esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Hybrid Elementary Cellular Automata and Its Application in a Stream Cipher
## 3 HECA with Dynamic Mask and Its Properties  
### Introduction of the Novel HECA with Dynamic Mask  
The proposed HECA with dynamic mask (HECA-M) consists of the driving and mask parts. The driving part is based on the HECA, and the mask part is essentially an ECA with global chaotic rules. The rule of cell \(i\)th in the driving part is determined by the status value of cell \(i\)th in the mask part. Furthermore, the cell rule in the driving part is changed with the iteration of the mask part, and this process is dynamic. The status values of the driving part are the output of the HECA-M. The structure of the HECA-M is shown in Figure 3.  
In Figure 3, the mask is iterated under the cell rule \(rm\), which is a constant. The cell rule \(rd\) in the driving part is changed with the status value of the cell in the mask. As for cell \(C_{i}\) in the driving part, the index \(d\) of the cell rule is determined by the status value of cell \(C_{i}\) in the mask. Furthermore, the mathematical expression of the iterative process in the driving part is shown in Equation (3).  
\[x_{i}^{t+1}=(f_{r1}(x_{i-1}^{t},x_{i}^{t},x_{i+1}^{t})\bullet f_{rm}(y_{i-1}^{ t},y_{i}^{t},y_{i+1}^{t}))\oplus(f_{r0}(x_{i-1}^{t},x_{i}^{t},x_{i+1}^{t}) \bullet\overline{f_{rm}(y_{i-1}^{t},y_{i}^{t},y_{i+1}^{t})}), \tag{3}\]  
where \(x_{i}^{t}\) (\(y_{i}^{t}\)) indicates the status value of cell \(C_{i}\) in the driving (mask) part at \(t\) iteration. \(i\) (\(i=1,\,2,\,3,\,\ldots,\,l\)) and \(t\) (\(t=1,\,2,\,3,\,\ldots,\,n\)) denote space and time dimension. \(r1\) and \(r0\) are the  
Figure 1: The iterative results of ECAs with rules (**a**) _No._ 126 and (**b**) _No._ 129. The black (white) lattice represents the cell with the status value “\(1\)” (“\(0\)”).  
Figure 2: The structure of a HECA. The yellow (blue) lattices denote the cells under global chaotic rule \(r1(r2)\) and \(r1\
eq r2\).  
cell rules of HECA in the driving part, and \(rm\) is the cell rule of the ECA in the mask part. Moreover, the boundary of these two parts is periodic. \(f_{r}\) denotes the transition function under the cell rule \(r\). \(\oplus\) and \(\bullet\) denote operation XOR and AND, respectively. \(\overline{x}\) represents the inverse of \(x\).  
Abstractly, the longest periods of the driving and mask part are both related to the number of cells \(l\) in each other. The theoretically longest period of the HECA or ECA reaches \(2^{l}\). Thus, the longest period of HECA-M can reach \(2^{2l}\) in theory, which is \(2^{l}\) times as long as the HECA's or ECA's. In conclusion, the period is further prolonged in the HECA-M.
