esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The \(\epsilon\)BBO Algorithm for SSD Model
### Main Procedure of \(\varepsilon\)BBO for SSD Model  
By incorporating the above-mentioned migration operator and mutation operator into Biogeography-based optimization, the inverse operation is performed after the mutation operator; in addition, modification probability and mutation probability are modified in term of Equations (26) and (27) respectively. Meanwhile, immigration rate and emigration rate based on cosine curve are modified. The \(\varepsilon\)BBO algorithm is able to explore the new search space with the mutation operator of DE algorithm and to exploit the population information with the migration operator of BBO algorithm. This feature overcomes the lack of exploration of the original BBO algorithm.  
The procedure chat of the \(\varepsilon\)BBO algorithm is shown in Figure 1.  
The \(\varepsilon\)BBO approach is described in Algorithm 1 and SortPopulation (Population \(P\)) is described in Algorithm 2.  
Figure 1: The procedure chat of the \(\varepsilon\)BBO algorithm.  
```
1:modification probability, pModify; mutation probability, pMutate; elitism parameter, keep; number of iterations
2:the best portfolio and the return of the portfolio
3:\(\textbf{P}\leftarrow\text{generate Initial Random Population()}\)
4:evaluateFitness(P)
5:\(Generation_{current}\gets 0\)
6:while halting criterion not being satisfied do
7:\(Generation_{current}\gets Generation_{current}+1\)
8:\(\textbf{Elite}\leftarrow\textbf{P}\)(1:keep)
9:\([\lambda_{i},\mu_{i}]\leftarrow\text{getLambdaMu(P)}\)
10:\(\textbf{P}\leftarrow\text{migrationDo(P,pModify)}\)
11:\(\textbf{P}\leftarrow\text{mutationDo(P,pMutate)}\)
12:evaluateFitness(P)
13:sortPopulation(P)
14:\(\textbf{P}\leftarrow\text{replaceWorst(P, Elite)}\)
15:\(\textbf{P}\leftarrow\text{clearDuplicates(P)}\)
16:[maximum cost, minimum cost, average cost] \(\leftarrow\) evaluateCostItems()
17:[pModify,pMutate] \(\leftarrow\) updateProbability()
18:endwhile
```  
**Algorithm 1** the main procedure of \(\varepsilon\)BBO for the SSD.  
```
1:\(Pun\leftarrow\text{Punish(}P\))
2:sortPun(\(Pun,P\))
3:\(\varepsilon_{0}\gets Pun_{(1/5)NP}\)
4:\(\varepsilon\leftarrow\varepsilon_{0}\times(\frac{Generation_{current}}{Generation_{max}})^{cp}\)
5:for\(i=1\) to NP do
6:for\(j=1\) to NP-1 do
7:if\((Pun_{j},Pun_{j+1}\leq\varepsilon)\) or \((Pun_{j}=Pun_{j+1})\) and \((f_{j+1}<f_{j})\) then
8:swap \((P_{j},P_{j+1})\)
9:elseif\((Pun_{j}>Pun_{j+1})\)then
10:swap \((P_{j},P_{j+1})\)
