esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Energy IoT Application Using Beacon for Indoor Localization
## 4 The Proposed Approach
Mini (smart drivers) and by the computer, which reads the data via serial and stores them in a database. Then, through a program (Matlab or Excel), the expert user can graphically view the results obtained. Therefore, the UI block--acronym for user interpretation--represents the graphic display of the stored data. In Figure 3, instead, the framework of the system is represented. Therefore, to identify a target object, it is necessary to have at least three master objects that identify the space in which the target is located. To do this, the trilateration technique was used in our system. In a two-dimensional plane, at least three sensors must be used and positioned in the plane to ensure that the measurement is unique. Under these conditions, the technique can be defined as hyperbolic trilateration. This technique guarantees that we can find a target position using the di RSSI signal between it and three other devices' (master) known positions and the time response with the target message. Figure 4 shows the same technique but using both the intersection between three circles and the a priori knowledge of the coordinates of the reference beacons. \[\left\{ \begin{array}{l}d_{1}^{2}=\left(x_{1}-x\right)^{2}+\left(y_{1}-y \right)^{2}\\ d_{2}^{2}=\left(x_{2}-x\right)^{2}+\left(y_{2}-y\right)^{2}\\ d_{3}^{2}=\left(x_{3}-x\right)^{2}+\left(y_{3}-y\right)^{2}\end{array} \right. \tag{1}\]  
Using this system, it is possible to obtain the (\(x\), \(y\)) coordinates of the target node:  
\[x=\frac{\left(x_{1}^{2}+y_{1}^{2}-d_{1}^{2}\right)\left(y_{3}-y_{2}\right)+ \left(x_{2}^{2}+y_{2}^{2}-d_{2}^{2}\right)\left(y_{1}-y_{3}\right)+\left(x_{ 3}^{2}+y_{3}^{2}-d_{3}^{2}\right)\left(y_{2}-y_{1}\right)}{2\left[x_{1}\left( y_{3}-y_{2}\right)+x_{2}\left(y_{1}-y_{3}\right)+x_{3}\left(y_{2}-y_{1}\right) \right]} \tag{2}\]  
\[y=\frac{\left(x_{1}^{2}+y_{1}^{2}-d_{1}^{2}\right)\left(x_{3}-x_{2}\right)+ \left(x_{2}^{2}+y_{2}^{2}-d_{2}^{2}\right)\left(x_{1}-x_{3}\right)+\left(x_{ 3}^{2}+y_{3}^{2}-d_{3}^{2}\right)\left(x_{2}-x_{1}\right)}{2\left[y_{1}\left( x_{3}-x_{2}\right)+y_{2}\left(x_{1}-x_{3}\right)+y_{3}\left(x_{2}-x_{1}\right) \right]} \tag{3}\]  
Figure 4: Localization based on the trilateration method. Figure 3: System framework. If the three beacons have the following coordinates \(R_{1}=(0,0)\), \(R_{2}=(0,v)\), \(R_{3}=(u,0)\), as shown in Figure 5, the system is simplified in the following way:  
\[\left\{ \begin{array}{l}x=\frac{u^{2}+\left(d_{1}^{2}-d_{2}^{2}\right)}{2i}\\ y=\frac{v^{2}+\left(d_{1}^{2}-d_{2}^{2}\right)}{2v}\end{array} \right. \tag{4}\]  
This method is very simple, useful and convenient, as the \(x\) and \(y\) coordinates can be easily obtained starting from the values of the distances, obtained through the RSS parameter of the reference beacons with respect to the target node.
# Spatial Perception Correntropy Matrix for Hyperspectral Image Classification
## 4 Experimental Results
### Performance Evaluation  
In this section, we show a comparison between SPCM and other advanced algorithms on three real datasets using small sample training sets.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 3 Proposed Protocol Design
### Frame Structure  
The uplink message format is re-engineered in the proposed system to contain the need for forwarding a message being transmitted by an end device. A MAC payload for a LoRaWAN frame consists of three parts: Frame Header (FHDR), Frame Port (FPORT), and Frame Payload (FRMPAYLOAD). The frame Header itself is divided into four sections with a size not exceeding 22 bytes, as shown in Figure 3. The frame option (FOpts) part is up to 15 bytes field and is used for MAC commands. A portion of the FOpts is used in the proposed system to hold the status of a transmitted message regarding the need for forwarding by other end devices in the network. Figure 3 shows the new format as well as the LoRaWAN frame header.  
