esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 5 Discussion
### Challenges and Future Research Needs  
The current model demonstrates significant potential for assessing phosphorus nutritional status in maize, but its broader applicability to other crops with similar physiological structures remains uncertain. Future research should focus on adapting this model to a wider range of crops by considering variations in plant morphology and nutrient uptake patterns, thereby enhancing its versatility and enabling its integration into diverse agricultural systems.  
Additionally, satellite imagery from platforms like Sentinel-2 provides valuable data, but its spatial and temporal resolution is insufficient for identifying phosphorus deficiencies at the field level. Since farmers make fertilizer application decisions at different growth stages, relying solely on satellite data for such critical decisions is impractical. Meanwhile, the model is also influenced by climate conditions, as fluctuations in temperature and weather events, such as rain, can complicate satellite data collection and impact crop growth. To mitigate these challenges, drone-based low-altitude remote sensing can be employed to provide more frequent and precise image acquisition throughout the crop's growth cycle. Drones offer a valuable supplement to satellite data, enabling more accurate and timely assessments of phosphorus status. Furthermore, the use of high-temporal-resolution satellites, such as FORMOSAT-2, which revisits daily and provides enhanced spatial and temporal resolution compared to Sentinel-2, could significantly improve largescale agricultural monitoring. The final fertilization prescription maps for maize could also be integrated with yield-monitoring equipment to enable more effective applications at the field scale. By combining these technologies, the model's reliability and practicality for phosphorus management can be further enhanced.
## 2 Methods: MVL Linked List as the Data Protection Model
### Logic Functions of the Multiple-Valued Allen-Givone Algebra
As MVL minimization by consensus method [50] for composed MVL function can shorten the number of product terms and change parameters in matrixes A,B,C, it can hide illegal modifications done in memory. In any way, all possible transformations of AGA expressions are based on the subsuming property of MVL product terms [50]. Definition 1: _, cited from [50]. Product term \(r_{1}\star X_{1}(a_{1},b_{1})\star\ldots\star X_{n}(a_{n},b_{n})\) subsumes another product term \(r_{2}\star X_{1}(c_{1},d_{1})\star\ldots\star X_{n}(c_{n},d_{n})\), if and only if conditions (1) and (2) are true: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{N_{row}}\)} & \multicolumn{4}{c}{Input Variables} & \multicolumn{4}{c}{Output Variable} \\ \cline{2-9}  & \(x_{1}\) & \(x_{2}\) & \(\ldots\) & \(x_{n-1}\) & \(x_{n}\) & \(F(x_{1}\ldots x_{n})\) & **Equivalent Product Terms:** \\ \hline
0 & 0 & 0 & \(\ldots\) & 0 & 0 & \(F(0,0,\ldots,0)\) & & \(F(0,0,\ldots,0)\star X_{1}(0,0)\star X_{2}(0,0)\star\ldots\star X_{n}(0,0)\) \\
1 & 1 & 0 & \(\ldots\) & 0 & 0 & \(F(1,0,\ldots,0)\) & \(\rightarrow\) & \(F(1,0,\ldots,0)\star X_{1}(1,1)\star X_{2}(0,0)\star\ldots\star X_{n}(0,0)\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \(k^{n}-1\) & \(k-1\) & \(k-1\) & \(\ldots\) & \(k-1\) & \(k-1\) & \(F(k-1,\ldots,k-1)\) & & \(F(k-1,k-1)\star X_{1}(k-1,k-1)\star X_{2}(k-1,k-1)\star\ldots X_{2}(k-1,k-1)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Truth table of a AGA function. Equivalent product term for every row with nonzero output value ) is given to the right. 1. \(r_{1}\leq r_{2}\),
2. \(c_{i}\leq a_{i}\leq b_{i}\leq d_{i}\) for all\(X_{i}i=1,\ldots,n\). For examelologic expression taken for k = 256 truth levels \(-57\times X_{1}(24,24)\times X_{2}(127,1275)\times X_{3}(317,331)+\) \(+80\times X_{1}(24,24)\times X_{2}(1275,1275)\times X_{3}(317,\ 331)=\) \(=80\times X_{1}(2,24)\times X_{2}(1275,1290)\times X_{3}(317,\ 331)\) contains three product terms with constants 57, 80. As according to condition (1) in Definition 1 relation \(57\leq 80\) is true, and according to condition (2) \(c_{i}=a_{i}\leq b_{i}=d_{i}\) for all \(X_{i}\), \(i=1,2,3\), respectively the first product term (shown above) will subsume the second one and can be crossed out. That is why data protection for AGA model need monitoring of the number of product terms and values of parameters in matrixes A, B, C.
