esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adaptive and Secure Holographic Image Watermarking Scheme
###### Abstract  
A novel adaptive secure holographic image watermarking method in the sharp frequency localized contourlet transform (SFLCT) domain is presented. Based upon the sine logistic modulation map and the logistic map, we develop an encrypted binary computer-generated hologram technique to fabricate a hologram of a watermark first. Owing to the enormous key space of the encrypted hologram, the security of the image watermarking system is increased. Then the hologram watermark is embedded into the SFLCT coefficients with Schur decomposition. To obtain better imperceptibility and robustness, the entropy and the edge entropy are utilized to select the suitable watermark embedding positions adaptively. Compared with other watermarking schemes, the suggested method provides a better performance with respect to both imperceptibility and robustness. Experiments show that our watermarking scheme for images is not only is secure and invisible, but also has a stronger robustness against different kinds of attack.  
image watermarking; encrypted computer-generated hologram; entropy; sharp frequency localized contourlet transform; Schur decomposition +
Footnote †: journal: Computer Science
# Throughput Capacity of Selfish Wireless _Ad Hoc_ Networks with General Node Density
## 1 Introduction
We denote these networks as SWANETs, which were widely researched in connectivity [20] and routing protocol [21]. [PERSON] [22]_et al._ proposed an easy and efficient cooperative of neighboring (COOPON) technique to detect the selfish cognitive radio attack. It is known that selfishness always comes with a price. The price may be tolerable in small-scale WANETs, but it may dominate the consumption of scarce network resources in large-scale WANETs. This situation makes the investigation of throughput with selfish characteristic in large scale WANETs an important open challenge. In this paper, we consider \(n\) nodes randomly and independently distributed in an area with dimension of \(\sqrt{A}\times\sqrt{A}\). The communication between source and destination nodes uses multi-hops transmission. we define \(p(n)\) as the probability that a node will forward a packet. When \(p(n)<1\), in general, there is network performance degradation because we require all communications to operate on partial nodes, and some network resources (_i.e._, the selfish nodes) cannot be utilized compared to WANETs. Therefore, it is natural to ask the following: What is the price of selfishness (performance degradation) we have to pay in SWANETs? We formally characterize the relation between the probability \(p(n)\), node density \(\zeta\), and network performance. Then, we answer these questions with rigorous analysis based on reasonable assumptions on SWANETs. The main contributions of this paper can be summarized as follows:  
1. Comparing to previous research, we firstly consider the model of selfish wireless _ad hoc_ network, which is more realistic. 2. We derive the asymptotic throughput capacity of the network combining selfish feature and general node density, which is different from the previous works of dense or extended networks. 3. We observe that the selfishness degrades the achievable throughput with a factor \(\sqrt{p(n)}\), where \(p(n)\) is the probability of forwarding transmission. In addition, the node density impacts the throughput trivially. The roadmap of the paper is as follows. In Section 2 we introduce the network model in detail. The achievable rate is derived in Section 3. In Section 4, we discuss the results and conclude the paper in Section 5.
# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 1 Introduction
### Artificial Intelligence in Alzheimer's Disease Detection  
Recent advances in artificial intelligence (AI), especially in machine learning (ML) and deep learning (DL) in [10; 11; 12; 13; 14], have introduced promising approaches to enhance the accuracy and efficiency of AD detection.  
AI-based tools, especially those using convolutional neural networks (CNN), like [14; 15; 16] have demonstrated exceptional performance in image classification tasks, - such as MRI [17], or CT [18], making them particularly relevant for medical imaging applications. These AI models can detect subtle patterns in brain scans that human experts might ignore [19], thus improving diagnostic accuracy and providing valuable insights into disease progression [20; 21]. However, despite technological advances, integrating AI into clinical practice remains a challenge. The \"black box\" nature of many AI models poses a major barrier, limiting their interpretability and reducing trust among healthcare providers. In clinical settings, where rapid and reliable decision-making is essential, the ability to understand and trust AI-driven recommendations is critical. The explainability of AI models is not just a technical concern; Rather, it is also a prerequisite for its acceptance and adoption in clinical settings [22; 23].