Moreover, an end device in the proposed system is required to attach a Time-To-Live (TTL) value to its messages. The TTL is positioned next to the Forwarding status field in the new structure of the frame header. The TTL is assigned 1 in messages generated by an out-of-range device and 0 in messages issued by end devices that reside within the coverage of the gateway. This is used to control the broadcasting of out-of-range devices' traffic and provide loop avoidance. The TTL is decreased to 0 once the message is forwarded by one or more of the neighbors of the out-of-the-range device. Therefore, the message no longer gets forwarded in case it has been received by other end devices, hence decreasing the chance of storming the network with the introduced forwarding mechanism and providing loop avoidance.  
Figure 3: Proposed Frame Header Structure.
## 5 Benchmark Testing and Performance Analysis of MRSO
### Comparative Study of MRSO and RSO Using Standard Benchmarks
#### 5.2.1 Unimodal Functions (F1-F7)  
Unimodal functions, which possess only one global optimum and no local optimum, provide an excellent means to assess the exploitation capability of optimization algorithms. By analyzing the average and standard deviation (std.) metrics, we can evaluate the precision and stability of both the MRSO and the RSO in finding optimal solutions within smooth search landscapes. As shown in Table 1, the MRSO demonstrates improvements in two out of seven unimodal functions (F6 and F7) over the standard RSO, indicating better exploitation capabilities.  
In terms of average performance, the MRSO outperforms the RSO in two out of the seven unimodal functions. For example, in function F6, the MRSO achieves an average of 8.160 \(\times\) 10\({}^{-1}\), significantly better than the RSO's 3.415 indicating the MRSO's improved exploitation capability. In F7, the MRSO's average is 1.981 \(\times\) 10\({}^{-4}\), which is also lower and thus better than the RSO's 4.861 \(\times\) 10\({}^{-4}\), highlighting the MRSO's precision in finding optimal solutions. For other functions like F1, although the MRSO has an average of 1.12 \(\times\) 10\({}^{-6}\), the RSO's average of 1.537 \(\times\) 10\({}^{-256}\) is much closer to zero, indicating betterperformance in this case. Similarly, for functions F2 and F4, the RSO outperforms the MRSO with much smaller averages. Overall, the MRSO shows superior average performance in two of the seven unimodal functions.  
When considering the standard deviation, which indicates the consistency of the algorithm, the MRSO outperforms the RSO in three out of the seven functions. In F6, the MRSO demonstrates greater stability with a standard deviation of \(4.033\times 10^{-1}\) compared to the RSO's \(4.748\times 10^{-1}\). Similarly, in F7, the MRSO has a lower standard deviation (\(1.438\times 10^{-4}\)) than the RSO (\(5.636\times 10^{-4}\)), showing that the MRSO produces more reliable results. In F5, the MRSO also has better stability with a standard deviation of \(1.323\times 10^{-1}\), while the RSO's is higher at \(2.635\times 10^{-1}\). In other functions, such as F1, the RSO has a perfect consistency with a standard deviation of 0, while the MRSO shows some variance. Thus, the MRSO provides more consistent results in three of the seven unimodal functions.
## 3 Numerical Study
### Load Displacement Response  
The comparison of the lateral load-displacement curve between the numerical and the experimental results is shown in Figure 9. In the initial loading stage, the numerical simulation and experimental load-displacement curves show good consistency, indicating the stiffness phase. Once the lateral displacement reaches 5 mm, the model enters the plastic strain phase. However, the lateral load generated by the numerical model in this phase is smaller than the experimental value. The relationship between lateral load and displacement shows a linear trend in the numerical model, whereas it exhibits a nonlinear behavior in the actual experiment. Under the same displacement conditions, the numerical model predicts lower lateral load values compared to the experimental results. The overall curve still shows a marked decline, especially during the stiffness phase, demonstrating a satisfactory level of conformity. The discrepancy observed during the plastic strain phase is due to the detachment between the pile cap and the GPF connection in the quasi-static loading of the experiment, resulting in rotation between the pile cap and the GPF and leading to a higher experimental lateral load than the simulated value. The high consistency between the load-displacement curves obtained from the numerical model and the experimental results demonstrates the accuracy of the model calculations.  