## 4 C-Monoa
### Main Steps of C-Monoa
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\mathbf{Sup_{mean}}\) & \(\mathbf{Conf_{mean}}\) & \(\mathbf{Kulc_{mean}}\) & \(\mathbf{Comp_{mean}}\) \\ \hline \multicolumn{5}{c}{Cardiovascular Disease} \\ MOPSO [32] & 0.089 & 0.294 & 0.327 & 0.215 \\ MOBMSASA [11] & 0.053 & 0.337 & 0.358 & 0.264 \\ MOWOA [33] & 0.041 & 0.195 & 0.215 & 0.183 \\ MOFA [34] & 0.075 & 0.243 & 0.223 & 0.178 \\ MONOA [18] & 0.226 & 0.276 & 0.144 & 0.419 \\ C-MONOA & 0.257 & 0.428 & 0.237 & 0.452 \\ \hline \multicolumn{5}{c}{Heart Disease} \\ MOPSO [32] & 0.046 & 0.354 & 0.193 & 0.187 \\ MOBMSASA [11] & 0.024 & 0.078 & 0.186 & 0.163 \\ MOWOA [33] & 0.038 & 0.091 & 0.088 & 0.156 \\ MOFA [34] & 0.061 & 0.332 & 0.328 & 0.144 \\ MONOA [18] & 0.137 & 0.313 & 0.116 & 0.349 \\ C-MONOA & 0.229 & 0.382 & 0.194 & 0.371 \\ \hline \multicolumn{5}{c}{Wholesale Customers} \\ MOPSO [32] & 0.044 & 0.184 & 0.177 & 0.274 \\ MOBMSASA [11] & 0.037 & 0.156 & 0.261 & 0.312 \\ MOWOA [33] & 0.126 & 0.339 & 0.331 & 0.265 \\ MOFA [34] & 0.154 & 0.385 & 0.316 & 0.237 \\ MONOA [18] & 0.161 & 0.334 & 0.127 & 0.513 \\ C-MONOA & 0.342 & 0.414 & 0.238 & 0.557 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average results of multiple runs of C-MONOA and other evolutionary algorithms on three datasets. ## 6 Conclusions

In the field of mining numerical association rules, dealing with the continuity and complexity of numerical attributes is an important challenge. The existing heuristic algorithms for mining numerical association rules rarely involve discrete attributes and are easily affected by local optima, resulting in poor population distribution and convergence. To address these problems, this paper proposes C-MONOA. This algorithm considers both continuous and discrete classification attributes during rule encoding, aiming to effectively handle mixed datasets of continuous and discrete types and mine valuable association rules. C-MONOA significantly improves the uniformity of population initialization and global searchability by combining it with a cubic chaotic map, avoiding the shortcomings of traditional methods that are prone to becoming stuck in local optima. The method in this article adopts support, confidence, the Kul metric, and comprehensibility as evaluation indicators for multi-objective optimization, successfully balancing the frequency, accuracy, and comprehensibility of rules. Algorithms can automatically process interval optimization of continuous data and value assignment of discrete data without the need for pre-discretization steps, making them suitable for various mixed datasets.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 1 Introduction
### Current Overview of Colombian Population and Agricultural Production  
Colombia's population is undergoing significant demographic changes, with projections estimating around 55.99 million people by 2035, followed by a negative growth trend starting from 2051. These shifts have important implications for resource planning and food security. Despite these negative projections, Colombia still has a considerably large population that demands further efforts to expand its food production matrix [12; 13]. In this context, according to the Ministry of Agriculture, the agricultural sector contributes considerably to social stability and the economy in rural areas, accounting for between 6% and 8% of GDP and providing almost 15% of national employment in Colombia [14; 15]. In January 2024, the agricultural sector demonstrated its significance by generating 244,000 additional jobs, increasing employment in areas such as agriculture, livestock, hunting, and forestry.  
One of the key challenges for Colombian agricultural production is to maintain and increase productivity to meet both domestic demand and export opportunities [16]. In the first half of 2022, this sector managed to export USD 6.116 billion, representing a 38.8% increase compared to the previous year [15]. This type of export progress reflects greater crop diversification and the adoption of advanced technologies such as precision agriculture and drone usage to boost productivity [7]. A combination of demographic, economic, and technological factors has positioned Colombia's agricultural sector as a central driver of food security, rural development, and access to international markets--particularly in countries like the United States, the Netherlands, Belgium, and Germany [17].
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 2 FSO Communication Channel Effects and Their Mitigation Techniques
### Improvement of Signal Detection by Filtering Method  
For signal detection in the Rx of FSO, PiN and an avalanche photodiode (APD) are widely used as the photodetector [75]. Those two characteristics of PD are determined by quantum efficiency and responsivity. Under turbulence effects, PDs produce greater shot and thermal noise that are initiated by scintillation, background noise, and spatial noise modulation, as illustrated in Figure 13.  
