esa-sceva/satcom-chunk-collection huggingface dataset:
# Differential Evolution with Linear Bias Reduction in Parameter Adaptation
## 2 Materials and Methods
### Proposed Approach: Linear Bias Reduction  
One of the problems in parameter adaptation for DE is the existence of structural bias in the parameter adaptation process. For example, the problem of reaching different areas of the search space by DE was discussed in [40]. The JADE and SHADE algorithms used the Lehmer mean [41] instead of the classical arithmetical mean to avoid the structural bias in terms of parameter values, because it is much easier for the algorithm to generate better solutions with smaller \(F\) and \(Cr\) values than larger \(F\) and \(Cr\), because smaller parameter values result in more local search, and thus easy improvements. However, such greedy behavior could only be beneficial from a short-term perspective, and only for local search. In [12], the generalized Lehmer mean was proposed, where a larger \(p\) parameter in the mean was allowed:  
\[mean_{p,m,wL}(S)=\frac{\sum_{j=1}^{|S|}w_{j}S_{j}^{p}}{\sum_{j=1}^{|S|}w_{j}S_ {j}^{m}} \tag{17}\]  
The provided equation in its general form defines a group of means, and the values of \(p\) and \(m\) control the bias towards smaller or larger values. For example, \(mean_{0,1,wL}(S)\) is the harmonic mean, \(mean_{0,5,1,wL}(S)\) the geometric mean, \(mean_{1,1,wL}(S)\) the arithmetic mean, and \(mean_{2,1,wL}(S)\) the contraharmonic mean. Any biased mean could be obtained by setting the parameter values, and the graph of possible \(mean_{p,m,wL}\) is presented in Figure 1.  
The behavior of the parameter adaptation in DE could be significantly influenced by the newly generated \((M_{F,h},M_{Cr,h})\) values, so that the setting of \(p\) and \(m\) may influence the sampled \(F\) and \(Cr\) values, thus leading to a more explorative search, with large \(p\) and small \(m\), or a more exploitative search with small \(p\) and large \(m\). Further, in this study, the fixed setting of \(m=p-1\) will be used, as changing only the \(p\) parameter in the Lehmer mean is sufficient to control the biased mean. Inspired by the idea of linear population size reduction, the Linear Bias Reduction (LBR) technique is proposed. As long as in most cases, the computational resource limit is known, at the beginning of the search process, more exploration is required to find the most promising areas of the search space, while at the end, fast convergence is required. The linear bias reduction starts with a large \(p_{max}\) value and gradually decreases the Lehmer mean parameter down to \(p_{min}\) as follows:  
\[p_{g}=p_{min}+(p_{max}-p_{min})*\frac{NFE_{max}-NFE}{NFE_{max}} \tag{18}\]  
Figure 1: Generalized weighted Lehmer mean depending on parameters \(p\) and \(m\). In this study, the lower limit is set to \(p_{min}=1\), which corresponds to the arithmetic mean, while the upper limit \(p_{max}\) is changed. The idea of LBR is inherited from the linear parameter reduction scheme L-SHADE algorithm [10] and other studies, such as [25]. The pseudocode of the L-SHADE-LBR is presented in Algorithm 1. ```
1:Set \(NP_{max},NFE=0,NP=P_{max},D,NF_{max}\).
## 4 Methods Used
### Feature Selection Based on the One-R Attribute Evaluation Technique  
In landslide prediction problems, it is very important to select suitable factors, which can be used to generate the optimal input data for training and testing of the machine learning models. Feature selection is one of the techniques for this task in landslide susceptibility modeling. It helps in evaluating the importance of each factor in predicting the final results using such models on which the irrelevant or unimportant factors might be removed from the input space. Thus, it can increase the quality of input data and enhance the predictive capability of landslide models by decreasing the dimensionality of the input space, preventing redundancy, and decreasing noise and over-fitting problems [78]. There are different feature selection methods used in selecting the suitable factors for prediction modeling such as Information Gain [79; 80], Forward Elimination [20], Backward Elimination [81], and One-R attribute evaluation (ORAE) [82]. Out of these methods, the ORAE, which is one of the effective filter selection methods, was selected for the first time for the landslide susceptibility modeling in this study. The main principle of the ORAE is to use statistical correlation between the output variable and a set of input factors on which it selects the most important factors for modeling. Using the ORAE, one rule (One-R) is separately constructed for each factor in the training dataset, and then the rule, which has the smallest error metric, is chosen for modelling. On the base of the smallest calculated error metrics, it will independently sort all factors according to their importance to solve prediction problems.
# DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks
## 7 Conclusions  
In this paper, we proposed a hybrid MAC protocol in a UASN for data collection. We focused on the data collection applications that make the traffic load non-uniformly distributed in the network. According to the traffic load, the network is partitioned into two kinds of sub-networks, and different MAC protocols are carried out in the different sub-networks. A modified contention-based MAC protocol is used in the sub-network with a light traffic load, to improve the performance, and a reservation-based MAC protocol is used in the sub-network with heavy traffic load, the to avoid the transmission collision. In addition, the proposed MAC protocol considered the access of mobile nodes performing the data collection task. Theoretical analysis and simulation results demonstrate that the modified contention-based MAC (CT-MAC) protocol outperforms the conventional contention-based MAC protocols for data collection in the UASN, in terms of the normalized throughput and the average packet delay. Moreover, the proposed MAC protocol performs well in the DCO-UASN, with, for example, a large normalized throughput, a low end-to-end packet delay, a small energy overhead, and a high fairness.  
In the future research, we will extend this work to a larger UASN with a more complicated traffic load distribution. On the other hand, we will validate the proposed DCO-MAC protocol in the real-life underwater acoustic communication transmission scenarios.  
[PERSON], [PERSON], and [PERSON] proposed the hybrid MAC protocol for data collection in underwater acoustic sensor networks, and compared the performance; [PERSON] and [PERSON] wrote the paper.  
This work is partly supported by the National Natural Science Foundation of China (NSFC) (No. 61531017), National Key Research and Development Program of China (No. 2017 YFC0305903, No. 2016 YFC0301206), and the Fundamental Research Funds for the Central Universities in Zhejiang University.  
**Acknowledgments:** The authors would like to thank all the anonymous reviewers for their valuable comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results and Discussion
### End of Discharge (EOD) Prediction
#### 5.2.3. EOD Prediction (Third Test)  
The third test involved cell B0038, which was cycled at different ambient temperatures of 24 and 44 \({}^{\circ}\)C during its lifetime using different discharge currents of 1, 2 and 4 A. The EOD voltage for this cell was 2.2 V and the last discharge cycle with a discharge current of 4 A was selected. The framework proved to work for different environmental and loading conditions for this test. The results for the two EOD predictions carried out are shown in Figure 15. Two EOD predictions were carried out and the results are shown in Figure 15. It can be seen that the algorithm needs some time to converge due to the initial voltage drop in the cell, but the predictions done around times 600 and 1100 show a quite accurate estimation of the EOD time.  
Figure 14. EOD prediction in discharge cycle 160 of cell B0005.  
Figure 15. EOD prediction in last discharge cycle of cell B0038.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Notable Components of DT Implementations
#### 3.2.3 Visualization and Representation  
Visualization and representation play a vital role in DTs, offering various forms of virtual representation depending on the specific application. DTs can leverage 3D visualizations to facilitate collaborative design or planning [69]. Depending on the context, these connections can manifest in various forms, such as a 3D representation, hologram, humanoid social robot, or purely as software components without physical attendance. Consequently, DT commonly refers to the digital depiction of a real-world object, system, or entity's physical twin that faithfully replicates a unique item, process, person, organization, or even a legendary narrative. Composite digital twins are formed when multiple digital twins merge. These DTs, as reflections of the actual world, share similar characteristics, necessitating the modelling of digital processes and objects with composition, flexibility, sensing, collaboration, and simulation capabilities. Furthermore, predictions suggest that future DTs will evolve toward a holistic virtual world [70].
# A Voxel-Based Watermarking Scheme for Additive Manufacturing
## 3 Experimental Results
### Watermark Verification for Printed Parts  
In the third experiment, we assessed the capacities of our verification method for printed parts. At first, we watermarked a plate and utilized the slicer to translate it into a G-code program. Then, we fabricated physical copies of the plate and the mug by using a Fusion Decomposition Modelling (FDM) printer. The plate was printed using acrylonitrile butadiene styrene (ABS) filament while the mug was manufactured with polylactic acid (PLA) thermal plastic. Since we did not have any thermal imaging facilities to retrieve watermarks, we illuminated the physical parts by using bright light sources and captured photos of these printed models by using a cellular phone camera.  