Figure 10 illustrates the overall schematic diagram of the model after the lateral displacement loading is completed. It can be observed that the top part of the pile foundation enters the underlying soil, causing the soil to bulge. Behind the loading direction, the GPF and the underlying soil experience separation, resulting in a crack. This phenomenon is consistent with the experimental observation. The load-displacement curve, along with the schematic diagram of pile foundation deformation shown in Figure 11, verifies that the GPF did not undergo plastic yielding under the lateral load, further indicating its excellent lateral bearing capacity characteristics. It also confirms that the calculation results of the numerical model are accurate.  
Figure 8: Numerical model of GPF: (**a**) the numerical model, (**b**) soil, (**c**) the GPF.
## 3 Results and Discussion  
### Extract Yields  
The yield of the P1E, P2E, and P3E are 14.37 \(\pm\) 0.80, 8.90 \(\pm\) 1.02, and 11.81% \(\pm\) 0.92, respectively. The difference in extraction yield between the three samples studied is statistically significant (\(p<0.05\)). A recent study shows that the extraction yield of four plant parts studied (root, stem, leaf, and flower) is 12.30%, 11.77%, 18.77%, and 18.60%, respectively [33]. In addition, according to [PERSON] et al. [39], the yield of the methanolic flower extract is 21%.  
The difference in yield can be explained by the difference in the solvent used [40], the organ of the plant used [33], and the extraction technique [39].
## 3 Methodology
### Evaluation  
This paper uses five evaluation metrics to comprehensively evaluate the model, namely root mean squared error (RMSE), mean absolute error (MAE), mean bias error (MBE), mean absolute percentage error (MAPE), and r-squared (R\({}^{2}\)). Among them, RMSE and MAE can measure the size of the error between predicted values and true values. MAPE measures the relative size of prediction error in percentage form. MBE can measure the systematic bias of predicted values. R\({}^{2}\) is used to measure the explanatory power of a model for data changes, reflecting the goodness of fit of the model. The five evaluation indicators are as follows.  
\[f_{\text{R}^{2}}=\frac{\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}}{\sum_{i=1}^{n}{ (y_{i}-\overline{y})^{2}}} \tag{22}\]  
\[f_{\text{RMSE}}=\sqrt{\frac{\sum_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}}{n}} \tag{23}\]  
\[f_{\text{MAE}}=\frac{\sum_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}}{n} \tag{24}\]  
\[f_{\text{MBE}}=\frac{\sum_{i=1}^{n}{(\hat{y}_{i}-y_{i})}}{n} \tag{25}\]  
\[f_{\text{MAPE}}=\frac{100\%}{n}\sum_{i=1}^{n}{\left|\frac{y_{i}-\hat{y}_{i}} {\hat{y}_{i}}\right|} \tag{26}\]  
where \(y_{i}\) is the measured value, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}\) is the mean measured value, and \(n\) is the number of samples.
## 4 Simulation Results and Analysis
### Results Analysis of Power Performance  
The vehicle velocity performance shows the dynamic response of the actual vehicle velocity following the driver's given velocity, which is also the most basic condition for judging whether EMS is feasible. From the vehicle velocity conditions shown in Figures 15 and 16, it can be seen that, during the entire cycle of NEDC and FTP75, the vehicle had good dynamic performance and could respond quickly to a given vehicle velocity, indicating that the vehicle model is rationally modeled and the control strategy is feasible. It can be seen from the following situation after enlargement in Figures 17 and 18 that the NNF-EMS based on DCR has the best performance, whose dynamic response time is the fastest with no overshoot. In addition, it can be seen from the following situation that it is obviously better in the steady-state driving cycle NEDC than in the transient driving cycle FTP75, which is close to the actual operation.  
Figure 14: The designed parallel hybrid electric vehicle (PHEV) model in AVL Cruise.  
Figure 15: Vehicle velocity following under NEDC cycle.
## 3 Results and Discussion
### pH Adjustment after AuNPs Preparation  
According to the color change, visible spectra curves, and different microscopic approaches, the best results were obtained for the sample at pH 10 of all the tested samples. Our experiments showed that when the mycelium-free supernatant was directly used for nanoparticle preparation, the pH was 3.86 and the obtained color was dark green. That AuNP sample was used along with the pH 10 sample for the subsequent pH adjustment experiment. As for the sample with an initial pH of 10, the color of the AuNPs changed rapidly after the pH was readjusted, indicating that the changes in pH resulted in a different size of the nanoparticles. Adjusting that sample to pH 2 resulted in a gray color; pH 4 resulted in a brown color; and pH 6, 8, and 10 turned into a range from purple to red (Figure 8A,B). Since this type of AuNP responded quickly and easily to the pH changes in the environment, it could be used as a biologically based nanosensor. Based on this property, we will try to develop a colorimetric sensor for the visual determination of pH in biological samples in the future.  