###### Abstract  
Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNs), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network types, namely, the nonlinear autoregressive network with exogenous inputs (NARX) and the deep feed-forward (DFF) neural network, have been developed and compared for modeling the maximum output power of HPV panels. Both neural networks have four exogenous inputs and two outputs. Matlab/Simulink is used in evaluating the proposed two models under a variety of atmospheric conditions. A comprehensive evaluation, including a Diebold-Mariano (DM) test, is applied to verify the ability of the proposed networks. Moreover, the work further investigates the two developed neural networks using their actual implementation on a low-cost microcontroller. Both neural networks have performed very well; however, the NARX model performance is much better compared with DFF. Using the NARX network, a prediction of PV output power could be obtained, with half the execution time required to obtain the same prediction with the DFF neural network, and with accuracy of \(\pm 0.18\) W.  
renewable energy; modeling; heterogeneous photovoltaic panel; artificial neural network; low-cost microcontroller; modeling; time series prediction Article Toward Better PV Panel's Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs
## 3 Results and Discussion
### Polyphenic Composition
On the contrary, Soxhlet extracts showed  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Thyme**} \\ \hline  & **N2h** & **N6h** & **S2h** & **S6h** & **UAE** & **M** \\ \hline Gallic acid & 42.74 \(\pm\) 0.50 d & 58.73 \(\pm\) 0.90 b & 678.67 \(\pm\) 0.40 a & 48.78 \(\pm\) 0.32 c & - & 40.23 \(\pm\) 0.89 e \\ \(p\)-OH-benzoic acid & 190.47 \(\pm\) 1.02 b & 197.15 \(\pm\) 0.89 a & - & 148.68 \(\pm\) 0.98 c & 55.04 \(\pm\) 0.95 d & 17.97 \(\pm\) 1.02 e \\ Chlorogenic acid & 66.54 \(\pm\) 0.96 c & 120.44 \(\pm\) 0.75 a & 30.07 \(\pm\) 0.89 d & 97.21 \(\pm\) 0.98 b & - & - \\ Vanillic acid & - & - & - & - & 376.27 \(\pm\) 0.56 a & - \\ Caffeic acid & - & - & - & - & 145.92 \(\pm\) 0.85 a & - \\ Springic acid & - & - & - & - & 159.69 \(\pm\) 1.02 a \\ Ferulic acid & - & 139.24 \(\pm\) 0.96 b & - & 516.02 \(\pm\) 0.84 a & - & - \\ Rosmarnic acid & 34201.41 \(\pm\) 1.05 b & 33955.20 \(\pm\) 1.02 c & 51686.96 \(\pm\) 0.95 a & 31549.93 \(\pm\) 1.25 d & 15049.48 \(\pm\) 1.09 e & 261.55 \(\pm\) 0.82 f \\ Lutdelin & 2671.96 \(\pm\) 1.10 c & 1554.86 \(\pm\) 1.05 f & 1931.49 \(\pm\) 0.95 d & 17042.12 \(\pm\) 1.32 e & 4143.43 \(\pm\) 0.65 b & 2099.47 \(\pm\) 0.84 a \\ Apigenin & 6068.97 \(\pm\) 1.15 b & 5309.40 \(\pm\) 1.03 c & 2909.13 \(\pm\) 1.01 e & 76187.27 \(\pm\) 0.98 a & 4416.70 \(\pm\) 0.87 d & - \\ Carvacrol & 3499.84 \(\pm\) 1.15 b & 885.03 \(\pm\) 1.02 d & 2873.81 \(\pm\) 0.99 c & 5595.41 \(\pm\) 1.05 a & - & 220.49 \(\pm\) 0.98 e \\ \hline \multicolumn{4}{c}{**Hemp**} \\ \hline Gallic acid & - & 52.29 \(\pm\) 0.98 c & 118.07 \(\pm\) 0.32 b & 