# Low-Complexity Recursive Least-Squares Adaptive Algorithm Based on Tensorial Forms
## 3 Tensor-Based RLS Algorithms
### Tensor-Based Recursive Least Squares Algorithm (RLS-T)
Consequently, the filter updates can be performed using  
\[e_{\widehat{\mathbf{h}}_{i}}(n) =d(n)-y_{\widehat{\mathbf{h}}_{i}}(n) \tag{17}\]2 \[e_{\widehat{\mathbf{h}}_{i}}(n) =d(n)-y_{\widehat{\mathbf{h}}_{i}}(n) \tag{17}\]3  
where \(\mathbf{k}_{i}(n)=\mathbf{R}_{i}^{-1}(n)\mathbf{x}_{\widehat{\mathbf{h}}_{i} }(n)\) are the Kalman gain vectors. The error signal can be easily computed by using (18). The arithmetic complexity associated with the computation of each set of filter coefficients is proportional to the square of the corresponding filter length. The usage of a single adaptive filter is similarly prohibitive for hardware implementations. In the end, the estimate of the resulting filter can be obtained using  
\[e_{\widehat{\mathbf{h}}_{i}}(n) =d(n)-y_{\widehat{\mathbf{h}}_{i}}(n) \tag{17}\]4  
The tensor-based RLS (RLS-T) algorithm is available in Algorithm 1. This approach has been deeply analyzed in the paper [26] by using \(N=3\) (i.e., third-order tensors). The current sub-section described a generalization for \(N\geq 3\), thus providing even more efficient implementation solutions based on RLS adaptive filters. However, the RLS-T form requires for each of the \(N\) filters a computational workload proportional to \(L_{i}^{2}\). When considering applications where one of the filters still has a considerable amount of weights, the cost of practical implementations remains quite high. ```
Step Actions  Initialization :  Set \(\widehat{\mathbf{h}}_{i}(0)\) \(\mathbf{R}_{i}^{-1}(0)=\delta_{i}^{-1}\mathbf{I}_{L_{i}}\), \(\delta_{i}>0\) \(\lambda_{i}=1-\frac{1}{\lambda_{i}L_{i}}\), \(K_{i}\geq 1\)  For \(n=1,2,\ldots,\) number of iterations :  Compute \(\mathbf{x}_{\widehat{\mathbf{h}}_{i}}^{-1}(n)\), based on (19) \(e_{\widehat{\mathbf{h}}_{i}}^{-1}(n)=\widehat{\mathbf{h}}_{i}^{-1}(n-1) \mathbf{x}_{\widehat{\mathbf{h}}_{i}}(n)\) \(\mathbf{k}_{i}(n)=\frac{\mathbf{R}_{i}^{-1}(n-1)\mathbf{x}_{\widehat{\mathbf{h }}_{i}}^{-1}(n)}{\lambda_{i}+\lambda_{i}^{-1}(n)\mathbf{R}_{i}^{-1}(n-1) \mathbf{x}_{\widehat{\mathbf{h}}_{i}}(n)}\) \(\mathbf{\widehat{h}}_{i}(n)=\widehat{\mathbf{h}}_{i}(n-1)+\mathbf{k}_{i}(n) e_{\widehat{\mathbf{h}}_{i}}(n)\) \(\mathbf{R}_{i}^{-1}(n)=\frac{1}{\lambda_{i}}\Big{[}\mathbf{I}_{L_{i}}-\mathbf{ k}_{i}(n)\mathbf{x}_{\widehat{\mathbf{h}}_{i}}^{-1}(n)\Big{]}\mathbf{R}_{i}^{-1}(n-1)\) \(\widehat{\mathbf{g}}(n)=\widehat{\mathbf{h}}_{N}(n)\otimes\widehat{\mathbf{h} }_{N-1}(n)\otimes\cdots\otimes\widehat{\mathbf{h}}_{1}(n)\)
```  
**Algorithm 1**RLS-T algorithm
## 1 Introduction  
Currently, the text-matching technique based on sentence representation is one of the mainstream text-matching algorithms, which converts two sentences into a vector representation and measures the degree of similarity of the sentences by calculating the similarity between the two vectors. A good sentence vector representation should satisfy two conditions: one is alignment, which means that similar sentence vectors should have similar distances in the vector space. The second is homogeneity, which means that word vectors should be uniformly distributed in the vector space. The word vectors produced by native BERT [1] are anisotropic and affected by word frequency. The word vector space is vertebrate-shaped, with high-frequency words clustered in a small region in the space (the top of the cone) and low-frequency words dispersed at the bottom of the vertebra. The spatial distribution is not only uneven, but a large part of the space is vacant and does not represent any semantics, so the sentence vectors produced at this time are of low quality and have no way to characterise the sentence semantics well. The SimCSE [2] (Simple Contrastive Learning of Sentence Embeddings) algorithm, proposed in 2021, improves the quality of sentence vectors to some extent. extent improves the quality of sentence vectors.  
In this paper, we will study the sentence representation-based text-matching algorithm based on the BERT family of models, analyse the advantages and disadvantages of SimCSE, and aim to construct a better sentence vector representation algorithm.