Figure 9: Spatial filtering using pinhole installed before the PD.  
Since the output of the PD is an electrical signal, there is an opportunity to implement an electronics filter before the signal is forwarded into a destination network. Thus, the role of the electronics filter related to this issue is giving the bandwidth a limited electrical signal so that it minimizes unwanted spectral components, as shown in Figure 14.  
Figure 11: The OSF composed of a cone reflector, pinhole, and MMF as the optical filter to improve the FD method.  
Figure 12: The sample of the signal spectral measurement at 1555.2 nm from the output of DD and the OSF. (**a**) The received signal for DD without implementing an OSF; and (**b**) the signal spectral measurement produced by the OSF. Both are under a strong turbulence condition.  
Figure 10: The cone reflector and pinhole as the optical spatial filter to improve the DD method.  
Applying a suitable filter can limit the frequency around the center frequency of a signal. In Ref. [76], a scheme of low-pass filtering (LPF) was successfully implemented to improve the performance of optical relaying networks in FSO. The type of LPF's that were implemented in the output of PD were tested for PiN and APD as well as for the data modulation formats of non-return to zero (NRZ) and return to zero (RZ). The results of the simulation in [76] show that the LPFs are successful in improving the performance of BER where the scale level can be reduced in the range of \(10^{-1}\)-\(10^{-3}\). In addition, the different format modulation for a signal provides a significant impact as well; RZ tends to comply with LPF better than NRZ does. It can be analyzed that RZ has half as long a cycle (short-pulse) of pulse modulation as in NRZ. It gives benefits for the noise modulation around the center frequency of a signal, where RZ has a smaller minimum noise modulation around the center frequency than in NRZ. For this reason, the results of the simulation in [76] show that the performance of SNR and BER in RZ is better than NRZ.
## 3 Bi-Level Optimal Model of a Distribution Network Comprising a Micro Energy Grid
### Optimal Micro-Energy-Grid Model  
#### 3.2.1 Objective Function  
The power-supply equipment of the lower micro energy grid mainly included PV power, wind power, microturbines, gas-fired boilers, heat-recovery boilers, lithium-bromide absorption-type refrigerators, battery storage, heating and cooling storage tanks, and air-source heat pumps. The lower model used the minimum integrated operating cost for the micro energy grid as the objective function. The integrated operating costs of the micro energy grid included the cost of equipment maintenance, the cost of the exchanged electrical power between the distribution network and the micro energy grid, the cost of environmental pollution, the cost of fuel, and the cost of investment depreciation in a dispatching cycle. The independent variables of the function were the power of each micro-source, and the exchanged power between the micro energy grid and the distribution network. In the micro energy grid, the cost of line maintenance was set to a fixed cost, and was not reflected in the objective function. The objective function is shown below.  
