esa-sceva/satcom-chunk-collection huggingface dataset:
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Data Reduction
#### 2.2.1 Data Aggregation  
The main idea under data aggregation techniques is to remove redundancies in the received data from the neighboring nodes, extracting the useful information by means of aggregation functions (maximum, minimum, average, etc.) before forwarding the final data to the BS, CH or any central node [57].  
Several data aggregation schemes have been proposed that, according to the aggregation methodology, can be classified [58] in: (a) centralized (all nodes send data to an energy-rich central node, responsible for aggregation); (b) in-network (aggregation is performed in all the intermediate nodes); (c) tree-based (a minimum spanning tree is built, where root node acts as base station, source nodes are the leaves that send the data to their parent intermediate node) and (d) cluster-based (CH is responsible for aggregating the data received from the cluster nodes and then sending the data to the base station).  
Data aggregation techniques is one of the most researched areas for energy efficiency in WSNs. It is noticeable that only in the last two years several surveys can be found in the literature in its field [59; 60; 61; 62].  
When sensitive data must be aggregated and transmitted, assuring privacy is also an important issue. With this focus on mind, the work in [63] surveys various existing solutions for secure data aggregation, classifying them based on the node topology and mechanisms employed for ensuring privacy.  
Another interesting approach for aggregating data is to exploit machine learning to select efficient cluster heads or for dimensionality reduction of the data at sensor nodes or cluster heads. The recent survey in [64] focuses on the application of machine learning for energy efficiency in WSNs, and devotes a section to data aggregation.
## 1 Introduction
We then consider research gaps and challenges of existing studies that may inform researchers who wish to further progress the techniques and applications. Several review papers have been published in recent years based on video camera imaging. [PERSON] et al. [45] presented a review of the work on remote PPG imaging using digital cameras. [PERSON] et al. [7] introduced PPG measurement techniques from contact to noncontact and from points to images. [PERSON] et al. [46] did a methodological review for contactless vision-guided pulse rate estimation. [PERSON] et al. [47] investigated both iPPG and ballistocardiography (BCG) estimation based on digital cameras. [PERSON] et al. [5] did a technical literature review on remote heart rate measurement using low cost RGB face video. [PERSON] et al. [12] provided a broad literature survey of remote cardiorespiratory monitoring, including the Doppler effect, thermal imaging, and video camera imaging. [PERSON] et al. [48] did a review on the technique's background for cardiovascular assessment using iPPG. A thorough review on iPPG in diverse scenarios is appropriate, including issues such as motion artefacts, illumination variations, alternate sensor modalities, different subjects, different vital signs, multiple ROIs, long distance and multiple persons. Moreover, there is no review paper that focuses on applications of iPPG considering both clinical and non-clinical sectors. To fill these gaps, this paper provides a comprehensive review of the recent advances of iPPG studies focusing on diverse and non-clinical scenarios. We compare different techniques to give a clear summary of the state of the field. We describe potential applications of iPPG in both clinical and non-clinical sectors separately to show the value of the iPPG technique in real world applications. Finally, we present several issues and scenarios for future studies.
## 3 Results
### Energy Fluxes  
The overstory accounts for the majority of H in the ecosystem (Figure 2d). On average, H exhibited almost 50% of the variation between AW and SS, with greater values in SS (see Supplementary Material, Table S1). The understory presented values close to zero. LE demonstrates pronounced seasonality, with higher values in SS than in AW for the ecosystem (76%), overstory (72%), and understory (87%) (Figure 2e, Table S1). Smaller differences were observed in an evergreen tropical forest in Amazonia, where the dry season presented 40% of the wet season's values [48]. Consequently, winter and summer had a more significant influence on the LE seasonality in the Araucaria forest compared to the dry and wet periods in the Amazonian forest. In the overstory, LE was approximately three times the values found in the understory. Thus, the overstory makes a higher contribution to the evapotranspiration processes, representing about 75% of the ecosystem's LE.  
