \(k\)-set cover [27].  
[PERSON] and [PERSON] improved on the straightforward enumeration algorithm by using fast rectangular matrix multiplication [20] (cf. [26]). Their Algorithm 3 can be simply rephrased in terms of a \(k\)-dimensional matrix product as follows.  
_Input:_ A graph \(G\) on \(n\) vertices and a natural number \(k\geq 2\).  
_Output:_ A solution to the \(k\)-dominating set problem in \(G\).  
1. \(\tilde{A}\leftarrow\) the complement of the adjacency matrix of \(G\)
2. \(D\leftarrow\) the \(k\) dimensional matrix product of \(k\) copies of \(\tilde{A}\)
3. **if** if there is a zero entry \(D[i_{1},\dots,i_{k}]\)**then** output \"Yes\" **else** output \"No\"  
**Algorithm 3** Improved straightforward enumeration algorithm by using fast rectangular matrix multiplication.  
It is easy to see that for a set \(S=\{v_{i_{1}},\dots,v_{i_{k}}\}\) of \(k\) vertices in \(G\), \(D[i_{1},\dots,i_{k}]\) is just the number of vertices that are not adjacent to (dominated by) any vertex in \(S\). Thus, \(S\) is a \(k\)-dominating set if \(D[i_{1},\dots,i_{k}]=0\). Hence, assuming that \(n\) is the number of vertices in \(G\), we obtain the following upper bound given in [20] by a simple application of Lemma 1.  
**Theorem 5**.: _Algorithm 3 solves the \(k\)-dominating set problem in \(O(n^{\omega([k/2],1,k/2])+o(1)})\) time._  
The reduction of the \(k\)-dominating set problem to the \(k\)-dimensional matrix product given in Algorithm 3 implies the \(W[2]\) hardness of the problem of computing the \(k\)-dimensional matrix product. By Theorem 5.4 in [39], we obtain the following corollary.  
**Corollary 3**.: _The problem of computing the \(k\)-dimensional Boolean matrix product is not solvable in \(f(k)n^{O(k)}\) time, for any function \(f\), unless \(FPT=W[1]\)._
Sensors_**2009**, \(9\), 445-462; doi:10.3390/s90100445  
_Series of Papers_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Energy-Aware Routing Protocol in Wireless Sensor Networks**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 and [PERSON] 1**  
1 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, P.R. China; E-Mail: [EMAIL_ADDRESS]  
2 Department of Computing, Hong Kong Polytechnic University, Hong Kong, P.R. China; E-Mail: [EMAIL_ADDRESS]  
3 State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, 210093, P.R. China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]  
_Received: 6 November 2008; in revised form: 7 January 2009 / Accepted: 9 January 2009 / Published: 13 January 2009_  
**Abstract:** The most important issue that must be solved in designing a data gathering algorithm for wireless sensor networks (WSNS) is how to save sensor node energy while meeting the needs of applications/users. In this paper, we propose a novel energy-aware routing protocol (EAP) for a long-lived sensor network. EAP achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes. EAP introduces a new clustering parameter for cluster head election, which can better handle the heterogeneous energy capacities. Furthermore, it also introduces a simple but efficient approach, namely, intra-cluster coverage to cope with the area coverage problem. We use a simple temperature sensing application to evaluate the performance of EAP and results show that our protocol significantly outperforms LEACH and HEED in terms of network lifetime and the amount of data gathered.  
**Keywords: Data gathering, Energy-aware, Clustering algorithm, Intra-cluster coverage**
## 7 Conclusions  
In this study, we propose PSA for UWSNs to solve the problem in which existing free-to-move node self-deployment algorithms usually target event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. In this algorithm, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes then divide the network into layers and cluster in each layer. Meanwhile, each cluster head node constructs a connected path to the sink node to guarantee network connectivity. The cluster head node finally regards the ratio of the movement distance of the node to the change in CRR as the target function. It employs pigeon swarm optimization to solve the node-deployment problem. The simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases the network coverage.  
As a future work, we plan to extend the ideas in this paper considering the node deployment about node probability perception model, which is more in line with the actual situation. The cluster-in position adjustment strategy may need to be modified to some degree. In addition, we plan to consider the additional scenario where the sensor nodes are not anchored. In the scenario, the node may be drifted by the current, and the algorithm should run several times to maximize the network service quality. Thus, the additional trigger mechanism that makes the algorithm run again will be designed.  
This paper was supported by The National Key Research and Development Program of China (2016 YFC0201400); The Provincial Key Research and Development Program of Science and Technology Department of Zhejiang Province (2016C03G2040707); The National Natural Science Foundation of China (NSFC61273072); and the National Natural Science Foundation of China and Zhejiang Joint Fund for Integrating of Informatization and Industrialization (U1509217).  