408.92 \(\pm\) 0.63 a & 35.10 \(\pm\) 0.98 d & 36.02 \(\pm\) 0.65 d \\ \(p\)-OH-benzoic acid & - & 47.70 \(\pm\) 0.75 c & 95.18 \(\pm\) 0.95 a & 36.42 \(\pm\) 0.35 d & - & 52.41 \(\pm\) 0.96 b \\ Chlorogenic acid & - & - & - & - & - & - \\ Vanillic acid & - & - & - & - & - & - \\ Caffeic acid & - & - & 36.98 \(\pm\) 0.48 b & - & 81.81 \(\pm\) 0.91 a & - \\ Sympie acid & - & - & - & - & 57.28 \(\pm\) 0.64 a & - \\ Ferulic acid & - & 247.77 \(\pm\) 0.64 a & - & - & - & 96.70 \(\pm\) 0.93 b \\ Rosmarnic acid & 259.56 \(\pm\) 0.97 c & 27.09 \(\pm\) 0.85 f & 206.30 \(\pm\) 0.94 d & 152.06 \(\pm\) 0.65 e & 514.33 \(\pm\) 1.01 a & 328.21 \(\pm\) 1.10 b \\ Lutdelin & 1572.05 \(\pm\) 1.04 a & 304.37 \(\pm\) 1.10 e & 502.83 \(\pm\) 0.95 d & 753.01 \(\pm\) 0.84 c & 1384.09 \(\pm\) 1.09 b & 127.67 \(\pm\) 1.03 f \\ Apigenin & 72.99 \(\pm\) 1.02 a & 51.43 \(\pm\) 0.48 c & 35.77 \(\pm\) 0.95 d & 54.22 \(\pm\) 1.06 b & - & - \\ Carvacrol & - & - & - & - & - & - \\ \hline \multicolumn{4}{c}{**Coriander Seeds**} \\ \hline Gallic acid & 42.37 \(\pm\) 0.98 b & 49.05 \(\pm\) 0.65 a & 22.45 \(\pm\) 0.35 d & 22.73 \(\pm\) 0.36 d & - & 31.02 \(\pm\) 0.39 c \\ \(p\)-OH-benzoic acid & 274.83 \(\pm\) 0.95 a & 31.80 \(\pm\) 0.98 d & - & 89.22 \(\pm\) 0.84 b & - & 46.61 \(\pm\) 0.91 c \\ Chlorogenic acid & 74.80 \(\pm\) 0.67 e & 4804.09 \(\pm\) 0.92 b & 149.47 \(\pm\) 0.41 c & 490.56  
higher concentrations of chlorogenic, vanillic, and caffe acids, in addition to the presence of syringic acid, that was not detected in N2h and N6h.
## 2 Main Blocks for Wirelessly-Powered Cage System
### Near-Field Data Telemetry  
Since the IMD in the wirelessly-powered cage typically monitors different types of biomedical signals recorded from the electrodes and performs stimulation to tissue for the treatment, the data transmission between the mobile device and the wirelessly-powered cage is required to collect the data and to control the device. In terms of the average power dissipation in the recent IMDs, composed of amplifiers, signal conditioning, digitization, processing, and radio frequency (RF) transmission blocks for neural recording application, the traditional far-field data communication blocks dedicate a relatively large portion of the IMD power budget compared to the other functional blocks [7]. Therefore, the near-field communication can be regarded as a more suitable method in terms of power saving in IMDs, considering that the animal subject moves within the wirelessly-powered cage, and the Tx/Rx coils are already implemented for the wireless powering. While there have been many data telemetry techniques are introduced, it is important for the designers to select suitable data telemetry methods, considering the practical limitations of IMD design related to key aspects, such as power budget, silicon area, IMD dimensions, temperature elevation, data bandwidth, sensitivity, reliability (Bit-Error Rate, BER), and robustness against various perturbations [7].
