esa-sceva/satcom-chunk-collection huggingface dataset:
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 6 Evaluation
### Analyses of Experimental Results  
#### 6.2.1 Examination of CTP under noEH and EH1  
We first conducted two experiments of CTP under deployments noEH and EH1, respectively, in order to examine the effect of EH-nodes on CTP. Then we conducted another two experiments in which La-CTP took over the above two deployments, but we only show part of experimental results in Figure 8 due to the page limit. Figure 8 compares the per-node throughputs of CTP achieved under noEH and EH1. We find that without EH-nodes involved, CTP works very well: most of nodes produce a per-node throughput higher than 86%. When EH-nodes come on, however, CTP deteriorates much in delivering packets to the sink. Even though some nodes can still obtain high per-node throughput, they count for less to guarantee the overall performance because almost half of nodes are lower than 75% in per-node throughput.  
Figure 8 demonstrate two snapshots taken right before the termination of experiment, which respectively indicate the numbers of the parent updates and the loop detections done at each node. Note here that we visually reshape the network topology in Figure 8,c, attempting to demonstrate the runtime characteristics of CTP as clearly as possible. In addition, we did not receive any debugging logs from node 38 as expected, and then we did not plot it in the topologies of Figure 8,c.  
We can easily find, from both Figure 8,c, that if a node has longer paths to the sink and traverses some EH-nodes, such as node 42, 47, or 48, it usually experiences more frequent parent updating and loop detecting. Another observation basically indicates that the further a node is away from the sink, the greater the amount of parent updates and loop detections happened to this node. Actually, those nodes with longer paths to the sink yielded lower per-node throughput due to the existence of EH-nodes on the half way. By the distributions of nodes with higher frequencies of parent updating and loop detecting, we can see an obvious pattern in Figure 8,c--the nodes undergoing more parent updates usually detected more loops. This is not a simple correlation between parent updating and loop detecting, but a causal relationship, as we have analyzed previously. Here, the experimental results indicate that proper control of parent updates is very necessary to restrain loops in EH-WSNs and is a beneficial start for designing effective routing protocols for EH-WSNs.
## 3 Results and Discussion
### Changes in pH and Instrumental Color
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**C**} & \multicolumn{4}{c}{**65 \({}^{\circ}\)C**} & \multicolumn{4}{c}{**70 \({}^{\circ}\)C**} & \multirow{2}{*}{**SEM**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**P**} \\ \cline{3-3} \cline{6-13}  & & & **12 h** & & & & & & & & \\ \hline Moisture \({}^{\mathrm{NS}}\) & 67.25 & 65.21 & 64.46 & 64.31 & 62.64 & 62.68 & 63.54 & 1.125 & 0.0834 & 0.9384 & 0.7428 \\ Crude protein & 26.38 \({}^{\circ}\) & 28.14 \({}^{\mathrm{h}\mathrm{c}\mathrm{c}\mathrm{c}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 29.46 \({}^{\mathrm{h}\mathrm{c}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 29.92 \({}^{\mathrm{h}\mathrm{c}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 30.72 \({}^{\mathrm{a}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 30.41 \({}^{\mathrm{a}\mathrm{b}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 0.559 & 0.0099 & 0.1379 & 0.7518 \\ Crude lipid \({}^{\mathrm{NS}}\) & 6.14 & 8.31 & 7.30 & 7.45 & 7.39 & 8.49 & 7.59 & 0.535 & 0.7738 & 0.7709 & 0.2016 \\ Crude ash \({}^{\mathrm{NS}}\) & 1.13 & 1.21 & 1.21 & 1.13 & 1.14 & 1.23 & 1.04 & 0.086 & 0.5182 & 0.3432 & 0.8211 \\ Cooking loss & 22.88 \({}^{\circ}\) & 32.09 \({}^{\mathrm{h}\mathrm{c}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\) \mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 32.97 \({}^{\mathrm{h}\mathrm{c}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{ }\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\mathrm{}\)} & 33.68 \({}^{\mathrm{h}\mathrm{c}\mathrm{}\mathrm{}\mathrm{  
The b* values in the 65 \({}^{\circ}\)C for 24 h (13.91) and the 70 \({}^{\circ}\)C for 24 h (14.31) treatment groups were significantly higher than those in the control group (12.96), and the b* values among the treatment groups were influenced by the sous-vide cooking duration (\(p\) = 0.0004). A trend of decreasing a* values and increasing b* values with a long sous-vide cooking duration was observed by [PERSON] et al. (2018), [PERSON] et al. (2019), and [PERSON] et al. (2018). This can be attributed to the formation of metmyoglobin and the subsequent heat-induced denaturation of this protein, resulting in the development of a brownish hue.