11:endif
12:endfor
13:if no swap then
14:break
15:endif
16:endfor
```  
**Algorithm 2** the main algorithm of sortPopulation (Population \(P\)).
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 4 Sender Authentication
### Strengthening Authentication Scheme  
#### 4.9.1 Obligation to Raise an Alarm  
By choosing \(\mathcal{SF}\) in such a way that the protection described in Remark 2 is provided, it is possible to extend the key distribution scheme using an alarm protocol. Let \(C\) be the set of all captured nodes (out of the receiver set \(\mathcal{R}\)) and let \(\mathcal{SF}=\{F_{1},F_{2},\ldots,F_{\ell}\}\). Define  
\[\mathcal{I}=\bigcup_{\begin{subarray}{c}1\leq i\leq\ell\\ V_{k\in C}\in F_{i}\end{subarray}}\{i\}=\{1,2,\ldots,\ell\}\setminus\bigcup_{ k\in C}\bigcup_{\begin{subarray}{c}1\leq j\leq\ell\\ k\in F_{j}\end{subarray}}\{j\}\,,\]  
that is, \(\mathcal{I}\) includes all indices of the sets \(F_{i}\) to which none of the compromised nodes belongs. Assume \(\mathcal{I}\) is nonempty. The adversary has no access to the keys \(K_{i}\) for \(i\in\mathcal{I}\); hence, for each forged message \(m^{\prime}\), the adversary must guess all tags \(t_{i}\) for \(i\in\mathcal{I}\). The guess for a single \(t_{i}\) is correct with probability \(2^{-u_{i}}\). If the pairs \"correct \(T_{2}\), wrong \(t_{i}\)\" are observed by the receivers in a sequence of packets, then they should be obligated to inform \([PERSON]\) about the phenomenon via a one-to-one channel. Each complaining receiver should provide:  
* The messages in question;
* The identifier of the group \(\mathcal{R}\) together with the index of the wrong \(t_{i}\);
* The observed values of the chunks \(t_{i}\);
* The correct values of the chunks \(t_{i}\).  
The adversary is then faced with the following alternatives:  
* They can also complain to \(S\) about the wrong \(t_{i}\) in order to not be blamed for being the author of the messages \(m^{\prime}\); however, the adversary then informs the sender about their own malicious activity. Moreover, the adversary should provide to \(S\) with the correct values of the chunks \(t_{i}\). If the guesses are wrong, then the adversary automatically points to themselves as the author of the suspicious messages.
* They can refrain from complaining to \(S\) about the wrong \(t_{i}\)s; however, the alleged sender will gradually obtain information from other nodes, narrowing the set of suspected nodes. In this case, the adversary not sending a complaint will confirm the suspicions.
###### Abstract  
This paper presents a new energy-harvesting system with two identical piezoelectric energy harvesters in a tandem configuration. Each harvester consists of a piezoelectric beam and a circular cylinder. Experiments are performed to investigate the energy-harvesting performances of this system in water. It can be found that their energy-harvesting performances are all different from that of the single harvester (without an upstream or downstream harvester). The experimental results show that the water speed and the spacing ratio have significant effects on the energy-harvesting performances of the two tandem harvesters. The output power of the upstream harvester first increases, and then decreases with the water speed increasing. The maximum output power of 167.8 uW is achieved at the water speed of 0.306 m/s and the spacing ratio (\(L/D\)) of 2.5. Increasing the water speed results in an increase in the energy performance of the downstream harvester. Compared with the single harvester, the performance of the downstream harvester is weakened in the low water speed range, but enhanced in the higher water speed range. Further, the output power of 533 uW is obtained by the downstream harvester at the water speed of 0.412 m/s and the spacing ratio of 1.7, which is 29 times more than that of the single harvester. The results indicate the superiority of the two tandem harvesters in energy-harvesting performance.  
piezoelectric energy harvester; tandem configuration; water flow +
Footnote †: journal: Applied Physics Letters
## 4 Discussion  
### Discussion on the Load Bridging Effects and Trends by Each Significant Factor  
#### 4.1.1 Effect of Number of Columns on the Bending of a Board  
Although the Gaussian process models were formulated independently for unit loads with different numbers of columns, the results can be grouped together to identify relevant trends in the data. Previous researchers have identified that the size of the boxes has a very significant influence on load bridging levels generated [12; 16]. In general, it is commonly assumed that smaller boxes generate higher bending. This hypothesis is based on the application of the discrete load on the pallet deck. Infinitely smaller boxes will resemble a uniformly distributed load and, therefore, generate the same bending as uniform load applicators. Figure 9 shows that same overall trend for most scenarios studied. It is of high relevance to note that when boxes are longer along the length (three columns), the payload is relatively short (700 mm) and the box-to-pallet CoF is low (0.30), this observed trend is no longer valid. In this scenario, the unit load with three columns of boxes presented a deflection ratio of 0.65, while that for four columns was 0.43, five columns was 0.59, and six columns was 0.64. Unit loads with these characteristics present a higher deflection than those with smaller boxes, almost matching the same deflection level as a unit load with six columns. The interaction of the box length dimension with low friction properties generated higher deflection than one driven mostly by box size. As observable in Figure 10, the rotational movement of the boxes when the board was under load was different for the unit load of three columns (Figure 10a) than for those with a higher number of columns. The same change in the movement of the boxes was generated by maintaining the number of columns but either increasing the friction coefficient of the pallet (Figure 10b) or the overall height of the payload (Figure 10c). This confirmed that no generalization can be made regarding the effect of box size on load bridging without considering the remaining interacting factors as well. A key aspect in reducing board deflection was to prevent the rotating movement of the packages. This was possible through many different combinations of factors. Because of this, no specific attempts to quantify a threshold point were made, avoiding misinterpretation of the model results.  