The results are presented in Figure 8. The photos show that the watermarks are invisible under ordinary lighting conditions (the left images of parts (a) and (b)). As the light sources are intensified, the watermarks show up and can be visually evaluated (the right images pf parts (a) and (b)). Based on several test results, we find that the visual detection procedure is greatly influenced by the raw materials. Since the ABS filament possesses higher transparency than the PLA thermal plastic, it is easier to detect the watermark in the plate than the mug.
## 5 Experimental Findings
### Difficult Cases  
As previously highlighted, medical images are characterized by an intrinsic variability in which boundaries or anatomical details may be not well defined. Furthermore, noise corrupts digital images, thus affecting certain features within the original image. MRI suffers from various kinds of noise and artefacts because of the nature of the signal detection and spatial encoding [37; 38]. For instance, hardware-induced errors are often caused by the complicated acquisition scheme depending on radiofrequency coils, while thermal noise can derive from transmission lines, receiver circuits and polarization magnetic field \(B_{0}\) drift during the scan acquisitions. In addition, natural body motion (e.g., respiratory and cardiac motion) can degrade the image quality too. As a consequence, even after a proper pre-processing step, it is common to deal with low-contrast, noisy images. The proper lesion segmentation in this kind of images is not a trivial task and user interaction would be required to produce accurate results.
# Progressive Multiple Alignment of Graphs
## 4 Computational Results
### Consensus Graphs  
An important motivation for graph alignments is consensus graphs generalizing the idea of consensus sequences [50] and consensus forests [4]. Given an alignment graph \((G,f)\), a consensus graph \(\hat{G}\) is obtained as an induced subgraph of \(G[W]\) with a vertex set \(W\) obtained from the gap pattern function \(f:V(G)\rightarrow\{0,1\}^{N}\) by means of a voting procedure. In the simplest case, the vertex set \(W=W_{\alpha}:=\{x\big{|}\sum_{i=1}^{n}f_{i}(x)\geq\alpha N\}\) comprises all alignment columns with a sufficient fraction \(\alpha\) of non-gap entries. Vertex and edge labels for \(W\) and \(E(G[W])\), resp., can also be obtained with the help of suitable voting procedure. A key property of consensus sequences is that they approximate the ancestral state or the master sequence of viral quasi-species. Consensus sequences also are of use, e.g., to determine an accurate genome or transcript sequence from a set of noisy sequencing reads. In the setting of graphs, suppose we have a set \(G_{1},\ldots,G_{N}\) of noisy empirical estimates of a graph \(G\). Then, we expect that, for a \"reasonable\" range of \(\alpha\), the graph \(G[W_{\alpha}]\) will be a very good approximation of the source graph \(G\).  
The consensus graph allows one to evaluate the accuracy of the reconstruction portrayed by the alignment graph. A comparison of the consensus graphs obtained from the alignments with different thresholds shows that retaining the columns that contain more gap than non-gap entries yields a very good approximation of the initial graph \(G_{0}\).  
The residual distance roughly matches the differences shared between \(G_{0}\) and all its offspring, namely those changes that separate \(G_{0}\) and \(G_{1}\). This matches the behavior of sequence alignments. In particular, it shows that graph alignments can be used as a noise-reduction method. We also compared the alignment of the mutant-scenarios, which were obtained using a kernel-based guide tree, against alignments obtained through a randomly generated guide tree. Note that random guide trees can also be produced through the WPGMA clustering by providing this with a random array of positive numbers as a distance matrix instead of the kernel-derived distances.  
Figure 5b shows a slightly better performance of the kernel-based guide trees: on average, the MCIS-distance between the initial graph \(G_{0}\) and the consensus graphs is systematically larger than for random trees. Similarly, the distance between \(G_{0}\) and the consensus is slightly smaller for the similarity-based guide trees. In addition, the implementation of ambiguous edges reduces the distance between graphs, thereby improving the alignment. Although alignments obtained with a kernel-based guide tree are systematically better than the ones obtained from random guide guide trees, these differences are small, amounting to typically less than 10%. This is observed consistently for MCIS computed with both VF2_step and with the Iterative_Trimming approach.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
## 3 Proposed Artificial Neural Network (ANN) Model  
Artificial neural networks, often known as ANNs, are artificial intelligence (AI) technology capable of handling complex problems in a manner similar to that of humans. Multiple applications have used ANNs as a tool, including image recognition, audio recognition, and natural language processing [32; 33].  
