esa-sceva/satcom-chunk-collection huggingface dataset:
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 4 Analysis and Description of the Proposed Palm Clipping Method
### Palm Clipping Function \(P_{\beta}^{A}\)  
The proposed clipping function based on the cosine hyperbolic for clipping a signal \(x(t)\) is defined as  
\[\overline{x}(t)=\left\{\begin{array}{cc}x(t)&,\\ P_{\beta}^{A}\left(x(t)\right)\ast e^{j\phi(x(t))}&,\end{array}\begin{array}{ c}x(t)\right]\leq A\\ x(t)\end{array}, \tag{11}\]  
where \(A\) is the threshold level, \(\phi\) is the phase of the input signal \(x(t)\), and the function \(P_{\beta}^{A}\left(r\right)\) for a given amplitude \(r\) is expressed by  
\[P_{\beta}^{A}\left(r\right)=\frac{A}{\cosh\left(\frac{r-A}{\beta}\right)}, \tag{12}\]  
where \(\beta\) is the smoothness factor of the curve function.  
The basic idea of the proposed function \(P_{\beta}^{A}\) is to attenuate the signal after a threshold value, as depicted in Figure 7. When \(\beta\) tends to infinity, function \(P_{\beta}^{A}\) tends to the saturation value \(A\) (the same as in Classical Clipping).  
Figure 6: OFDM system transmitter side with Palm Clipping and the High-Power Amplifier (HPA).  
Figure 7: Palm clipping function \(P_{\beta}^{A}(r)\) with \(A=10\) and the variable smoothness parameter \(\beta\).  
Figure 8 presents a graph of Palm Clipping as a function of \(\beta\); this figure regroups the response curves when the input amplitude \(r\) is equal to 10, 11 15, 20, and 25, and the threshold value \(A\) is equal to 10. From the curve presented in this figure, we observe that the response of the function is controlled by beta. When beta tends to infinity, \(P_{\beta}^{A}\) tends to clipping value \(A\), which is the same as in Classical Clipping (CC). For example, when \(r=25\), the curve of function \(P_{\beta}^{A}\) converges slightly to the clipping value \(A\). Moreover, when \(r=11\), we see a convergence toward \(A\); this variation will be exploited with the variation of the constellation. We know that the amplitude increases with the constellation; therefore, the signal is very sensitive to variation. In order to validate our method, we carried out several simulations that are detailed in the following section.
# Signal Consensus in TSP of the Same Grid in Road Network
## 5 Simulation Results  
### Mathematical Model Establishing  
Combined actual situation with graph theory and matrix theory, the topology network of traffic light signal control agents of the intersections belonging to the same grid is shown in Figure 2, the numbered nodes represent the control agents of traffic lights. The states of the agent represent the weight of priority in TSP, the directed edges represent the relationship of information interaction between the agents, and the corresponding Laplacian matrix in Figure 2 is:  
\[\mathbf{L}=\left(\begin{array}{ccccc}2&-1&0&0&-1\\ -1&2&0&-1&0\\ 0&-1&1&0&0\\ -1&0&-1&2&0\\ 0&0&0&-1&1\end{array}\right) \tag{9}\]  
In this paper, we set the initial weight of priority P1 (0) = 15, P2 (0) = 10, P3 (0) = 0, P4 (5) = 0, and P5 (0) = 8, respectively. Traffic light signal agents interact information according to the topology network Figure 2 by using consensus algorithm. Eventually the system will converge and be stable, a consensus is asymptotically reached for all weight of priority in the same grid.  