# Path Segmentation-Based Hybrid Caching in Information-Centric Networks
## 3 Path Segmentation-Based Hybrid Caching
### Caching in Non-Edge Area  
Nodes in the non-edge area receive two types of data packets: a normal data packet and a migrated packet. When a normal data packet is transmitted in a non-edge area, the nodes in the non-edge area collaboratively select a node as the user-edge area's migration node and add the node address to the packet. When the migration node receives the migrated packet, due to the node's limited cache space, the migration node should use an appropriate caching mechanism to decide whether to cache the migrated content.
## 2 Recent Review Articles

Table 1: Statistics of Review Articles on Building Energy Consumption Prediction from 2018 to 2025.  
networks (CNN) and LSTM, which have demonstrated unique advantages in handling temporal features. Secondly, regarding the distribution of research focuses, the issue of data quality has become a consensus challenge across different fields. Approximately 65% of the literature involves preprocessing techniques such as data cleaning, missing value imputation, and standardization. However, there are still significant differences in feature engineering strategies for building heterogeneity (such as the differences in energy consumption between office buildings and residential buildings). Thirdly, in terms of the expansion of application scenarios, the prediction objectives show a diversified trend. In addition to traditional power load prediction, the proportion of research on predicting thermal load, cooling load, and water load has been increasing year by year. Especially, combined load prediction has gradually become an emerging research direction. It is worth noting that there are three contradictions in the methodology of existing research: the gap between algorithm innovation and engineering implementation, the conflict between the dependence of high-precision models on the amount of data and the scarcity of data in practical scenarios, and the tension between black-box deep learning models and the demand for interpretability of the physical mechanisms of building energy consumption. This contradiction is particularly prominent in the field of industrial buildings, as the complex energy flow network and high-frequency dynamic disturbances limit the generalization ability of existing models. In addition to focusing on the algorithms themselves, researchers have also analyzed building energy consumption prediction from multiple perspectives, including datasets, data preprocessing, feature extraction, and model application. However, there is still a lack of a comprehensive framework that can provide in-depth guidance for understanding data-driven building energy consumption prediction methods. Therefore, this paper proposes a comprehensive data-driven building energy consumption prediction framework, covering five key stages: data collection, data preprocessing, feature analysis, model prediction, and result evaluation. This framework serves as a complete guide for practical applications, identifies the specific methods used in each stage, and discusses the advantages and disadvantages of these methods.
# Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram
## 3 Materials and Methods
### Reconstructions  
The reconstructions of all sinograms were performed with our Chebyshev-based method and with FBP, and were implemented in Matlab. The reconstruction grid for the reconstructed images generated by both reconstruction algorithms was \(119\times 119\times 161\) pixels, corresponding to scale factors of 1.17, 1.17 and 0.585 mm/pixel, respectively. For the FBP reconstructions, a ramp filter was applied with a cut-off frequency equal to the Nyquist frequency. In our computations, we chose to take Chebyshev polynomials, \(T_{n}(\rho)\), up to \(N=119\), i.e., equal to the number of the total segments of the \(\rho\) domain, available from the sinogram. The reason for this selection is dictated by the mathematics of Chebyshev polynomials. More specifically, when interpolating via Equation (6), we take into account that the number of points available, located at the roots of the corresponding Chebyshev polynomial, are \(\Lambda\), as in Equations (13) and (15). Furthermore, when choosing \(N\) in this fashion, the approximation Formula (6) is exact for \(\rho\) equal to the \(N\) zeros of \(T_{N}(\rho)\), as in Section 5.8 of [28]. Initially, for both Chebyshev and FBP reconstructions, no post-reconstruction filtering was applied. However, in order to fairly compare the algorithms at equal noise levels, we also employed Gaussian post-reconstruction filtering in the reconstructions of NL3. In this direction, all images corresponding to NL3 (20% of the total counts) were blurred in Matlab with a Gaussian smoothing kernel of three different standard deviations, namely 0.5\(\times\), 1\(\times\), and 1.5\(\times\) sigma. NL3 was selected for further filtering, as in [18], since it corresponds to the noise level of FBP for acquisition duration of 1200 s, which is the suggested acquisition duration according to the NEMA protocol [23].