Figure 7: FTIR analysis of AuNPs at pH 10 confirmed the presence of proteins as capping agents.  
The pH readjustment of the AuNPs with an initial pH of 4 resulted in the same color (green) for all of the samples (Figure 8C,D). Based on the size analysis described below, the size of the AuNPs produced at pH 10 was below 100 nm, and the size of AuNPs produced at pH 4 was above 100 nm. Thus, after the production of AuNPs, pH adjustment could only be performed for the samples with sizes below 100 nm. In the green AuNP samples, since the initial size of the nanoparticles was above 100 nm, they could agglomerate. Thus, pH adjustment had no further effect on the color of the dispersion and the AuNPs could not be dissociated by pH adjustment. The spectrophotometry results confirmed that, unlike the AuNPs with an initial pH of 10, the ODs of the AuNPs with an initial pH of 4 in the sample did not change. Figure 9 shows the spectra of the AuNPs with an initial pH of 10. Prior to spectrophotometry, all the samples were diluted 1:2 with ddH2O, as their ODs were above 1. The maximum absorbance peak for pH 10 (after readjustment of pH) was 525 nm.
# Novel Polymer Sorbents with Imprinted Task-Specific Ionic Liquids for Metal Removal
## 2 Materials and Methods
### Sorption Studies  
Sorption experiments were carried out by employing the batch method by mixing 0.1 g of the sorbent with 100 mL of an aqueous solution of Cu(II), Zn(II), and Cd(II) with variable concentrations of each metal ion at a constant temperature of 23 \({}^{\circ}\)C. The adsorption was studied at a different pH (1-6), times (1-350 min), and metal ion concentrations (50-200 mg/L) and at constant temperature (23 \({}^{\circ}\)C). The working metal concentrations were prepared by appropriate dilution of stock solutions. The pH of each of the solutions was adjusted to values of 1.0-6.0 by the dropwise addition of 0.1 M NaOH or 0.1 M HCl using the T5 Excellence titrator fitted with pH electrode. The desorption experiment was conducted for the selected sorbents using different aqueous solutions of HCl, NaCl, and Na\({}_{2}\)SO\({}_{4}\). The mixtures were shaken using a temperature controlled shaker (KS 4000 ic control, IKA) at 240 rpm for a period of desired time. After finishing sorption or desorption, the solid phase was separated and the metal ion concentration in the supernatant was determined by atomic absorption measurements using ContrAA 300 (Analytik Jena AG). Each experiment was replicated three times, and the results were averaged. The metal ion removal efficiency (\(R\)) and amount of adsorbed metal ions (\(Q\)) were calculated using the following equations:  
\[R =\frac{C_{0}-C_{t}}{C_{0}}\cdot 100 \tag{1}\] \[Q =\frac{(C_{0}-C_{t})\cdot V}{m} \tag{2}\]  
where \(C_{0}\) and \(C_{t}\) (mg/L) are the concentrations of the metal ions before and after contact between the phases at time \(t\). \(V\) is the volume of aqueous phase used (L), and \(m\) is the mass of the sorbent used (g).  
The experimental data were also used to evaluate the kinetics and equilibrium of the sorption (Table 1). The pseudo-first order, pseudo-second order, Elovich, and intra-particle diffusion models were applied to describe the kinetics of the sorption [33]. The equilibrium was modelled using isotherms such as Freundlich, Langmuir, Dubinin-Radushkevich, and Temkin [34]. The nonlinear forms of the models are presented in [35]. Kinetic studies were carried out using 50 mg/l at pH 5 at 298.15 K. The isotherm experiments were carried out by using different initial concentrations of metal ions (50-200 mg/L) at 298.15 K and at a pH of 5.  
Figure 1: Scheme of the synthesis of the sorbent series VBC and VBBr.