Figure 1: Distribution of intersections in a traffic grid.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 7 Open Challenges
### Enabling Technologies  
Advances in microelectronic design and manufacture has allowed for miniaturisation of node hardware and at a lower production cost a factor which is useful in large-scale applications and allows for placement of redundant nodes in a network. However, there is need for continuous research in developing increased energy efficiency while keeping these nodes small and at a reduced cost. The energy harvest mechanisms is one such area of development, there is room for developing batteries that support quick and efficient charging at lower currents. A characteristic like this would be useful in harvesting solar energy for example. Integration of these nodes into the SDN architecture will also require further development of software and hardware to enable the SDN functionality and also provide support for multiple retasking, inter-plane data exchange via APIs and communication protocols (Flow-Sensor [128], FlowVisor [143], HyperFlow [93]. etc.). There is also need to develop some form of modularity in both software and hardware to allow for use in multiple applications and enable easy system maintenance. In terms of choice of using FPGA, micro-controller or a hybrid of both in SDN sensor node design, it can be argued that it vastly depends on energy efficiency, cost and nature of application. There are still possibilities of integrating low power FPGAs however the cost impact for application in large-scale networks also needs to be accounted for. Generally, novel hardware and software needs to be developed to improve support for the measurement, analysis and decision making process for IoT applications such as smart water grids while allowing compatibility with the integrated management framework.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Applicability of Damage Evolution Equation
#### 3.5.2. Consideration of Unloading and Water-Softening  
This method of damage variables' calculation is suitable for tunnel excavation in water-rich soft rock without considering time, which means only the final damage results are taken into account. The corresponding equations and change curves of the dual softening damage variables are shown in Equations (36) and (37) and Figure 9.  
Tensile damage:  
\[D_{t}=\begin{cases}0&\overline{t}\leq\epsilon_{t}\\ 1-\frac{\sigma_{t,1}\sigma_{t,2}}{E_{0}2^{2}}&\epsilon_{t}<\overline{\epsilon} <\epsilon_{tu}\\ 1&\epsilon_{tu}\ \leq\overline{\epsilon}\end{cases} \tag{36}\]  
Compression shear damage:  
\[D_{c}=\left\{\begin{array}{ll}(D_{m1}+D_{m2})\frac{\overline{t}^{p}}{ \overline{t}^{p}}-D_{m1}D_{m2}\frac{(\overline{t}^{p})^{2}}{(\overline{t}^{p} )^{2}}&0\leq\overline{t}^{p}<\overline{\epsilon_{t}}^{p}\\ D_{m1}+D_{m2}-D_{m1}D_{m2}&\overline{t}^{p}\geq\overline{\epsilon_{t}}^{p} \end{array}\right. \tag{37}\]  
where, \(\sigma_{t,1}\) and \(\sigma_{t,2}\) are the residual tensile strength of natural and saturated rocks, respectively; \(D_{m1}\) and \(D_{m2}\) are the maximum damage values of rock under unloading softening and water-softening, separately.
###### Abstract  
_Background_: Optical coherence tomography (OCT) is a non-invasive imaging technique based on the interferometry of backscattered light. However, strong light scattering hinders its applicability in clinical dermatology. The strength of scattering is exemplified by the attenuation coefficient which is the rate of OCT signal decay in depth. Attenuation can be reduced by topical application of hyperosmotic liquids with a high refractive index, namely optical clearing agents (OCAs). In this study, we assessed the impact of different OCAs to enhance skin optical permeability in OCT images. In vivo tests were carried out to determine the OCT attenuation coefficient (\(\mu_{\text{OCT}}\)) and epidermal thickness in the treated and untreated epidermis. _Methods_: Four OCAs were studied: Propylenglycol, propylenglycol combined with oleic acid in equal proportions (1:1 \(\
u/\
u\)), Vaseline, and liquid Vaseline. Percentage change of \(\mu_{\text{OCT}}\) and epidermal thickness were estimated by OCT imaging of a healthy forearm skin, prior to the application of each OCA and after the application, at two time points, \(t_{1}=5\) min, and \(t_{2}=90\) min. \(\mu_{\text{OCT}}\) was quantitatively obtained by fitting the OCT signal to a single scattering model. _Results_: The application of OCAs induced significant changes in both \(\mu_{\text{OCT}}\) (decreased) and epidermal thickness (increased). The synergistic effect of the combined propylenglycol with oleic acid reduced the \(\mu_{\text{OCT}}\) by 43% while propylenglycol induced the highest increase (33%) in epidermal thickness, both at \(t_{2}\). _Conclusions_: Topical administration of propylenglycol combined with oleic acid can reduce light attenuation in OCT imaging within the clinically relevant timeframe of 90 min.  