# Chaos-Based Underwater Communication With Arbitrary Transducers and Bandwidth
## 3 Simulation Results and Performance Analysis
### The BER Performance in the Underwater Acoustic Channel  
The conventional DCSK [21], NR-DCSK [23], HE-DCSK [28], RM-DCSK [27], PS-DCSK [30], CDSK [31] and the proposed method are simulated over the underwater acoustic channel at different \(E_{b}/N_{0}\) levels. The optimal spreading gain \(L=1000\) (\(n_{samp}=10\), \(N_{s}=100\)), \(f=50\) kHz are used for the proposed method, the conventional DCSK, NR-DCSK, CDSK, HE-DCSK, and RM-DCSK. In Figure 7, the BER curves of the proposed method are compared with other methods for the same transmission rate \(R_{b}=1000\) bits/s. It can be seen from Figure 7 that the BER performance of HE-DCSK and RM-DCSK in the underwater acoustic channel appears to be identical. The DCSK system shows similar BER as compared to PS-DCSK and it appears to be better than those of HE-DCSK, RM-DCSK and CDSK. The reason is that the orthogonality of the chaotic signal in HE-DCSK, RM-DCSK and CDSK is destroyed over the complex channel, increasing the inter-symbol interference of the reference signal and the information bearing signal cross correlation in the correlator outputs. Thus, these methods are less robust in the underwater acoustic channel. In the complicated acoustic channel, the proposed method has better BER performance as compared to NR-DCSK (\(p=5\)) because of the serious distortion caused by the acoustic channel. Moreover, NR-DCSK is an ultra wide band communication method, the larger \(p\), the better BER performance. However, for the underwater acoustic channel, the available bandwidth is very limited, which means the NR-DCSK would not be a better choice for the acoustic communication channel because of the \(p\) times bandwidth needed as compared to other DCSKs. Thereby, the proposed method shows the best performance by using the discrete optimal SNR points to decode the information, which is superior to our previously proposed decoding method using entire filter output [12]. The matched filter and optimal SNR points correlation relieves the effect of interference caused by multipath propagation and ambient noise.
###### Abstract  
This study focused on improving the mechanical properties of hemp-fiber-reinforced epoxy (HFRE) composites by modifying the surface of hemp fibers (HFs) using dielectric barrier discharge (DBD) plasma treatment. By exposing the fibers to different gas mixtures Ar, Ar+N2, and Ar+O2, the surface of the fibers was altered, adding functional groups, increasing surface roughness, and improving crystallinity. The researchers created HFRE composites using both untreated and plasma-treated HF, and then tested their mechanical properties. The results revealed that Ar+O2 plasma treatment boosted both the tensile strength (by 15.2%) and energy absorption of the composites. To fine-tune the process, the response surface methodology (RSM) was used to determine the most important factors for optimizing the treatment: input power and treatment time. The ideal conditions were found to be 162.63 W of power and 10 min of treatment. These findings highlight the potential of DBD plasma as a reliable method for modifying the surface of hemp fibers, even with changes in the setup or reactor design. Overall, this approach shows great promise for industrial applications, providing an effective way to improve the strength and durability of HFRE composites for a variety of uses.  
h _Applied Sciences_  
[[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)]([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).
## 3 Methodology
### Planning for the Dimensional Inspection
#### 3.1.2 Feature Selection  
As mentioned starting Section 3, the metrologist interactively selects the different CAD features (FACEs) from \(\mathcal{C}\) associated with the workpiece revolution axis \(A=(\vec{v},a_{0})\) (Figure 3a). In this case, the metrologist selects the CAD FACEs \(\mathcal{C}_{bore}\) (blue) of the cylindric surface which dictate the rotation of the workpiece. The axis vector of \(\mathcal{C}_{bore}\) defines the theoretical revolution axis vector \(\vec{v}\) (green).  
On the other hand, the metrologist must define the axis point \(a_{0}\) as a reference point. In the context of stub axle forming, the point is computed as follows:  
1. The metrologist selects the CAD FACEs \(\mathcal{C}_{cone}\) (red) corresponding to a conical surface at the bottom of the punch zone of the workpiece (Figure 3a).