Figure 3 represents the evaporative fraction that illustrates the turbulent energy process used for LE. In the ecosystem (n = 32, the position of the measurements), LE constituted, on average, 70% of the total energy (H + LE in n = 32). A slight increase was observed in SS and a decrease in AW. When calculating the evaporative fraction, considering only the energy intercepted by the overstory (n = 32-11), this ratio increased to 55%, indicating a higher utilization of energy in the canopy, predominantly through the transpiration process. It is noteworthy that transpiration in the overstory can account for more than half of the available energy in the system, playing a crucial role in the region's water and energy balance. Similar findings were observed in different biomes in Brazil: [PERSON] et al. [49], studying the Cerrado biome, noted a high dependence of the evaporative fraction on the LAI, with average values of 60% in Cerrado Dense (LAI = 3.45 m\({}^{2}\) m\({}^{-2}\)) and 49% in Field Cerrado (LAI = 2.01 m\({}^{2}\) m\({}^{-2}\)); [PERSON] et al. [50] reported a value of 86% for a tropical forest in Amazonia, while [PERSON] et al. [51] found an average value of 72% across several tropical forest ecosystems.  
LE, expressed in terms of ET, corresponded to approximately 60% of the total annual precipitation that occurred in this ecosystem (ET = 1010 mm yr\({}^{-1}\); Prec = 1674 mm yr\({}^{-1}\)). In other forest systems in Brazil, [PERSON] et al. [50] and [PERSON] et al. [52] found values around 60% for the Amazon region, and [PERSON] et al. [53] found values ranging from 82 to 96% for the relationship between ET and Prec in a eucalyptus plantation.
## 3 System Characterization Using FEM
### Electric Field Analysis  
The electric field distribution was analyzed for different equipotential planes from the bottom of the culture well, as shown in Figure 4b. The bottom of the well, where the epidermal cells are modeled, is depicted as a plane at 0 mm. The electric field is observed at equipotential planes from 0 mm to 4.00 mm at intervals of 0.25 mm. The field distribution depends on the current injected through the source electrode. By keeping the injecting current and width of electrodes unchanged, the height of the electrodes (\(h_{elec}\)) was increased from 0.5 mm to 2.00 mm.  
It can be seen from Figure 4b that the electric field distribution is highest at the bottom of the well for all electrode sizes, and this gradually falls off at subsequent equipotential planes away from the bottom. This depicts that the electrodes placed close to the bottom of the culture wells give the highest electric field along the surface. Electrodes with smaller surface areas show higher electric fields towards the bottom of the well compared to their large surface area counterparts.  
Furthermore, it can be seen that electrodes with different \(h_{elec}\) construct different field distribution trends for the same amount of injected current. Electrodes with a \(h_{elec}\) = 0.5 mm show a rather constant field distribution in planes up to 0.5 mm from the bottom of the well, and they show a notable drop in planes toward the top of the well. As the value of \(h_{elec}\) increases, the field distribution starts to flatten out at subsequent equipotential planes from the bottom to the top of the well. Table 2 summarizes the difference between the maximum and minimum values of field distribution for different electrode sizes. Electrodes with a small surface area, such as the one with \(h_{elec}\) = 0.50 mm, show a difference of \(3.072\times 10^{-3}\) mV/mm between the equipotential plane at the bottom of the well and a plane at 4.00 mm from the bottom. On the contrary, electrodes with \(h_{elec}\) = 2.00 mm exhibit a much lower difference of \(1.989\times 10^{-3}\) mV/mm between the same equipotential planes. For smaller electrodes, the field is much more concentrated around the vicinity of the bottom of the well, where the target cells are seeded. Thus, it can be considered that smaller electrodes placed near the bottom of the culture wells give better electrical bio-interfacing in terms of electric fields.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Height of Electrodes (\(h_{elec}\))** & **Ratio \(J_{1}:J_{2}\)** \\ \hline
0.50 mm & 0.27174 \\
0.75 mm & 0.27032 \\
1.00 mm & 0.26856 \\
1.25 mm & 0.26645 \\
1.50 mm & 0.26399 \\
1.75 mm & 0.26128 \\
2.00 mm & 0.25856 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The ratio between the current density in cells (\(J_{1}\)) and the current density in growth media (\(J_{2}\)) for different sizes of electrodes.