Figure 9: Network reliability comparison.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the research; [PERSON], [PERSON] and [PERSON] performed the research; and [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Estuarine Habitat Mapping  
Our final maps had area weighted accuracies ranging from 79% field/83% UAS to 93% field/93% UAS (Table 3). The standard 8-Band WorldView-3 image realized the best overall accuracy at 93%field and 93% UAS. Notably, the addition of NDVI and image texture did not increase the accuracy of the WorldView-3 classification but did marginally increase the RapidEye classification accuracy in both UAS and field assessments (Figure 5). Across all final classifications there was a consistent underestimation of Emergent Wetland. The addition of NDVI and texture to WorldView-3 did appear to increase the discrimination of the segmentation between classes, but increased confusion of the final classification between the Scrub-Shrub and Emergent Wetland Classes as well as Intertidal Sand and Subtidal Haline. On the RapidEye platform, the addition of NDVI and texture improved differentiation between Scrub-Shrub and Forest but otherwise overall accuracy was not markedly different with or without NDVI and texture (Table S1). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Reference Class**} \\ \hline
**Habit Class** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **Total Area** & **User’s Acc** \\ \hline \multirow{9}{*}{Map Class} & Subtidal Haline-1 & **0.4969** & \multirow{9}{*}{0.0393} & \multirow{9}{*}{0.0169} & \multirow{9}{*}{0.0215} & \multirow{9}{*}{0.0169} & \multirow{9}{*}{0.0229} & \multirow{9}{*}{0.0141} & \multirow{9}{*}{0.0229} & \multirow{9}{*}{0.0141} \\  & Supratidal Sand-2 & & **0.0393** & & & & & & 0.0393 & 100\% \\  & Emergent Weltand-3 & & 0.0068 & **0.1698** & & & & & 0.1765 & 96\% \\  & Scrub-Shrub Weltand-4 & & & 0.0024 & **0.0234** & & & & 0.0258 & 91\% \\  & Intertidal Sand-5 & & & 0.0059 & **0.1352** & & & & 0.1410 & 96\% \\  & Heterogeneous Upland-6 & & & & & **0.0225** & & & 0.0012 & 0.0237 & 95\% \\  & Upland Sand-7 & & & & & 0.0026 & **0.0169** & & & 0.0195 & 87\% \\  & Scrub-Shrub Upland-8 & & & & & & & **0.0202** & 0.0014 & 0.0216 & 93\% \\  & Forested Upland-9 & & & & & & & 0.0016 & **0.0126** & 0.0142 & 89\% \\ \hline \multicolumn{11}{c}{Total Area} & 0.4969 & 0.0461 & 0.1780 & 0.0234 & 0.1766 & 0.0251 & 0.0169 & 0.0229 & 0.0141 & **0.9367** & \\ \hline \multicolumn{11}{c}{Producer’s Accuracy} & 100\% & 85\% & 95\% & 100\% & 77\% & 90\% & 100\% & 88\% & 90\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for WorldView-3 8-band field validation. Each cell holds the percentage of total area in that class, the column describes the field reference and the row describes the predicted map class. The major diagonal represents the classes that were correctly classified.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion
### Network Lifetime  
For varying node density topologies, Figure 7 proves the performance of the ESR protocol with respect to the network lifetime as compared to existing work. It is observed in Figure 6 that ESR had a  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network field & \(100\times 100\) m\({}^{2}\) \\ \(E_{elect}\) & 100 nJ/bit \\ \(E_{amp}\) & 10 nJ/bit/m\({}^{2}\) \\ \(E_{fs}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ Packet size, k & 50 bits \\ Payload size & 512 bytes \\ Initial energy & 5 J \\ Simulation time & 2000 s \\ Node’s transmission range & 20 m \\ Beacons interval & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters.  
prolonged network lifetime, especially in high network load, as an average of 37% enhancement was accomplished in the comparison of other solutions. This is due to optimizing the clusters formation process and initiating the re-election mechanism on the network analysis. In addition, ESR protocol used local clustering functionality that decreased overheads and unnecessary energy consumption in the re-clustering phase. Further, the ESR gave a light-weight secure routing mechanism to route the encrypted sensory information towards BS.
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 3 Secure Data Communication Framework
### Secure Data-Communication Framework  
Instead of sending harmful data packets to the next hop, these packets can be dropped completely or partially received. The initial communication between the source and the target nodes is made to discover malicious nodes (refer [34, 35]). The trust value for every node is derived using fluctuating criteria, ranging from 0 to 1. Each node has a common threshold value, and each network node may be identified by utilizing the threshold value either as a compromised node or a regular node. Identified malicious nodes are removed from the network by altering the state power. The source node can select a reliable path to its target node if the malicious nodes between the source and destination nodes are already removed.  
The proposed model utilizes asymmetric key cryptography, and two specialized nodes, CK and DK, are utilized. These two nodes are used for key generation and verification. Thus, all other nodes in the network need not make any other computations. This will help us to save the energy of nodes. Here, the CK and DK nodes are selected based on their energy and trust factors. The secret keys are distributed to other nodes using the DK. In most of the existing models, every node needs to generate its own keys. Thus, every node spends its energy on key-generation and key-distribution processes. Moreover, if any node is compromised, it will result in security breaches. In the proposed approach, if any node other than the CK and DK is compromised, it will not affect the security of the entire network.