2. The metrologist defines the datum diameter \(d>0\). In this case, the metrologist defines \(d\) as the diameter of the supporting fixture for the machining of the workpiece (after it has been formed).
3. The point at the revolution axis \(A\) where the surface \(\mathcal{C}_{cone}\) attains the diameter \(d\) is the theoretical axis point \(a_{0}\). The diameter \(d\) is measured perpendicular to the axis \(A\) (see Figure 3b). The point \(a_{0}\) is a reference point for machining operations (after the piece has been formed).  
The features \(\mathcal{C}_{bore}\) and \(\mathcal{C}_{cone}\) (Figure 3a) have been chosen for the definition of the reference axis \(A\) due to two main reasons:  
1. \(\mathcal{C}_{bore}\) is the part of the punch that suffers less wearing since the direction of the compression load during the forging process is parallel to its axis. This fact makes this geometry more stable from a dimensional assessment perspective.
2. The surfaces \(\mathcal{C}_{bore}\) and \(\mathcal{C}_{cone}\) are the same surfaces used to hold the workpiece during the posterior machining process. In this way, the algorithm uses the same coordinate system that will be used in the next step of the manufacturing process. In addition, it can be said that any possible registration error induced by the tool wearing is not relevant given that the subsequent machining process will use the same defective geometries to establish its reference frame.  
The runout height \(h>0\) is the distance from \(a_{0}\) along the axis \(A\) (Figure 3b) where the circular runout is measured. This height \(h\) is manually defined by the metrologist.  
Figure 2: User-assisted alignment of correspondences [28]. The metrologist selects 3 corresponding points in both the CAD (orange) and a scanned mesh (gray). (**a**) CAD reference and its coordinate system \(W\); (**b**) Scanned mesh and its coordinate system \(W_{M}\); (**c**) Alignment of corresponding points [28].
## 3 Results and Discussion
### Effect of LED Light Treatments on Phenolic Compounds
 Values are expressed as mean \(\pm\) SD (\(n\)To address the incomplete and tentative identification of phenolic compounds as well as the lack of standards and expression of their content in equivalents of selected compounds, spectrophotometric methods were used to supplement the results presented in Tables 4 and 5. Therefore, the total phenolics were also determined by the colorimetric method with the Folin-Ciocalteu reagent and expressed as gallic acid equivalents (Table 6). Moreover, the content of total anthocyanins was estimated by pH-differential methods and expressed as cyanidin-3-glucoside equivalents. Both methods have been used in _Brassica oleracea_ microgreens research [30; 44; 45; 46; 47]. The results presented in Table 6 proved that the addition of red or blue light to white light significantly increased the content of both total phenolics and anthocyanins, except for anthocyanins in red cabbage microgreens. The accumulation of phenolic compounds in microgreens was particularly favored by the addition of blue light, causing a 2.0-4.6-fold and 1.4-1.7-fold increase in the content of phenolic compounds and anthocyanins, respectively. Furthermore, greater content increases were found in the case of white cabbage microgreens. Total phenolic compounds and anthocyanin levels were higher in red cabbage microgreens. The content of total phenols and anthocyanins in red cabbage microgreens in our study exceeded the content determined in 10-day-old microgreens grown using soilless culture and white fluorescent light [30]. For comparison, these microgreens contained 2106 mg of phenolics and 4.62 mg of anthocyanins per 100 g DW. Similar observations in different _Brassica_ vegetables that blue light has a pronounced effect on flavonoids and phenolic acid synthesis have been made by others [46; 48]. Light is necessary for the biosynthesis of anthocyanins, which of course depends on the quality and intensity of the lighting. Short-wavelength light, such as blue light and UV light, might be more effective at increasing anthocyanin content [12]. The total anthocyanin concentration was enhanced by 75% in red cabbage microgreens in response to an increased proportion of blue light (from 5 to 30%) supplied from LED arrays during cultivation in a controlled environment [19]. Contrary to this, [PERSON] et al. showed that the content of total phenolics in red cabbage microgreens has not been impacted by varying the blue light percentage [19]. The combination of red and blue LEDs increased the concentration of total phenolic content and anthocyanin content in amaranth microgreens as compared to sole red and sole blue treatments [49].