Keywords:optical coherence tomography; tissue clearing: optical clearing agents; skin +
Footnote †: journal: Applied Sciences
## 5 Experiment
### Comparing Models  
To validate and evaluate the performance of the proposed DSTAGCRN method, we employed nine baseline models, including HA, LSTM, ASTGCN, DGCN, MegaCRN, STGCNDE, DCRNN, AGCRN, and DGCRN. Below is a brief explanation of each baseline model:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Zurich** & **Guangzhou** & **Singapore** \\ \hline Start Time & 1 January 2023 & 1 June 2018 & 1 June 2022 \\ End Time & 13 April 2023 & 30 June 2018 & 11 August 2022 \\ Time Interval & 5 min & 5 min & 5 min \\ Timesteps & 29,664 & 8640 & 20,736 \\ Parking Lots Number & 33 & 18 & 122 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description and statistics of the dataset.  
HA [52]: Historical average. It uses past VPS data to calculate the average and predict the next value.  
LSTM [53]: Long short-term memory recurrent neural network model. Due to its memory function, LSTM can build learning models using long sequences of VPS data.  
ASTGCN [29]: Attention-based spatio-temporal graph convolutional network. It utilizes spatial and temporal attention mechanisms to extract temporal and spatial features from the data.  
DGCN [31]: Dynamic graph convolutional network. It proposes a latent network of graph Laplacian matrices to adaptively represent the spatio-temporal connections in traffic data.  
MegCRN [54]: Meta graph convolution recursive network (MegaCRN). It relies solely on observed data to maintain robustness and adapt to any traffic situation, from normal to nonstationary.  
STGNCDE [30]: Spatio-temporal graph neural control differential equation (STGNCDE). The neural control differential equation (NCDE) is a breakthrough concept for processing sequence data, with both time and space NCDEs designed.  
DCRNN [47]: Diffusion convolutional recurrent neural network. It combines the bidirectional random walk of the graph and the recurrent neural network in an encoder-decoder structure.  
AGCRN [39]: Adaptive graph convolutional recursive network. It is a deep learning model based on graph convolution, an adaptive adjacency matrix, and a recurrent neural network. An adaptive learning of node-specific parameters for graph convolution used to explore spatio-temporal correlations in data.  
DGCRN [55]: Dynamic graph convolutional recurrent network. It extracts dynamic features from node attributes to generate a dynamic graph, capturing dynamic features of correlation between positions on the road network.
# Development and Characterization of High-Fiber, Gluten-Free Pasta for Celiac Disease Patients
## 3 Results and Discussion
### Chemical Composition of the Pasta Treatments
#### 3.1.4 Crude Fiber Content  
The percentages of fiber content in control sample 1.24 and pasta treatments were 2.19, 1.83, 2.79, 3.33, 3.95, and 4.68, respectively. The results of the fiber content indicated that there was a significant difference between the control sample and different gluten-free pasta treatments. The crude fiber content of gluten-free pasta treatments was higher than the control sample because of the incorporation of fiber-rich ingredients (lupin and flaxseed flour). The fiber content of flaxseed is about 20-28% of total dietary fiber [24], and lupin flour is about 25-30% fiber [28]. The percentage of crude fiber content in the gluten-free pasta treatments varied significantly (\(p<0.05\)) from each other except for T1 and T2, which were insignificantly different (\(p>0.05\)) from each other. On the other hand, T6 was significantly higher in fiber content compared to other treatments and the control, which had the lowest fiber content.