## 3 Crack Identification and Measurement System
### Measurement System Based on Camera and Laser Rangefinder  
#### 3.1.1 Description of the Proposed Equipment System  
The proposed crack measurement system is composed of data acquisition and image processing. As shown in Figure 3, the camera and the laser rangefinder are fixed on bases and are connected by a rod to ensure the optical axis is parallel during use. The equipment is supported by a tripod, which can be placed anywhere. The image acquisition device consists of a Canon EOS 80D camera with EF-S 18-200 mm zoom focal length, and the focal length is fixed at 200 mm to capture target images clearly from a long distance. Another measuring device is the Leica S910 laser rangefinder--in addition to measuring distance, when it rotates horizontally and vertically, the angle of rotation can be recorded. The stability of the distance measured by the laser rangefinder was tested in different environments--after fixing the device, the same point was measured five times, and the measurement distance remained the same, which proved the feasibility of the equipment. The accessory is a base, where two twists can control the horizontal and vertical rotation of the laser rangefinder. A laptop is adopted to store images with a data cable and record the photographing distance and rotation angle information with a Wi-Fi connection.
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 4 The Proposed Scheme
### SURF and A-KAZE Feature Extraction  
To address the issues of difficult detection and high computational complexity that occur in smooth regions with tampering at the present stage, we combined two detection methods, SURF and A-KAZE. Specifically, these methods are used to extract feature points from tampered images, and descriptions of them are provided in Section 3 [34].  
When using SURF to extract feature points from an image, we set the contrast to \(P_{s}\) and store the extracted feature points in matrix \(x_{s}=\{x_{1},x_{2},\ldots,x_{n}\}\). To better represent the coordinates of each feature point, the coordinates of the \(i\)th feature point are represented as \(x_{s}^{i}=\left(x_{x}^{i},y_{s}^{i}\right)\). When extracting feature points from an image using A-KAZE, the contrast threshold is set to \(P_{A}\) and the extracted feature points are stored in matrix \(x_{A}=\{x_{1},x_{2},\ldots,x_{m}\}\). To better represent the coordinates of each feature point, the coordinates of the \(i\)th feature point are represented as \(x_{A}^{i}=\left(x_{x}^{i},y_{s}^{i}\right)\). Then, SURF feature matrix \(f_{s}=\{f_{1},f_{2},\ldots,f_{n}\}\) and A-KAZE matrix \(f_{A}=\{f_{1},f_{2},\ldots,f_{m}\}\) are calculated separately. In this article, contrast threshold \(P_{s}\) is set to 0.1 and contrast threshold \(P_{A}\) is set to 0.0001.
## 2 Algorithm Principle  
### Sparse Decomposition of Signals  
A dictionary matrix \(D\) represents the atomic library of \(N\) dimensional Hilbert space, as follows:  
\[D=\{\ g_{k};k=1,2,3.....K\} \tag{1}\]  
where \(g_{k}\) is an atom in an over-complete atomic library and is normalized as \(\|g_{k}\|=1\) and \(K\) is the number of atoms in dictionary \(D\). Let \(s(t)\) be a signal to be decomposed, and let \(N\) be the signal length. The signal can be adaptively represented as a linear combination of certain atoms, as follows:  
\[s(t)\approx\sum_{k=1}^{N}c_{k}g_{k}(t) \tag{2}\]  
where \(N\leq K\) and \(c_{k}\) are expansion coefficients. If the signal can be linearly represented by a few atoms with coefficients that are not zero, then approximation of the signal is achieved.
# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 2 Methodology
### Physics-Inspired Metaphor Algorithms
#### 2.3.2 Gravitational Search Algorithm  
This algorithm is inspired by [PERSON]'s law of gravitation and the second law of motion [17]. It treats each candidate solution in the search space as an object whose mass is considered to be its fitness. Heavier objects are considered fitter than lighter objects. The objects are attached to each other with some gravitational force that causes objects to explore the search space. The heaviest object is considered the global best solution. Since the heavier objects attract other objects with more force, the whole population ultimately converges toward the heaviest object, called the global best solution. The algorithm is comprised of a few mathematical equations that are expressed below.  
Force calculation: The force from an object \(j\) on an object \(i\) is calculated using the following equation:  
\[F_{ij}^{d}(t)=G(t)M_{pi}(t)\times\frac{M_{aj}(t)}{R_{ij}(t)+\epsilon}(x_{j}^{d }(t)-x_{i}^{d}(t)) \tag{4}\]  
In the above equation, \(G\) is the gravitational constant that controls the search accuracy, \(M_{pi}\) is the passive gravitational mass of solution \(i\), \(M_{aj}\) is the active gravitational mass of solution \(j\), the distance between solution \(i\) and solution \(j\) is denoted by \(R_{ij}\), \(x^{d}\) is the position of a solution in \(d^{\text{th}}\) dimension, and \(\epsilon\) is a small constant.  
The ultimate force on a solution (mass) is calculated by taking the weighted sum of all the forces on that solution from the \(k_{best}\) solutions, which are calculated as follows:  
\[F_{i}^{d}(t)=\sum_{j\in k_{best},\ j\
eq i}rand_{j}F_{ij}^{d}(t) \tag{5}\]Acceleration calculation: Once the total force on a solution in a particular dimension \(d\) is calculated, the acceleration of the solution in that dimension can be computed using the following equation:  
\[a_{i}^{d}(t)=\frac{F_{i}^{d}(t)}{M_{ii}(t)} \tag{6}\]  
where \(M_{ii}\) is the mass of inertia of solution \(i\).  
Velocity calculation: Based on the acceleration, the velocity of a solution can be computed by adding the acceleration to a fraction of the previous velocity of that solution. The equation to compute velocity is given below:  
\[v_{i}^{d}=rand_{i}\times v_{i}^{d}(t)+a_{i}^{d}(t) \tag{7}\]  
Position updating: To update the position of a solution, the updated velocity is simply added to the old position of the solution, as formulated below:  
\[x_{i}^{d}(t+1)=x_{i}^{d}(t)+v_{i}^{d}(t+1) \tag{8}\]  
Gravitational constant updating: To update \(G\), the following relation is used:  
\[G(t)=G_{0}\exp\biggl{(}-\alpha\frac{t}{t_{max}}\biggr{)} \tag{9}\]  
where \(G_{0}\) is the initial gravitational constant, and \(\alpha\) is a constant. \(t\) and \(t_{max}\) represent the current and final iteration numbers.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 2 Background and Literature Review  
In this section, we present an introduction to routing, RPL protocol and its working principle. Following, we present literature review of the articles that address mobility in RPL and describe the importance of the current research work.
## 1 Introduction
### Related Work  
The large number of reactions catalyzed by microorganisms like _E. coli_ makes it impractical to conduct wet-lab experiments to test all potential genetic modifications, despite the availability of resources such as the Keio collection [20], which serves as a valuable repository for systematic genome-wide testing of single non-essential mutated genes in the _E. coli_ K-12 BW25113 background.  