\[\begin{array}{c}\min F_{\mathrm{S}}(t)=\min\limits_{t=1}^{24}\left[\frac{Z_{ 1}(t)+Z_{2}(t)}{2}\left|P_{\mathrm{exgrid}}(t)\right|+\frac{Z_{1}(t)-Z_{2}(t)} {2}\left|P_{\mathrm{exgrid}}(t)\right|+\\ \sum\limits_{x=1}^{n}\left(C_{x}(t)+E_{x}\frac{I(1+t)^{A_{x}}}{(1+t)^{A_{x}}-1 }\right)P_{x}(t)+\sum\limits_{x=1}^{n}C_{\mathrm{fuelx}}P_{x}(t)+\sum\limits _{x=1}^{n}\sum\limits_{y=1}^{m}\left(\left(C_{y}+D_{y}\right)B_{xy}P_{x}(t) \right)\right)]\,\end{array} \tag{10}\]  
where \(C_{x}(t)\) is the cost of equipment maintenance of micro-source \(x\); \(C_{\mathrm{fuelx}}\) is the cost of fuel of micro-source \(x\); \(E_{x}\) is the cost of initial fixed investment of micro-source \(x\); \(l\) is the interest rate; \(A_{x}\) is the lifespan of micro-source \(x\); \(P_{x}(t)\) is the electrical power of micro-source \(x\); \(C_{y}\) is the penalty of pollutant gas \(y\); \(D_{y}\) is the environmental value of pollutant gas \(y\); and \(B_{xy}\) is the value of pollutant gas \(y\), which is  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale** & **Meaning** \\ \hline
1 & Comparing two factors, they are equally important. \\
3 & Comparing two factors, one is slightly more important than the other. \\
5 & Comparing two factors, one is more important than the other. \\
7 & Comparing two factors, one is significantly more important than the other. \\
9 & Comparing two factors, one is strongly more important than the other. \\
2, 4, 6, 8 & The scale of compromise between two adjacent judgments. \\ \hline \hline \end{tabular} Note: When comparing factors \(i\) and \(j\), the judging result is \(x_{ij}\), whereas the judging result is \(x_{ji}=\frac{1}{x_{ij}}\) when comparing factors \(j\) and \(i\).  
\end{table}
Table 1: Formation criteria of the judgment matrix.  
produced by micro-source \(x\). \(Z_{1}(t)\) is the price of purchasing electricity for the micro energy grid. \(Z_{2}(t)\) is the price of selling electricity for the micro energy grid.
## 3 Proposed Hardware Architecture
### Memory Unit  
To efficiently manage the storage of initial, intermediate, and final results, the proposed architecture includes a Memory Unit (MU) with a size of \(11\times m\). In this context, the number \(11\) signifies the number of memory locations, while \(m\) determines the width of each of these memory locations. Specifically, the architecture allocates memory locations for \(W_{1}\), \(Z_{1}\), \(W_{2}\), and \(Z_{2}\) to hold the initial values, while memory locations for A, C, D, and F are dedicated to storing the final projective points. Additionally, memory locations for B, E, and G are utilized for the storage of intermediate results. To facilitate efficient data retrieval from these memory locations, the architecture integrates two multiplexers, denoted as \(Mem\_RN\_1\) and \(Mem\_RN\_2\), each of size \(11\times 1\). The outputs of these multiplexers are referred to as \(Mem\_OP\_1\) and \(Mem\_OP\_2\). In order to ensure effective data access and manipulation, the architecture incorporates a \(1\times 11\) demultiplexer (DEMUX). The DEMUX  
Figure 1: Proposed BEC architecture.  
plays a vital role in organizing and directing data to their corresponding memory locations. It results in the optimization of the overall data management process.
## Results
### Ground Measured GPP Time-Series vs. Smoothed Satellite NDVI Time-Series  
At the four flux measurement sites, \(\rho\) of non-smoothed 8-day MODIS NDVI and GPP was 0.338 on average (Table 4). After applying smoothing, \(\rho\) on average increased up to 0.513, and the range between the best and the worst case was 0.228 (Table 4). In 98% of the runs, the tested methods successfully increased \(\rho\) values at the four test sites (Table S3). The method that produced the highest maximum \(\rho\) values varied between the sites.  
When comparing the smoothing methods, we note that the highest maximum and median \(\rho\) were generated by AG of 0.673 and 0.555, respectively (average for four sites). However, they varied among sites (Table S3). CV automatically selected fairly suitable smoothing parameter for SP but sometimes failed for SG and LO. In contrast to the previous experiment, the local filtering methods did not generate the highest \(\rho\) even with the optimized settings. The smallest range of \(\rho\) was generated by DL, which was similar to the first experiment.  