The effect of the pallet friction levels can be observed for a commonly used unit load scenario. The unit load is supported on a 1219 mm \(\times\) 1016 mm wooden pallet (0.45 pallet top deck friction) and contains corrugated boxes made of kraft paper (average box friction of 0.45) that are stacked to a total height of 1200 mm. Everything else remaining equal, increasing the box length of such a unit load from 169 mm to 338 mm reduces the experienced initial deflection by 60%, from a ratio of 0.64 to 0.25. Taking into account the relationship between box size and pallet performance can influence the design process of tertiary packaging.
## 3 Single Channel Modulated Wideband converter
### Spectrum Analysis  
At first, we introduce a definition \(g_{T}(t)=\left\{\begin{array}{ll}1,&|t|\leq T/2\\ 0,&|t|>T/2\end{array}\right..\) Then, the input signal \(x(t)\) in \(\textbf{{T}}_{i}\) can be expressed as  
\[x_{i}(t)=x(t)g_{T_{p}}(t-t_{i}),\quad t_{i}=(i-1)T_{p}+T_{p}/2+nmT_{p} \tag{3}\]  
And the random sequence \(p_{i}(t)\) in \(\textbf{{T}}_{i}\)  
\[p_{i}(t)=a_{ik},\quad kT_{p}/M\leq t\leq(k+1)T_{p}/M \tag{4}\]  
with \(a_{ik}\left\{+1,-1\right\}\), \(1\leq i\leq m\), \(0\leq k\leq M-1\). And \(M\) is a prime number representing the number of intervals in each \(p_{i}(t)\). For convenience of explanation, we present a equivalent structure in Figure 3.  
Figure 2: The Schematic Diagram of a single channel modulated wideband converter (SCMWC) sampling scheme (**a**). Periodic Waveform and low-pass filter (LPF) Frequency Response (**b**).  
Since \(p_{i}(t)\) is a periodic function, the Fourier expansion can be expressed as  
\[p_{i}(t)=\sum_{l=-\infty}^{\infty}c_{il}e^{j2\pi lf_{p}t} \tag{5}\]  
where \(c_{il}=\frac{1}{T_{p}}\int_{0}^{T_{p}}p_{i}(t)e^{-j2\pi lf_{p}t}dt\). Then, after mixing, the Fourier transform of the \(i\)th output signal \(\tilde{x}_{i}(t)=x_{i}(t)p_{i}(t)\) is  
\[\tilde{X}_{i}(f)=\int_{-\infty}^{\infty}x_{i}(t)(\sum_{l=-\infty}^{\infty}c_{ il}e^{j2\pi lf_{p}t})e^{-j2\pi lf_{l}t}dt=\sum_{l=-\infty}^{\infty}c_{il}X_{i}( f-lf_{p}) \tag{6}\]  
where \(X_{i}(f)\) is the Fourier transform of the \(i\)th time interval of \(\mathbf{x}(t)\). We can see that before LPF filtering process, it is a linear combination of \(f_{p}\)-shifted copies of \(X_{i}(f)\).  