In their place, algorithms are developed to make the selection of genes to modify more straightforward. Some available algorithms integrate Flux Balance Analysis (FBA) and bi-level optimization to identify reactions that improve metabolite production when silenced [2; 18; 21; 22; 23; 24; 25; 26; 27; 28]. Parsimonious Enzyme Usage Flux Balance Analysis (pFBA) is centered on optimizing biomass production; the conception behind pFBA is that the cells with faster growth rates are selected over multiple generations because they develop a fitness advantage using the least number and quantity of enzymes [29]. pFBA classifies reactions into essential, optima, Metabolically Less Efficient (MLE), Enzymatic Less Efficient (ELE), and no-flux related to biomass production. On their part, the Flux Scanning based on Enforced Objective Flux (FSEOF) registers changes in the fluxes of the metabolic network when the system moves from reference to intermediate states until it arrives at the simulated theoretical maximum flux of the selected target metabolite [30]. The algorithm registers these changes by filtering the fluxes that increase when the target product increases; thus, it predicts reaction candidates to overexpression.  
Transport reactions are crucial in producing metabolites such as tryptophan [4]. Secreting metabolites into the medium improves their production because it reduces the feedback inhibition of the enzymes of the biosynthetic pathway [31]. However, coupling transport reactions to biomass production in metabolic models necessitates multiple modifications to the metabolic network, especially if all competing pathways are contemplated.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 1 Introduction
In this paper the analysis in [37] is extended to investigate if and to what extent the conclusions on the worse EMI-induced capacity loss of SS-modulated signals presented in [37] apply to the case of resonant coupling. The paper is organized as follows: in Section 2, the model of a digital communication channel under EMI resonant coupling is introduced. With reference to such a model, a description of the adverse effects of EMI in terms of capacity loss is introduced in Section 3 and discussed in Section 4. Simulations on the effects of resonant-coupled EMI from a switching mode DC--DC converter on a 4-quadrature amplitude modulation (4-QAM) data link featuring Turbo coding are then presented in Section 5 to verify the theoretical results with reference to a practical case. Finally, in Section 6, some concluding remarks are drawn.
## 4 Applicability of TR and Its Approximate Combination Rules
### Applicability of TR Rule  
The Monte Carlo simulation method was used to analyze the relative error of the combination extreme of the TR rule and the CQC rule to determine the application range of the TR rule. In Monte Carlo simulation, variance of the first wind load effect component was set as 1, and variance of the second wind load effect component was set as 0.1 to 3, so the variance ratio ranged from 0.1 to 3. The correlation coefficient of effect components varied from \(-\)1 to 1 with an increment interval of 0.1. The number of samples was 1000. The Monte Carlo simulation method was used to generate the random component values of specific conditions, and the combined extremums were calculated according to each combination rule. Figure 1 demonstrates how the relative error between the TR rule- and CQC rule-based combination extreme varies in terms of correlation coefficient and standard deviation ratio.  
It can be seen from Figure 1 that when the effect components are positively correlated, uncorrelated, and negatively correlated, the relative error between the TR rule-and CQC rule-based combination extreme first increases and then decreases as \(\sigma_{2}\)/\(\sigma_{1}\) increases, and reaches the maximum when \(\sigma_{2}\)/\(\sigma_{1}\) = 1. The relative error between the TR combination extreme value and the CQC combination extreme value decreases with the increase in \(\rho\). When \(\rho\geq\) 0.4, regardless of the value of \(\sigma_{2}\)/\(\sigma_{1}\), the relative error is less than 10%; when \(\sigma_{2}\)/\(\sigma_{1}\)\(\leq\) 0.4 or \(\sigma_{2}\)/\(\sigma_{1}\)\(\geq\) 2.2, and the effect components are negatively correlated, uncorrelated, and positively correlated, the relative error is less than 10%. In summary, the range where the relative error of the combination extreme between the TR rule and the CQC rule is less than 10% is: \(\rho\geq\) 0.4; \(\sigma_{2}\)/\(\sigma_{1}\)\(\leq\) 0.4; \(\sigma_{2}\)/\(\sigma_{1}\)\(\geq\) 2.2.
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 2 Results and Discussion
### Optimization of the TFME Extraction Procedure
The \(y\)-axis represents log-transformed peak area values, where means are given with a respective standard deviation for three replicates (\(n=3\)) for each extraction time: 10 min (grey boxes), 20 min (blue boxes), 35 min (green boxes), 60 min (red boxes), 90 min (magenta boxes).