In conclusion, this experiment showed that applying smoothing methods to MODIS NDVI improved covariation with ground-measured GPP. No single method always generated the best results  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistic** & **SG** & **LO** & **SP** & **AG** & **DL** & **Average** \\ \hline Maximum & 0.635 & 0.635 & 0.658 & 0.673 & 0.603 & 0.641 \\ Median & 0.488 & 0.498 & 0.505 & 0.555 & 0.503 & 0.510 \\ CV & 0.453 & 0.528 & 0.534 & - & - & - \\ Range & 0.313 & 0.245 & 0.243 & 0.215 & 0.165 & 0.236 \\ \hline \hline \end{tabular} Note: the \(\rho\) between raw MODIS NDVI and ground measured NDVI is 0.338.  
\end{table}
Table 4: The four sites’ average maximum, median and range of \(\rho\) between smoothed MODIS NDVI from all settings of each smoothing algorithm and ground measured GPP.  
Figure 8: Smoothed time-series of each method at all test sites based on the settings that provide the minimum RMSE values at Fájemyr site.  
in representing NDVI time-series highly correlated with GPP time-series, but AG and DL were more robust in the sense of being less influenced by individual settings.
###### Abstract  
Assessing scapular position and motion during functional arm movement patterns may add relevant information to the evaluation of the clinical status and athletic performance of overhead sports athletes' shoulders. This study aimed to examine the three-dimensional scapular kinematics of elite volleyball players with (n = 11) and without scapular dyskinesis (n = 11) in comparison to non-athletes (n = 27). Four distinct arm elevation/depression tasks were assessed: shoulder abduction/adduction, flexion/extension, scaption, and a diagonal movement pattern mimicking throwing (proprioceptive neuromuscular facilitation diagonal 2 for flexion/extension or PNF-D2-flx/ext). Kinematic data was recorded from the spiking/dominant shoulder using an electromagnetic system (FASTRAK, Polhemus Inc., Colchester, VT, USA); MotionMonitor v9 software, Innovative Sports Training, Inc., Chicago, IL, USA). The study compared scapular rotations at 15\({}^{\circ}\) intervals of humor-thoracic (HT) angles, ranging from minimum to 120\({}^{\circ}\). Significantly different 3D scapular kinematics were observed between traditional arm motion tasks and PNF-D2 arm motion task (HT angle \(\times\) task interaction effect, \(p\) < 0.001, 0.275 \(\leq\eta_{p}^{2}\leq\) 0.772). However, when considering the combined influence of phase, HT angle, task, and group factors, no differences were found between groups (phase \(\times\) HT angle \(\times\) task \(\times\) group, \(p\) \(\geq\) 0.161, 0.032 \(\leq\eta_{p}^{2}\leq\) 0.058). The inclusion of a functional arm movement pattern when evaluating scapular position and movement in overhead athletes does not appear to be mandatory. However, these findings are preliminary and highlight the need for more research in this area.  
dyks; overhead sports; rehabilitation; scapula +
Footnote †: journal: Applied Sciences
# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 5 Discussion
[88] used the principal component analysis classifier for discrete wavelet transform, feature selection, and support vector machine for feature extraction. Three glioma grades were classified using 92 MR images. The proposed method achieved the highest accuracy of 88.26%, the highest sensitivity of 92.23%, and the highest specificity of 93.93%. [PERSON] and [PERSON] applied \"tumor or non-tumor\" classification with a deep learning classifier and achieved 83% accuracy [89]. Although it is easier to differentiate that, the accuracy obtained was insufficient, which indicates the shortcomings of the applied method. In another study, the results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm DNN classifier [90]. Wndchrm is open source software that can be used for classification with feature extraction and selection processes in the biomedical field. The results obtained with this software are lower than those obtained with our feature selection method. The increase of open source artificial intelligence libraries is increasing the number of deep learning applications in the field of medical imaging. However, being able to interpret these deep learning systems correctly and running the correct methods in datasets requires significant expertise. While grading brain tumors, they were not limited to a single radiomic feature category. The first-order features, the morphological and wavelet features, and the properties of the matrix groups were all examined. This has provided a comprehensive acquisition of all macro and micro level features of the 3D tumor area. This contributed to the high accuracy achieved. More features and higher accuracy have been obtained compared to other studies in the literature that examine different categories of radiomic features at the same time. The adopted network structure classified Grade II and Grade III tumors with high accuracy with wavelet filters. The DNN architecture, statistical analysis process and segmentation method used in this are of great importance. In addition, when we recognize that most of the features such as wavelet and first-order features cannot be identified and detected by the human eye, the medical contribution of this study is better understood. The method applied in this study achieved high success with w-HHH radiomic features with accuracy of 96.15%. As a result, the use of wavelet-based radiomic features in classification with deep learning methods has led to higher quantitative results.