Due to the low pass filter, only frequencies in the interval \(F_{s}=[-f_{s}/2,f_{s}/2]\) can appear in the final output sequence \(y_{i}\left[n\right]\). So the discrete-time Fourier transform (DTFT) of \(y_{i}\left[n\right]\) is  
\[Y_{i}(e^{j2\pi fT_{s}})=\sum_{l=-\infty}^{\infty}y_{i}[n]e^{-j2\pi fnT_{s}}= \sum_{l=-L_{0}}^{L_{0}}c_{il}X_{i}(f-lf_{p}),\ f\in F_{s} \tag{7}\]  
where \(L_{0}=\left\lceil\frac{f_{\text{raw}}+f_{s}}{2f_{p}}\right\rceil-1\), and let \(L=2L_{0}+1\), which represents the number of spectrum bands we divided.  
Now, we can write Equation (7) in matrix form as  
\[\boldsymbol{y}(f)=\boldsymbol{A}\boldsymbol{z}(f),\ f\in F_{s}\, \tag{8}\]  
with \(\boldsymbol{y}(f)=diag\left\{Y_{i}(e^{j2\pi fT_{s}})\right\}\), \(\boldsymbol{A}=diag\left\{C_{i}\right\}\), \(C_{i}=\left[\begin{array}{cccc}c_{i_{l}-L_{0}}&c_{i,-L_{0}+1}&\cdots&c_{i,L _{0}}\end{array}\right]\), \(\boldsymbol{z}(f)=diag\left\{z_{i}\right\}\), \(z_{i}(f)=\left[X_{i}(f+L_{0}f_{p})\ X_{i}(f+(L_{0}-1)f_{p})\cdots X_{i}(f-L_{ 0}f_{p})\right]^{T}\), \(i=1,\ldots m\), and \((\cdot)^{T}\) denotes transpose operation.
## 1 Introduction
Recently, we realized efficient frequency down-conversion of a low-power fiber source, based on the intracavity DFG process inside the periodically poled lithium niobate (PPLN) OPO. This method adopted a high-power assisted fiber laser to build a parametric oscillation and creatively took full advantage of the high-power intracavity resonant signal beam. For low-power fiber sources with different wavelengths, the signal wavelength was properly adjusted by changing the PPLN temperature or grating period to ensure that the DFG process between the pump source and signal beam satisfied the phase-matching conditions. In 2019, we reported this new scheme to achieve frequency conversion of the low-power source [14]. The pump source consisted of a 1018 nm fiber laser and a 1080 nm fiber laser. Under 3 and 28 W of 1018 and 1080 nm simultaneously pumping and 40 and 2.4 W of 1018 and 1080 nm simultaneously pumping, respectively, intracavity DFG between the low-power pump beam and high-power resonant signal beam was achieved in both cases, thus verifying the scheme. Based on the research results we have achieved, in this paper, we demonstrate the highly efficient frequency down-conversion of a low-power SF fiber source using phase-matched intracavity DFG. The SF pump source was fixed at 1064 nm and the maximum power was 340 mW. A high-power 1018 nm fiber source was used as the assisted laser to build a parametric oscillation and generate the intracavity signal beam. The PPLN temperature was also properly adjusted to ensure that the phase-matching conditions were met in the DFG process. At last, the 1064 nm SF pump beam was successfully converted to the 3.7 \(\upmu\)m mid-infrared radiation. When the PPLN temperature was set at 20 \({}^{\circ}\)C, the maximum idler power reached 73.5 mW and the maximum pump-to-idler conversion efficiency reached 21.6%. The linewidth of 3.7 \(\upmu\)m idler beam was as narrow as 271 pm (5.9 GHz) without adding any extra devices in the cavity, which also meant that the characteristics of the pump beam were successfully transferred to the idler beam. The experiment results prove that this method has great potential in the frequency down-conversion of low-power special fiber sources like frequency-swept lasers and generating special mid-infrared sources.