# Towards Environmentally Friendly Accelerometers Based on Bacterial Cellulose
## 2 Materials and Methods  
Figure 2 shows the structure of cellulose. Biocompatibility, mechanical, and electrical properties of cellulose have caused a relevant interest for applications, such as medicine and aerial vehicles [35; 36; 37]. Unfortunately, cellulose is commonly obtained from vegetal stocks with industrial production processes. These require relevant quantities of energy and freshwater. Potentially pollutant wastes are eventually produced.  
BC has been proposed as an interesting substitute for plant-derived cellulose [26]. BC is produced by bacteria of the genera Acetobacter, Sarcina ventriculi, and Agrobacteriumium in suitable solution cultures, where they grow at the interface between the open air and the liquid phase, in a kind of bio industry. Although the BC is characterized by the same chemical structure as plant-derived cellulose, it can be obtained with a higher level of purity. Moreover, BC can be obtained through a greener production process [38].
# A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm
## 4 Experimental Results
### Comparison with Related Algorithms
#### 4.2.2 Analysis and Comparison on the Basis of Descriptive Statistics
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DPD-HABC** & **Primer 5** & **Oligo 7** \\ \hline  & T[AC]G[CT][AC] & [AT][AG][AC][CT][GA][AT]A & [AT][AC][AT]A \\ Forward primer (5\({}^{\prime}\)-3\({}^{\prime}\)) & [AT]G[AT][AC] & [AT][AG][GT][GT][GT][AC] & [AG][AT][AG] \\  & C[CT][GT][GT][GT][GT] & C[CT][GT][GT][GT][TTC] \\  & C[CT][GT][GT][GT] & [GA][CG] & [GT][GT][GT][GG][GG] \\  & CAG[AC][GA] & CA[AG][AC][GA] & CA[AG]CA \\  & C[AT][GT][AC] & C[AT][GT][AC] & C[AT][GT][AGC] \\ Reverse primer (5\({}^{\prime}\)-3\({}^{\prime}\)) & [ACT][GC][GT][TAC] & [AC][ACT][GA][GT][TAC] & [AC][AT][GT][GG][GT] \\  & Al[CT][AG] & A[CT][A][GT][GT] & [AT][CT][GA][GT] \\ Degeneracy (F/R) & 1536/1536 & 926/13824 & 2304/3072 \\ \multicolumn{2}{c}{Converge (F/R)} & 30 & 20 & 26 \\ Primer length (F/R) & 19/19 & 19/19 & 19/19 \\ Melting temperature (F/R) (\({}^{\circ}\)C) & 54/56 & 58/62 & 58/59 \\ Temperature difference (\({}^{\circ}\)C) & 2 & 2 & 2 \\ GC-content (F/R) (\({}^{\circ}\)C) & 50 & 48 & 52 \\ GC-clamp (F) & G & G & CG \\ GC-clamp (F) & C & CG & GC \\ Self-annealing & No & No & No \\ Pair-annealing & No & No & No \\ specificity & Yes & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of three methods on a 600 bps DNA template. The specific experimental data of the DPD-HABC algorithm on two DNA template sequences consisting of 300 bps and 400 bps are shown in Table 5. On the basis of Table 5, it can be seen that when solving template sequences of different sizes, the degeneracy of the DPD-HABC algorithm is also different. With increasing template size, there is a corresponding increase in degeneracy. For example, when solving the 200 bps DNA template sequence, the degeneracy is 256; however, when solving the 300 bps DNA template sequence, the degeneracy is 1536. However, the coverage is not related to the size of the DNA template sequence. For example, when solving four groups of typical DNA template sequences, the coverage is 30. This is related not only to the number of bases in the template, but also to the excellent characteristics of the global search when using the DPD-HABC algorithm.