## 4 Application
### Scenario
We further investigate whether both data sets result in similar metamodels that describe the underlying phenomena. Using these 100 designs, we want to receive analytical hints for the NVH optimization in the design process. An easy to use metamodel that takes linked influences into account are polynomials of third order with two variables, introduced in Equation (11). In Figure 10, the two metamodels for Direct and Box Morphing respectively show \(R^{2}\) values greater than \(0.9\). The original design is located in the center and marked by a black circle. The amplitude at \(777\,\mathrm{Hz}\) clearly depends on all three variables. In Figure 10b,d, we can recognize a direct dependency on the \(x_{3}\) coordinate. With varying the \(x_{1}\) coordinate, the amplitude hardly changes compared to varying the \(x_{3}\)-coordinate. The combination of both metamodels indicates an optimum location for \(P\) at a small \(x_{1}\)-value, great \(x_{2}\)-value and great \(x_{3}\)-value. The \(x_{1}\)- and \(x_{2}\) coordinates seem to have smaller influence than the \(x_{3}\) coordinate and \(x_{1}\) even could stay near \(0\,\mathrm{mm}\), depending on the other coordinates. These simple metamodels allow a fast decision for the direction of change, without numerical optimization. We validate them in the next section. Figure 9: FRF between hard point track rod in \(x_{2}\)-direction and wheel hub in \(x_{3}\)-direction and Skyline of 100 designs. (**a**) Results using Direct Morphing. (**b**) Results using Box Morphing. Polynomials considering all three variables are less vivid, but can lead to better results when using optimization tools. We calculate second order polynomials with three variables and cross terms for both morphing approaches using _Optislang_. We use a combination of an evolutionary algorithm and a downhill simplex method to find optimum designs. The results using the different models are discussed in the next section. Figure 10: Metamodels for the influence of the \(x_{1}\)-, \(x_{2}\)- and \(x_{3}\)-coordinates derived from 100 design variants by using Direct and Box Morphing. Original design at \((0\,\mathrm{mm},0\,\mathrm{mm},0\,\mathrm{mm})^{T}\) and an amplitude of \(6.68\,\mathrm{N}\,\mathrm{N}^{-1}\), highlighted by a black dot. Manually optimized design at \((-5.77\,\mathrm{mm},5.77\,\mathrm{mm},5.77\,\mathrm{mm})^{T}\), highlighted by a black triangle. (**a**) Direct Morphing, metamodel for \(x_{1}\)- and \(x_{2}\)-coordinates with \(R^{2}=0.94\), manually chosen optimum at \(5.8\,\mathrm{N}\,\mathrm{N}^{-1}\). (**b**) Direct Morphing, metamodel for \(x_{1}\)- and \(x_{3}\)-coordinates with \(R^{2}=0.90\), manually chosen optimum at \(4.7\,\mathrm{N}\,\mathrm{N}^{-1}\). (**c**) Box Morphing, metamodel for \(x_{1}\)- and \(x_{2}\)-coordinates with \(R^{2}=0.95\), manually chosen optimum at \(5.8\,\mathrm{N}\,\mathrm{N}^{-1}\). (**d**) Box Morphing, metamodel for \(x_{1}\)- and \(x_{3}\)-coordinates with \(R^{2}=0.94\), manually chosen optimum at \(4.3\,\mathrm{N}\,\mathrm{N}^{-1}\).