# Superhydrophobic Polypyrrole-Coated Cigarette Filters for Effective Oil/Water Separation
## 3 Results and Discussion
### Surface Morphology and Characterization of Superhydrophobic Cigarette Filters
The S2p XPS spectrum of the Ppy particles modified by dodecanethiol is shown in Figure 3e. It could be fitted to three major peaks. The peak located at 162.8 eV was attributed to C-S bonds between sulfur of n-dodecanethiol and Ppy nanoparticles [35]. The peak located at 163.3 eV was attributed to unbound thiols [36]. The S2p peak at 164.4 eV was attributed to S-R, which gives Ppy particles hydrophobicity [37]. The C1s spectrum of Ppy particles modified by dodecanethiol is shown in Figure 3f. It could be fitted to five major peaks. The peaks located at 283.8 eV, 284.8 eV, 285.2 eV, 286.3 eV, and 297.5 eV were attributed to C-H, C-C, C-S, C=C, and C-N, respectively [38]. The marked appearance of the C-S peak component and the appearance of the S2p signal peak indicated that dodecanethiol was successfully grafted onto the Ppy filter fiber surface. Figure 3: XPS survey spectra of Ppy particles before and after modification with n-dodecanethiol: (**a**) Wide survey spectra; (**b**) C1s spectra of the original cigarette filter; (**c**) N1s spectra of the original cigarette filter; (**d**) O1s spectra of the original cigarette filter; (**e**) S2p spectra of the n-dodecanethiol-modified Ppy-coated cigarette filter, and (**f**) C1s spectra of the n-dodecanethiol-modified Ppy-coated cigarette filter.
## 2 Method
The relative equivalent parameters of the ss1 cell can be determined by comparing its equivalent parameters with those of water, and these obtained parameters are presented in Figure 3a. The baseline for the curve is set based on the equivalent parameter of the ss1 cell, where both the relative equivalent density and sound velocity are equal to 1, thus exhibiting analogous properties to water. Previous studies have demonstrated that the number, length, and thickness of skeleton edges in a unit cell can influence its equivalent parameters. However, it has been discovered that incorporating a \"re\" structure commonly utilized in metamaterial design also plays a significant role in determining the cell's equivalent parameters. Therefore, this paper introduces a novel configuration, depicted in Figure 1b, that incorporates the \"re\" form into the skeleton design of underwater metafluid cells. Figure 3: Influence of the “re” factor on the equivalent parameters of cell and vibration modes at A and B points on the two cell band curves. (**a**) As the “re” factor e increases, the black dotted line representing the relative equivalent density of the cell gradually rises, while the red dotted line representing the relative equivalent sound velocity gradually decreases. The green arrow indicates e = 0.086, its shape is shown in (**c**). (**b**) The vibration mode at point A in the ss1 cell band curve occurs when the cell vibrates in the direction of \(\Gamma\)M and experiences skeletal contractions and expansions. (**c**) The vibration mode at point B in the band curve of a “re” cell occurs when e = 0.086. Figure 2: The band curves of cells with different honeycomb lattice cells. (**a**) The band curve of an ss1 cell is depicted. Different color curves represent different characteristic frequencies. The red dotted line represents two band curves originating from the Brillouin region \(\Gamma\) point. Point A, indicated by the black arrow, signifies that point 1/16 \(\Gamma\)M has been selected to analyze the vibration mode of this band. (**b**) The band curve of a cell exhibiting a specific “re” feature is illustrated. The black dashed lines in the figure represent two band curves starting from the Brillouin region \(\Gamma\) point. Similarly to (**a**), point B has been chosen accordingly. Additionally, for comparison purposes, the red dotted line from (**a**) has been translated into (**b**).