# Recognition of Acoustic Signals of Commutator Motors
## 2 Proposed Fault Detection Approach Using a Smartphone and Acoustic Signals
### NM Classifier  
To classify feature vectors, the NM classifier was used. It is a well-known classifier used for various applications (similar to the nearest neighbour classifier and k-means clustering) [29; 30; 31; 32; 33; 34; 35; 36]. It used an averaged feature vector. The averaged feature vector was the arithmetic average of the training feature vectors of one class. For example, for 5 classes, there are 5 vectors. Next, the averaged feature vector was compared with the test feature vector using the Manhattan distance (Equation (2)).  
\[MD(\mathbf{a-hd})=\sum_{i=1}^{1}\lvert(a_{i}-hd_{i})\rvert \tag{2}\]  
where \(MD(\mathbf{a-hd})\) is the Manhattan distance for vectors \(\mathbf{a}\) and \(\mathbf{hd}\), where the test vector \(\mathbf{a}=[a_{264},\)\(a_{265},\)\(a_{276},\)\(a_{322},\)\(a_{526},\)\(a_{527},\)\(a_{643},\)\(a_{644},\)\(a_{824},\)\(a_{825},\)\(a_{827},\)\(a_{964}\)] and the averaged vector \(\mathbf{hd}=[hd_{264},\)\(hd_{265},\)\(hd_{276},\)\(hd_{322},\)\(hd_{526},\)\(hd_{526},\)\(hd_{527},\)\(hd_{643},\)\(hd_{644},\)\(hd_{824},\)\(hd_{825},\)\(hd_{827},\)\(hd_{964}\)].  
The NM classifier performed Equation (2) for all feature vectors: \(\mid\mathbf{a-hd}\mid\), \(\mid\mathbf{a-ddgt}\mid\), \(\mid\mathbf{a-dfive}\mid\), \(\mid\mathbf{a-dfive}\mid\), \(\mid\mathbf{a-dfive}\mid\). The nearest distance between vectors was essential for making decisions about the recognised class. For example, if \(\mid\mathbf{a-hd}\mid\) is the nearest distance, then the class \"healthy electric impact drill\" will be recognised.  
There are 2 recordings of the electric impact drill in the presented approach. The first recording was split into training samples. The second recording was split into test samples. The MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS method used the training samples. Next, it computed the common frequency components (in the analysed case, it was 12 common frequency components; please see Section 2). The number of common frequency components depended on the analysed signals. Equation (2) uses the unknown test vector \"a\" and the known training vector \"\(\mathbf{hd}\)\". Each new recorded test sample is compared with the \"training set\" (all averaged feature vector). Assuming a signal for a new drill is acquired; next, it is compared with all states in the training set. Moreover, there is also the possibility of adding new training samples and new type of motors to the training set.  
Figure 25: Values of features of the blender with a faulty fan (5 broken rotor blades).
## 6 Results
### Data Association
#### 6.3.1 Mahalanobis Distance  
The experimental results of the sensitivity analyses for the Mahalanobis Distance computation are shown in Figure 10. Figure 10 shows the results of the particle-based sensitivity analysis (\(N_{l}=1024\)), while Figure 10 the results for the landmark-based analysis (\(N_{p}=1024\)). The fully parallelized implementation (\(MBNT\)) performs best under all test cases since the Mahalanobis Distance computation is highly computationally intensive on the landmarks, making block-based parallelization the most inefficient among the parallelized implementations. The most efficient implementations reduce by a factor of around 1500 for the highest numbers of particles (\(N_{l}=1024\)), a factor of almost 2000 for the highest values of \(N_{l}\), with \(N_{p}=1024\).
# Service Interface Translation. An Interoperability Approach
## 6 Conclusions  
The number of connected devices and systems has increased in an attempt to achieve the Industry 4.0 benefits estimated by analysts. This situation has led to an increase in heterogeneity and a lack of interoperability.  
Traditionally, the interoperability problems related to service interfaces are solved manually. Developers spend large amounts of time and effort implementing interfaces that solve these issues. A generic and automated solution is thus needed. This paper proposed a new approach to address service interoperability issues at the interface level based on autonomous code generation. New interface instances are generated to act as a bridge between heterogeneous systems. The instances translate the interfaces based on the service metadata included in the service contracts.  
This approach allows solving interoperability mismatches at runtime without any human intervention, consequently reducing the engineering costs and time. The performance of the proposed architecture has been validated through a practical implementation. A prototype of the consumer interface system, the system in charge of the generation, has been tested in a use case scenario. The results showed the potential of the approach. The approach may be used in conjunction with other technologies to provide a generic solution to the interface interoperability problems. However, to obtain the maximum benefits of the approach, limitations such as security considerations, semantic accurate definition, and semantic translation need to be overcome.  
This research was funded by EU ECSEL Joint Undertaking under grant agreement No. 826452.  