## 4 Security Analysis of LEACH-Based Secure Routing Protocols and ESPDA
### Security Analysis of LEACH-Based Secure Routing Protocols
#### 4.1.1 Security Analysis of SLEACH  
In SLEACH, nodes are not equipped with secret keys to secure the communication among them. Some main drawbacks have their root in this absence. The following are the observed drawbacks of this protocol:  
* Non-authenticated join request messages allow intruders to join the network freely. Cluster member verification can only be done at the BS level, not at the CHs level. This situation can lead to wasted network resources since any aggregated data containing data from an illegitimate member will be dropped by the BS.
* Intruders can exploit the plain-text non-authenticated schedule messages to disrupt the communication. For example by sending false schedules, data transmission collisions may happen. Therefore schedule messages need authentication and integrity protection.
* In the case of applications where the data are sensitive information, the transmission of plaintext sensor readings is not desirable, and they need confidentiality protection. Another problem with the sensor readings transmission happens also to the aggregated data transmission. The BS cannot afford authentication for aggregated data though it can verify cluster member authentication using the accompanying MAC.
* No key update provisioning for key \(k_{j}\).
* Not knowing the previous blacklisted nodes, the new CHs will include those old intruders in the data gathering process. This may lead to more aggregated data get dropped before the new list of intruders gets issued by the BS.
## 2 Materials and Methods  
### Study Area  
Data from the following projects were used in this study: Ecological Status of Aquatic Systems with the Sentinel Satellites project (ESAQS) and FLEX L3-L4 project--Advanced Products L3 and L4 for the FLEX-S3 mission. Both projects were completed but only the ESAQS data (data until 2018) has been published in various papers [6; 12].  
The main study area was the Xuquer Basin Authority, sampling nine reservoirs and one lake belonging to the Xuquer river basin and other rivers belonging to the administrative basin. For the European project FLEX L3-L4, other water bodies were also sampled: a reservoir belonging to the Segura Basin Authority (La Pedrera), two reservoirs of the Tajo Basin Authority (Cazalegas and Valdecanas), and a hypersaline endorheic lagoon, Petrola. The situation of the 12 reservoirs and two lakes analyzed from several Spanish watersheds is shown in Figure 1.  
The water bodies had total volumes ranging from \(2\times 10^{6}\) m\({}^{3}\) (Petrola) to 1446 \(\times\) 10\({}^{6}\) m\({}^{3}\) (Valdecanas reservoir) and a maximum depth between 1.5 m in the Albufera lake and 129 m in the Contreras reservoir. The climate variability greatly influences the water quality, and the sample sites correspond to three different climate types of Koppen-Geiger classification for the 1981-2010 period (Figure 1), based on precipitation and temperature: Csa (Mediterranean hot summer climate), Csb (Mediterranean warm/cool summer climate), and Bsh (Cold semi-arid climate). Reservoir water levels vary broadly throughout the year and are therefore highly dependent on upstream rainfall and water temperature, meaning that the residence time oscillates from 0.10 years (Albufera) to 5.96 years (Maria Cristina reservoir). In addition, other factors not related to climate, such as altitude, vary from 1 m above sea level in Albufera lake to 890 m above sea level in Petrola. An overview of the sampling sites and their characteristics is given in Appendix A (Table 1).
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 3 Simulation Results and Discussion
### Influence of AR on the Torque Correlation Factor \(K_{t}\)  
For the design and construction of helical anchors, accurately predicting their uplift capacity is crucial. In past research, [PERSON] and [PERSON] [16] summarized empirical Formula (2) based on engineering experiments, which relates the axial load capacity of helical anchors to the final installation torque, with \(K_{t}\) being the torque correlation coefficient. Although there is no consensus among many scholars on the theoretical research results, this empirical formula is widely used in the industry for predicting the uplift capacity of helical anchors [17]. Additionally, according to a large number of field test results, [PERSON] [6]  
Figure 12: Uplift stage force–displacement curves. (**a**) \(p_{w}=0.06\) m. (**b**) \(p_{w}=0.08\) m.  