# Modulus Stretch-Based Circular SAR Imaging with Contour Thinning
## 3 Experimental Results  
### Simulated Data Imaging  
The simulation of CSAR data generation and imaging was carried out by programming on the MATLAB platform. The simulation radar had a carrier frequency of 10 GHz, a bandwidth of 600 MHz, a slant distance of 10 km from the ground scene origin, a height angle of 30 degrees, a frequency sampling of 128 points, a steering angle step of 0.1 degrees, a total of one cycle of rotation and a circumference sampling of 3600 points. The length of the imaging area was 20 m, and the imaging resolution was \(200\times 200\) pixels. All targets are assumed to be plane targets with zero height.
## 5 Conclusions and Future Work  
The interaction between a Sender and a Receiver that have to communicate over a bidirectional line can improve the transmission process if both Sender and Receiver share previous knowledge about the data they want to communicate.  
By doing this in the fundamental source coding theorem we can substitute entropy with conditional entropy and therefore we have a new theoretical limit that allows for better compression. In this paper we have reviewed recent work that applies interactive approaches to data compression and discussed this possibility. Future work will focus on the improvement of the algorithms presented and on a wider testing of the approaches described.  
There are points of contacts between part of the work described in this paper and the transfer learning techniques studied in machine learning and data mining. Future work might also include a wider analysis of this relationship between the techniques that are described in this paper to improve the interaction between the Sender and the Receiver and the transfer learning techniques.
## 4 Numerical and Experimental Results  
Before developing the numerical and experimental results, we shall give a description of the different geometrical structures considered in this work. As mentioned above the periodic and Fibonacci structures considered here are constituted from two blocks A and B. The block \(A\) is made of an horizontal waveguide and a vertical stub of the same length (i.e., \(d_{1}=d_{2}=1\) m) (Figure 1a). The shape of the block \(B\) is made of the same horizontal segment as for the block A (i.e., \(d_{1}=1\) m), whereas the vertical stub is characterized by a different length \(d_{3}=d_{2}/2=0.5\) m (Figure 1b). Therefore, one obtains the Fibonacci sequences \(S_{3}\) (ABA), \(S_{4}\) (ABAAB) and \(S_{5}\) (ABAABABA)... sketched in Figure 1c-e respectively. For the sake of simplicity, the horizontal segments separating the stubs are chosen similar in such a way that only the stubs follow the Fibonacci rule. However, the theoretical results developed here can be applied also to any lengths of the waveguides constituting the blocks \(A\) and \(B\). The particular case of periodic structures can be either obtained from only A blocks or only B blocks. All the waveguides are made of the same standard coaxial cables. The boundary conditions at the ends of the stubs are chosen such that \(E=0\) (short-circuited).
# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
###### Abstract  
The global population's rapid growth necessitates a 70% increase in agricultural production, posing challenges exacerbated by weed infestation and herbicide drawbacks. To address this, machine learning (ML) models, particularly convolutional neural networks (CNNs), are employed in precision agriculture (PA) for weed detection. This study focuses on testing CNN architectures for image classification tasks using the PyTorch framework, emphasizing hyperparameter optimization. Four groups of experiments were carried out: the first one trained all the PyTorch architectures, followed by the creation of a baseline, the evaluation of a new and extended dataset in the best models, and finally, the test phase was conducted using a web application developed for this purpose. Of 80 CNN sub-architectures tested, the MaxVit, ShuffleNet, and EfficientNet models stand out, achieving a maximum accuracy of 96.0%, 99.3%, and 99.3%, respectively, for the first test phase of PyTorch classification architectures. In addition, EfficientNet_B1 and EfficientNet_B5 stood out compared to all other models. During experiment 3, with a new dataset, both models achieved a high accuracy of 95.13% and 94.83%, respectively. Furthermore, in experiment 4, both EfficientNet_B1 and EfficientNet_B5 achieved a maximum accuracy of 96.15%, the highest one. ML models can help to automate crop problem detection, promote organic farming, optimize resource use, aid precision farming, reduce waste, boost efficiency, and contribute to a greener, sustainable agricultural future.  
agriculture; ML algorithms; CNN; flora classification; precision agriculture +
Footnote †: journal:  
00 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).