Not applicable.  
The authors thank the European Commission and the _Arrowhead Tools_ project (EU ECSEL Joint Undertaking under grant agreement number 826452) for funding.  
The authors declare no conflict of interest.
## 1 Introduction
The proposed FVIMDE method is designed to bridge these gaps by combining the strong exploration capabilities of FVIM with the focused exploitation proficiency of DE. The main contributions of introducing the proposed methods are:  
* The HybridFour Vector Intelligent Metaheuristic and Differential Evolution (FVIMDE) algorithm is introduced, merging the advantages of both optimizers to enhance optimization efficiency. * The FVIMDE algorithm integrates FVIM's four-vector strategy with DE's mutation and crossover strategies, utilizing the average mean of the four best solutions for exploration and DE strategies for exploitation. * The FVIMDE algorithm is implemented and evaluated using unimodal, multimodal, hybrid, and composition benchmark functions from the 2022 and 2017 IEEE Congress on Evolutionary Computation and challenging benchmark function that consists of complex, hybrid, and composite functions. * The FVIMDE algorithm is validated on complex real-world engineering design problems such as welded beam design, pressure vessel design, spring design, speed reducer design, cantilever beam design, and three-bar truss design, showing superior performance when compared to 12 well-known and state-of-the-art optimization algorithms, such as GWO, WOA, MFO, and MVO. The rest of this paper is organized as follows: Section 2 provides a detailed review of the recent related work, and Section 3 focuses on the foundational concepts of the Four-Vector Intelligent Metaheuristic (FVIM) and Differential Evolution (DE), highlighting their key characteristics and applications. In Section 4, we introduce the proposed Hybrid FVIMDE Algorithm, detailing its conceptual framework and operational mechanics, and discuss how it integrates the strengths of FVIM and DE. It also presents the mathematical model of FVIMDE, including variable definitions, mutation and crossover processes, and the FVIM position-update mechanism. In addition, it explores the strategic components of exploration and exploitation within the hybrid algorithm, describing the balance between these elements to optimize performance. Section 5 delves into the experimental analysis and results, presents evaluations using benchmark functions, and discusses the algorithm performance relative to other optimization algorithms. Section 7 applies the FVIMDE algorithm to real-world engineering design problems, discussing optimization constraints and parameter settings. Finally, Section 9 concludes the paper by summarizing the findings, reiterating the advantages of the hybrid algorithm, and suggesting future research directions.
## 4 Routing Using SPWO Algorithm  
Routing is an important phenomenon in IoT and it has the facility to handle the dynamic nature of network topology. It is more significant to select the best routing path to transfer the data packets such that the procedure of routing is created by the proposed SPWO approach [41]. The features associated with SPBO are inherited by WOA to derive a hybrid solution to accomplish the task of data routing in an IoT network. The proposed algorithm is highly efficient at routing the data packets as it effectively eliminates local optima and attains a higher convergence by moving the solution toward global optima. Even though a number of routing protocols are available, selecting the routing path by excluding malicious nodes still results in a complex task in a network domain. Hence, the proposed algorithm is designed for data communication from IoT devices to the sink node located in the coverage area. Solution encoding: It signifies resolution in terms of a vector that shows the best path chosen for routing. Among \([1\times b]\), in which \(b\) specifies total nodes, it is important to select the index of nodes that are involved in the routing process. For instance, the below figure shows that nodes with indexes 1, 3, 6, and so on are selected for routing. Figure 2 portrays solution encoding.  
Figure 2: Solution encoding.  
The fitness function is a function exploited to discover the best path to route data bytes from sender to recipient and this is done using the below expression in Equation (13):  
\[F=\frac{1}{5}[H_{r}+A_{r,s}+(1-B_{r,s})+K+(1-Y_{r,s})]. \tag{13}\]  
Here, \(F\) implies fitness measure, \(H_{r}\) shows energy consumption in the \(r\)th node, \(A_{r,s}\) indicates the distance between the \(r\)th and \(s\)th nodes, \(B_{r,s}\) denotes link quality, K represents delay, and \(Y_{r,s}\) denotes the trust of the \(r\)th node to the \(s\)th node. The Path that has the least wellness esteem is chosen as the ideal direction consisting of minimum energy consumption, minimum distance, minimum delay, maximum link quality, and maximum trust. The distance factor is \(A_{r,s}\), represented in Equation (14):  
\[A_{r,s}=\|A_{r}-A_{s}\|, \tag{14}\]  
where \(\|A_{r}-A_{s}\|\) denotes the Euclidean distance between the \(r\)th node and the \(s\)th node. However, delay K is measured in Equation (15):  
\[K=\frac{G_{N}}{b}, \tag{15}\]  
where \(G_{N}\) denotes the total nodes in the path, and b shows the total nodes. Accordingly, link quality factor is measured in Equation (16):  
\[B_{r,s}=\frac{P_{r,s}}{A_{r,s}}. \tag{16}\]  
Here, \(P_{r,s}\) denotes the power constant between \(r\)th and \(s\)th nodes.