Figure 13: Results of the influence of AR on normalized tensile capacity.  
points out that the value of \(K_{t}\) is not unique and is related to the shaft diameter d of the helical anchor, and proposed a formula for calculating \(K_{t}\), as shown in Formula (3).  
\[Q_{u}=K_{t}T \tag{2}\]  
\[K_{t}=2.54d^{-0.9198} \tag{3}\]  
In this paper, using Formula (2), the final installation torque \(T\), and the uplift capacity \(Q_{u}\) obtained from Sections 3.2 and 3.3, the \(K_{t}\) values under several \(AR\)s were calculated. These were compared with the \(K_{t}\) values obtained from Formula (3) and the \(K_{t}\) values from [PERSON]'s experiments [18], as shown in Figure 14. It can be observed that the calculated \(K_{t}\) values in this paper range from 8.5 to 17.8 m\({}^{-1}\). The \(K_{t}\) value at \(AR=1.0\) is close to the results of [PERSON]'s research and is lower than [PERSON]'s theoretical calculation value of 25.9 m\({}^{-1}\). Therefore, using [PERSON]'s theoretical \(K_{t}\) value would result in a significant overestimation of the uplift capacity.  
Furthermore, [PERSON]'s theoretical formula indicates that for a helical anchor with a certain shaft diameter, the \(K_{t}\) value is also predetermined and does not consider the impact of the installation method. However, as can be seen from Figure 14, the \(AR\) significantly affects the \(K_{t}\) value. Compared with \(AR=1.0\), the \(K_{t}\) value decreases by 8% at \(AR=0.8\), by 22% at \(AR=1.2\), and by 45% at \(AR=1.5\). Therefore, using the \(K_{t}\) value measured at an \(AR\) of 1.0 to predict the ultimate uplift capacity of the helical anchor under other \(AR\)s could lead to an overestimation, which may adversely affect the engineering project.  
To accurately predict the uplift capacity of the helical anchor, it is recommended to monitor the \(AR\) during the installation and select the appropriate \(K_{t}\) value based on the actual \(AR\) when applying the empirical Formula (2) in actual engineering. This can avoid the prediction errors of the uplift capacity due to the improper selection of the \(K_{t}\) value, thus ensuring the safety of the project.
## 2 Self-Adaptive Differential Evolution Algorithm with Ensemble of Mutation Strategies  
Metaheuristics are one of the most used optimization methods in the domain of architectural design [2]. These algorithms can avoid local minima and maxima while coping with real-parameters in large search spaces. In addition, metaheuristics can present near-optimal results when compared to other direct search methods in a reasonable time [3].  
Swarm intelligence (SI) and evolutionary algorithms (EAs) are the most common sub-categories of metaheuristics. These algorithms are inspired by nature using different search strategies. SI is based on interactions of swarms such as flocks of birds, schools of fish, ants and bees. Some examples of SI can be shown as Ant Colony Optimization (ACO) [9] and Particle Swarm Optimization (PSO) [45; 46]. EAs are in the class of population-based metaheuristic optimization algorithms. EAs are inspired by the mechanisms of biological evolution that mimics the selection and reproduction processes of living  
Figure 3: Optimus development process.  
organisms. EAs are very effective to deal with NP-hard problems. Some examples of EAs are Genetic Algorithms (GAs) [8; 47]. Genetic Programming (GP) [48], Evolution Strategy (ES) [49], and DE [7].  
DE, which is introduced by [PERSON] and [PERSON] [7], is potentially one of the most powerful stochastic real-parameter optimization algorithms in the literature. The algorithm can converge very fast in solving real-world problems such as in scheduling [50], optics [51], communication [6], power systems [52], pattern recognition [53] and recently in architectural design [54; 55]. A recent survey by [PERSON] and [PERSON] [56; 57] clearly explained the history of DE and its success. DE algorithm has many advantages. The simple code structure of the algorithm facilitates its implementation. Other advantage is that the number of control parameters in DE is few, which are crossover rate (\(CR\)), mutation rate (\(MR\)), and population size (\(NP\)). In classical DE, these control parameters are constant during the whole optimization process. However, a simple change in \(MR\) or \(CR\) generation strategies can significantly improve the performance of the algorithm. Therefore, some variants of DE in the literature focus on parameter settings as presented in [42; 58]. Moreover, DE can tackle the large scale and computationally expensive optimization problems. Because, the space complexity of DE is low as mentioned in [59].