To solve a problem, the ANN does not need prior knowledge of the relationships between the variables involved, unlike many statistical and probabilistic techniques. Similar to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Descriptive** & **MIC** & **Distance** & **Elevation** & **Blast** & **Blast** & **Measured** & **Measured** & **SD** & **PPV** \\  & **(kg)** & **(m)** & **(m)** & **Longitudinal** & **Latitude** & **Longitudinal** & **Latitude** & **(m/kg\({}^{12}\))** & **(mm/s)** \\ \hline Count & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Mean & 26.1 & 350.01 & 19.1 & 39.98 & 140.0 & 39.98 & 140.0763 & 68.64 & 0.62 \\ Std & 2.13 & 70.77 & 9.03 & 0.000709 & 0.00058 & 0.0015 & 0.001312 & 13.8 & 0.36 \\ Min & 23 & 206.64 & 1 & 39.98 & 140.1 & 39.98 & 140.0744 & 40.5 & 0.18 \\ \(25\%\) & 24.42 & 296.067 & 15 & 39.98 & 140.07 & 39.98 & 140.0752 & 58.06 & 0.365 \\ \(50\%\) & 25.5 & 348.6715 & 18 & 39.986 & 140.07 & 39.98 & 140.0763 & 68.3 & 0.53 \\ \(75\%\) & 28.5 & 400.12 & 26 & 39.98 & 140.08 & 39.9 & 140.0771 & 78.4 & 0.77 \\ Max & 32.5 & 482.18 & 44 & 39.987 & 140.08 & 39.9 & 140.081 & 94.5 & 1.87 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the input and output variables.  
Figure 5: Frequency distribution of the ground vibration dataset.  
nonlinear regression models, ANNs can precisely solve nonlinear regression problems; the similarity in structure between ANNs and the human brain makes this possible [22; 34; 35].  
Some researchers, such as [PERSON] and [PERSON] [33], believe that the feed-forward back propagation (BPNN) algorithm is the most efficient learning technique for multilayer neural networks, among the several algorithms that may be utilized in an ANN (Figure 6). The learning process may be either unsupervised or supervised. Unsupervised is the most common approach for regression and classification problems, and it is used to predict the peak particle velocity (PPV) in this study. The ANN must be trained by analyzing many input and output patterns. The ANN then interprets the observational data into the hidden layer. First, a layer, or layers, of hidden neurons create the weights transmitted by the transfer function of the neurons in the input layer, and then the output layer performs the desired output prediction.
# High-Frequency Limits of Graphene Field-Effect Transistors with Velocity Saturation
## 3 Characterization of Low-Frequency GFETs  
Figure 1c,d shows the I-V characteristic of sample-A in the p-doping and n-doping regime respectively. Both regimes show a trend toward saturation, with larger currents on the p-side due to a lower contact resistance (the contacted graphene is p-doped at \(\sim\)0.7 \(\times\) 10\({}^{12}\) cm\({}^{-2}\) by the contact gate kept at a constant voltage here). Figure 1e shows sample-A's DC resistance for increasing bias voltages \(V_{ds}\). In the low doping regime, there is a broadening of the resistance peak as \(V_{ds}\) is increased, which we attribute to a thermal effect. More interestingly, in the highly doped regime, we observe an increase of the resistance which is generally attributed to optical phonon scattering. However the relevant quantity is rather the small signal resistance \(dV_{ds}/dI\), shown in Figure 1f, which is directly obtained from the S-parameters and averaged over a 10 MHz to 4 GHz bandwidth. Indeed, on one hand, the small signal resistance is more sensitive to current saturation and on the other hand, it is used to describe the operation of an RF transistor [21]. In addition to the increase of resistance in the doped regime, we observe a pronounced shift of the resistance peak towards positive gate voltages (the resistance peak shifts towards negative \(V_{g}\) when \(V_{ds}\) is negative). This peculiar shape is characteristic of locally gated devices [11] and not observed when using a remote Si gate. This effect, already introduced in Refs. [8; 22], is a consequence of finite quantum capacitance effects and geometry which lead to a non-uniform carrier density in the channel at large \(V_{ds}\) and eventually to a Dirac pinch-off. The next section is devoted to the development of a comprehensive model taking into account both mechanisms.  