## 2 Materials and Methods
### Adsorption Process  
#### 2.3.1 Adsorption Kinetics  
The study of adsorption kinetics was carried out on barley seeds and biochar, operating under optimal conditions (pH 7 \(\pm\) 0.3; adsorbent dose (0.01-0.02 mm) = 50 mg; dye concentration \(C_{0}\) = 50 mg L\({}^{-1}\) stirring speed 150 rpm), the experiment matrix being adapted from other works [21]. In appropriate beakers, 50 mg of the adsorbent were mixed with 50 mL of the X-PR and MGC solution (\(C_{0}\) = 50 mg L\({}^{-1}\)). The suspension was stirred for 150 rpm at room temperature (21 \({}^{\circ}\)C). At previously defined time intervals (in the range of 0 to 120 min), the two adsorbents were separated from the liquid by centrifugation. The concentrations of the X-PR and MGC in the liquid phase were then determined by measuring the absorbance at 432 nm for X-PR and 617 nm for MGC following the reading on a calibration curve established from a range of X-PR and MGC concentrations from 0 to 50 mg\(\cdot\)L\({}^{-1}\). The amount of X-PR and MGC (\(q_{t}\)) adsorbed by the two materials, as a function of time, was calculated according to the following formula:  
\[q_{e}=\frac{(C_{0}-C_{e})*V}{m} \tag{1}\]  
\[R\%=\frac{(C_{0}-C_{e})}{C_{0}}*100 \tag{2}\]  
where \(C_{0}\) is the concentration of the dye in (mg L\({}^{-1}\)) in the liquid phase, \(V\) is the volume (L) of the dye solution and \(m\) is the mass of the adsorbent (g).  
The four models tested are: the pseudo-first-order model developed by [PERSON] in 1898 [22], which proposed a pseudo-first-order equation, Equation (3), in order to clarify the adsorption of the liquid/solid system. The second-order model is then given by the linear expression Equation (4) [23]. Then, the [PERSON] model can be expressed by the linear formula Equation (5) [24], and finally the intraparticle diffusion model is given by Equation (6) [25]:  
\[\log(q_{e}-q_{t})=\log(q_{e})-\frac{K_{s1}}{2.303}t \tag{3}\]  
\[\frac{t}{q_{t}}=\frac{t}{q_{e}}+\frac{1}{K_{2}q_{e}^{2}} \tag{4}\]  
\[q_{e}=\frac{1}{\beta}\ ln(t)+\frac{1}{\beta}\ ln\ (\alpha\beta) \tag{5}\]  
Figure 10: FTIR spectra of BS\(-\)HVL (X\(-\)PR), BS\(-\)HVL (MGC), BC\(-\)HVL (X\(-\)PR) and BC\(-\)HVL (MGC) after adsorption showing the appearance of the new functional groups in the BC-HVL.  
\[q_{e}=K_{i}\sqrt{t}+C \tag{6}\]  
where:  
* \(q_{e}\) is the amount adsorbed at equilibrium (mg g\({}^{-1}\)),
* \(q_{t}\) is the quantity adsorbed at time \(t\) (mg g\({}^{-1}\)),
* \(K_{\rm S1}\) is the rate constant of first order sorption (min\({}^{-1}\)),
* \(K_{i}\) is the intraparticle diffusion rate constant,
* The value of the ordinate at the origin C provides an indication of the thickness of the boundary layer,
* \(K_{\rm 2}\) (g mg\({}^{-1}\).min\({}^{-1}\)) is the adsorption rate,
* \(\alpha\) is the initial adsorption capacity (mg g\({}^{-1}\) min),
* \(\beta\) is the desorption constant (g mg\({}^{-1}\)),
* \(t\) is the time (min).