## 2 Preliminaries
### Empirical Predictability  
Recently, [PERSON] [13] defined a new complexity measure called _empirical predictability_. If we are asked to guess a symbol chosen uniformly at random without any context given, our best chance is to guess the most frequent symbol. The probability of a correct prediction \(P_{0}(T):=\max_{c\in\Sigma}\frac{n_{c}}{n}\) is called _the empirical predictability of order 0_. Now we consider being asked the same question, but we are allowed to read the preceding \(k\) bits of context or, if there are not enough preceding bits, the exact symbol. We then call the chance to predict correctly the _empirical predictability of order \(k\)_. It is defined as \(P_{k}(T)=\frac{1}{n}\sum_{s\in\Sigma^{k},T^{s}\
eq\epsilon}|T^{s}|\cdot P_{0}( T^{s})\). (Actually, [PERSON] [13] adds a summand of \(\frac{k}{n}\) for the first \(k\) bits of the text. In contrast, we consider \(k\) to be a small constant. Considering \(\lim_{n\rightarrow\infty}\frac{k}{n}=0\) we can omit this summand for both empirical entropy and empirical predictability.) Note that \(1\geq P_{k}(T)\geq\frac{1}{|\Sigma|}\). If \(P_{k}(S)\) is high we intuitively expect a compressed version of the text to be small. This is inverse to the other complexity measures, where we need a low value to get a small compressed text. In fact, we prove:  
**Lemma 1**.: _For binary texts (\(\Sigma=\{0,1\}\)) the empirical entropy \(H_{k}(T)\) is an upper bound for \(1-P_{k}(T)\)._  
Proof.: Let \(k\) be fixed. For \(s\in\Sigma^{*}\) let \(p_{s}:=|T^{s}|\cdot P_{0}(T^{s})\) be the number of correct predictions after reading \(s\) and \(m_{s}:=|T^{s}|-p_{s}\) the number of incorrect predictions after reading \(s\).  
To simplify notation we assume \(|T^{sx}|\
eq 0\)\(\forall s\in\{0,1\}^{k},x\in\{0,1\}\). Then:  
\[H_{k} =\sum_{s\in\{0,1\}^{k}}\frac{|T^{s}|}{n}\sum_{x\in\{0,1\}}\frac{| T^{sx}|}{|T^{s}|}\log\frac{|T^{s}|}{|T^{sx}|}\] \[=\sum_{s\in\{0,1\}^{k}}\quad\quad\left(\frac{|T^{s0}|}{n}\log \frac{|T^{s}|}{|T^{s0}|}\;\;+\;\;\frac{|T^{s1}|}{n}\log\frac{|T^{s}|}{|T^{s1}| }\right)\] \[=\sum_{s\in\{0,1\}^{k}}\quad\quad\frac{m_{s}}{n}\underbrace{ \log\frac{\widehat{[T^{s}]}}{m_{s}}}_{\geq 1}\quad\quad+\;\sum_{s\in\{0,1\}^{k}} \frac{p_{s}}{n}\underbrace{\log\frac{\widehat{[T^{s}]}}{p_{s}}}_{\geq 0}\] \[\geq\sum_{s\in\{0,1\}^{k}}\quad\quad\frac{m_{s}}{n}\] \[=1-P_{k},\]  
which proves the claim.  
Note that we omitted some significant terms; the actual \(1-P_{k}\) should be even lower. This relation between the empirical entropy and the empirical predictability had not been established before. Lemma 1 will be used in Section 4, since there the test data generator mimics the process of empirical predictability; but we think that the lemma could also have different applications in the future.