Figure 2: Radio-frequency GFETs. (**a**) Optical image of sample-B before graphene and contact deposition. Dimensions: \(L=800\) nm, \(W=22\) μm and \(t_{BN}=18\) nm. (**b**) Small signal lumped elements circuit of an RF transistor. (**c**) RF small signal resistance \(r\) and transconductance \(g_{m}\) of sample-B measured at 2 GHz for biases in the 0–2 V range. (**d**) Logarithmic plot of the current H(f) and Mason U(f) gains measured between 1 GHz and 40 GHz after de-embedding. (**e**) \(V_{g}\) dependence of the corresponding measured cut-off frequencies (black circles) together with the model prediction (red lines).
## 6 Field Experiments
### Experimental Setup
This is done to mitigate the effects of interference between various receivers. It also means that the effects of channel noise and inter-receiver interferences are negligible between each RSS spacial sample, as each sample is measured at more than \(\frac{\lambda}{2}\) spacing. Moreover, we pre-select the best channel for each receiver based on the traffic conditions to avoid adjacent and co-channel interference, and hence, channel optimization is done beforehand. The settings for the algorithmic parameters used in the experiments were as follows: learning rate constant \(\kappa=0.1\); IIR filter coefficient \(\alpha=0.9\); sensors' separation distance \(\Delta x=0.16\) m, \(\Delta y=0.2\,\mathrm{m}\); \(RSS_{\mathrm{threshold}}\) is set to \(-130\,\)dBm because of the space restrictions; threshold in difference between the RSS from the server and client nodes \(RSS_{\mathrm{difference}}=2\,\)dBm; gradient threshold \(g_{\mathrm{threshold}}=0.5\). Certain constraints in the experimental setup are listed below. * Due to the fundamental nature of radio signal propagation in physical environments, the theoretical global optimum position where the objective function value is maximum cannot be determined without a dense RSS sampling of the complete environment. * Because of large metal objects in the environment, the radio reflections from such objects constitute a major part of the intensity in the received signal [6], and hence, the direction of RSS determined using RSS gradients could point to the metal objects themselves instead of pointing to the actual radio source. The above-mentioned physical constraints and fundamental limitations complicate determining the performance metrics used in Section 5.2, and therefore, we report mainly the success rate (reaching the 10% threshold of theoretical global optimum position) and prove the performance of our algorithm by showing the continuous improvements in the measured RSS values on both client and server sides.
# Silver Isotopes in Silver Suggest Phoenician Innovation in Metal Production
###### Abstract  
The current study presents Ag isotopic values of 45 silver artifacts with known Pb isotopic composition from the Southern Levant. These items originate from seven pre-coinage silver hoards, dating from the Middle Bronze Age IIC to the end of the Iron Age (\(\sim\)1650-600 BCE). These are the earliest silver artifacts analyzed for Ag isotopes; all former studies were performed on coins. All the sampled silver in this study contains relatively unfractionated Ag (\(-2\leq\epsilon^{109}\)Ag \(\leq\) 1.5) that was more likely produced from hypogene, primary Ag-bearing minerals (e.g., galena and jarosite) and not from native, supergene silver. Four of the sampled hoards containing silver from Anatolia and the West Mediterranean (Iberia and Sardinia) are associated with the Phoenician quest for silver (\(\sim\)950-700 BCE). A significant amount of this Phoenician silver (12/28 items) plots within a narrower range of \(-0.5\leq\epsilon^{109}\)Ag \(\leq\) 0.5. This is in contrast to non-Phoenician silver, which mostly underwent some degree of fractionation (16/17 items \(\epsilon^{109}\)Ag \(\geq\) 10.5l). The results suggest that while all silver was exploited from primary ore sources, the Phoenicians dug deeper into the deposits, reaching ore minerals that did not undergo any weathering-associated fractionation. The results also call for further investigation regarding the influence of sealing and bundling in silver hoards on post-deppositional fractionation of Ag isotopes.  
silver isotopes; silver hoards; Levant; Phoenicia; Ag-fractionation +
Footnote †: journal: Applied Sciences