## 2 Routing Goals
### Fault-Tolerance  
In WSNs, fault tolerance mechanisms should be considered in both nodes and links. As sensor nodes are battery enabled devices, they may lose their power and die quickly. However, the failure of some sensor nodes should not affect the overall task of the sensor network. If many nodes fail, MAC and routing protocols must accommodate formation of new links and routes to the data collection base stations. This may require actively adjusting transmitting powers and signaling rates on the existing links to reduce energy consumption, or rerouting packets through regions of the network where more energy is available. Additionally, such node failures may disrupt the connectivity of the network, therefore multiple levels of redundancy may be needed in a fault-tolerant sensor network. On the other hand, in a multi-hop sensor network, communicating nodes are linked by a wireless medium. Generally, the needed bandwidth of nodes' information will not be high, on the order of 1 to 100 kb/s. In such an area, the conventional problems associated with a wireless channel such as fading and high error rate have negative effects on data transmissions between sensor nodes and sink and may lead to packet loss. A packet loss has a direct effect on the accuracy of the results, therefore, a data routing approach in WSNs should be able to handle such faults, too [36,49,50].
# A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation
## 3 Proposed Method
### Decision Support in Immersive Environment: VROOM  
We apply game theory as the decision support system in our immersive visualisation VROOM [15]. The visualisation allows the analyst to interrogate patient cohorts in an immersive environment based on their genomic similarity and treatment data to hypothesise treatment options. Non-linear and linear dimensionality reduction methods, such as t-distributed Stochastic Neighbour Embedding (t-SNE) [35; 36], Uniform Manifold Approximation and Projection (UMAP) [37; 38; 39], Non-negative Matrix Factorisation (NMF) [40], Principal Component Analysis (PCA) [41], and computation analytics algorithms such as autoencoder [42] were also used to transfer the complex genomics data to a 3D immersive space, where patients with similar genomic properties are located closely together [15]. Dimensionality reduction process transfers the data from a high-dimensional space into a low-dimensional space while they retain some meaningful properties of the original data. This process is crucial for genomics data analytics where the data can have tens of thousands of dimensions in a patient's genetic information [43].  
For medical domain users, the complex data analysis and exploration process could be overwhelming; hence, there is a need for a decision support system to assist the analyst. In our VROOM system, the analysts can look at four different data sets with two types of risk assessment. On top of that, six types of dimensionality reduction algorithms are used. These combinations can easily overwhelm the user when deciding where to start. Our objective is to apply game theory to provide a good starting point. In this section, we discuss the mathematical part of game theory application.  
This paper investigates how two clinicians with different opinions can be modelled into the visualisation system as a decision support system. The model helps to drive which patients, when compared, should be selected in the similarity space to look for the gene combination that yields the list of neighbours with the highest survival rate. For non-technical users, e.g., clinicians, the visual analysis process could be overwhelming; hence, a decision support system to assist them is useful. For example, the clinicians can decide on a treatment method by using the game theory model to identify the best treatment success rate with the highest survival rate of the selected patient and the patient's neighbours. Our model assumes that the decision-making process for our \"two doctors\" scenario is a strategic game. The two main game theory concepts used here are Nash Equilibrium and Social Optimality. The concept can be generalised to more than two clinicians. For discussion, we will use two clinicians in our case study.
## 4 Experimental Results and Comparisons
### Experiments on the Benchmark Dataset NEU
#### 4.1.4 Comparisons with Recent State-of-the-Art Methods  
The proposed method was evaluated by comparing it with recent state-of-the art methods specifically developed for learning from label noise, i.e., CleanNet [27] and MentorNet [29]. The experimental objects of both the CleanNet and MentorNet comparison methods are different from the experimental objects of this paper: for better comparison, the NEU dataset after augmentation was used as the basic experimental dataset for all three methods in this experiment. In CleanNet and MentorNet, we reset the hyperparameters according to the characteristics and quantities of the input data. At the same time, in order to validate the effect of noisy labels in the training set on these three methods, we set the percentage of noisy labels in training set as 50%. The reason for this is that, in actual production, a defect classification of more than 85% is required, and we proved in Figure 7 that the proposed method can maintain a classification accuracy above 85% when the percentage of noisy labels does not exceed 50%. The full results are presented in Table 6.  
Figure 7: The trends in classification accuracy of standard Inception_v2 and the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Percentage of Noisy** & **True Labels in One** & **Noisy Labels Distributed in Other** \\
**Labels** & **Category** & **Categories (Per Category)** \\ \hline
0\% & 1500 & 0 \\
20\% & 1200 & 60 \\
50\% & 750 & 150 \\
60\% & 600 & 180 \\
70\% & 450 & 210 \\
80\% & 300 & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The truth labels in one category and the noisy labels are distributed to other categories.  
It can be seen from the table that the classification accuracies with 50% noisy labels of CleanNet and MentorNet are 81.33% and 82.44%. The accuracy of the proposed method was improved by 4.9% from the best result in the comparison methods.