# Multi-Label Extreme Learning Machine (MLELMs) for Bangla Regional Speech Recognition
## 3 Results and Discussion
### Dialet and Age Correlations Classification  
Findings indicate that the recognition rate for a speaker's dialect increases with age. Children have a smoother acoustic characteristic compared to adults, who have a high pitch-shift. [PERSON], [PERSON], and [PERSON] [18] state that aging has physiological changes that affect how auditory feedback is processed in the brain. Therefore, considering the age feature maps when predicting the dialect of a speaker yields a higher accuracy rate than predicting classes alone. As a result, low false predictions were observed for dialects between Sylhet-Chittagong and Bogra-Rangpur regional languages, as well as American and European speakers. Also, the confusion between close age groups 30s and 40s is reduced when two class labels are considered.  
The age and dialect classification for Bangla speech is a fourteen class classification problem; Khulna-Bogra-Rangpur-Sylhet-Chittagong-Noakhali-Mymensingh. There are 12 categories in the English Speech dataset; 20s-30s-40s-50s; Asian-American-European. For Bangla speech, precision, recall, and F1-score are highest at 86%, 78%, 75%, while for English speech they are 83%, 87%, 86% respectively, as shown in Table 5. For Bangla and English speech, the average accuracy is 81 and 87 percent, respectively. Figure 7 shows the confusion matrix obtained from the model prediction for dialect classification. In Bangla speech; CK, CB, CR, CS, CC, CN, CM, AK, AB, AR, AS, AC, AN, AM stands for Child-Khulna, Child-Bogra, Child-Sylhet, Child-Chittagong, Child-Noakhali, Child-Mymensingh, Adult-Khulna, Adult-Bogra, Adult-Rangpur, Adult-Sylhet, Adult-Chittagong, Adult-Noakhali, Adult-Mymensingh, respectively. 2 AS, 2 AM, 2 EU, 3 AS, 3 AM, 3 EU, 4 AS, 4 AM, 4 EU, 5 AS, 5 AM, 5 EU stand for 20s-Asian, 20s-American, 20s-European, 30s-Asian, 30s-American, 30s-European, 40s-Asian, 40s-American, 40s-European, 50s-Asian, 50s-American, 50s-European, respectively.
## 5 Performance Evaluation
### Comparison of BSMH and Other Benchmarks
#### 5.4.1 Average Routing Path Stretch  
The comparison in terms of the average routing path stretch is shown in Figure 13. As shown, for all network topologies, LVGR attains the best performance and followed by BSMH with \(\epsilon=0.3\). However, as will be shown in Sections 5.4.6 and 5.4.7, the shortest routing path stretch of LVGR has to compensate by a poor performance regarding the load balance and the network lifetime. In general, BSMH with small values of \(\epsilon\) (i.e., \(\epsilon=0.3;0.5\)) attains smaller routing path stretch than EDGR and GPSR. However, the increase of \(\epsilon\) will increases the routing path stretch, and when \(\epsilon=1.2\), the routing path stretch of BSMH is higher than that of EDGR and GPSR in 8/12 cases. More specifically, we have the following detail observations. Regarding to the 1-n communication, the average routing path stretch attained by BSMH with \(\epsilon=0.3\) is always less than 79% that of EDGR in all topologies, and 97% that of GPSR in 5/6 topologies (excepting Topology 5). When \(\epsilon\) reaches 0.5, BSMH results in the average routing path stretch which is smaller than 89% that of EDGR in all cases, and smaller than 97% that of GPSR in 4/6 cases (in the other cases, the average routing path stretch caused by BSMH and GPSR are almost similar). Regarding the n-n communication, the average routing path stretch attained by BSMH with \(\epsilon=0.3\) is less than 81% that of EDGR and 96% that of GPSR in all topologies. In the best cases, the BSMH gains the average routing path stretch begin smaller than 75% that of EDGR and 72% that of GPSR. When \(\epsilon=0.5\), the average routing path stretch caused by BSMH is always less than  
Figure 12: Impact of \(\epsilon\) on the network lifetime. The blue lines represent the network lifetime, the red lines represent the percentage of Euclidean routing paths that exceed the network boundary.  
89% that of EDGR, and less than 93% that of GPSR in 4/6 cases (in the other cases, the average routing path stretch caused by BSMH and GPSR are almost similar).
# Cytotoxicity and Antimicrobial Activity of BioAktTM and Phytic Acid: A Laboratory-Based Study
###### Abstract  
(1) Background: To improve endodontic treatments, it is necessary to find good irrigation solutions that balance potential toxic effects with optimal antimicrobial capacity. The aim of this laboratory study was to assess the cytotoxic and antimicrobial capacity of phytic acid (IP6) and BioAktTM in a laboratory setting to determine their suitability prior to endodontic evaluation. (2) Methods: The antimicrobial and cytotoxic effects of IP6 and BioAktTM were compared with those of 1.5% NaOCl. Cytotoxicity was evaluated via crystal violet assay and CellTiter-Glo(r) 2.0 assay, while antimicrobial capacity was tested via disk diffusion and the effect of a 1-h treatment on Enterococcus faecalis. (3) Results: A crystal violet test and CellTiter-Glo(r) 2.0 assay showed statistically significant differences in cell viability and cell activity after 4 and 24 h for all tested solutions (\(p<0.05\)). The antimicrobial activity of BioAktTM was similar to that of 1.5% NaOCl, while phytic acid showed the lowest antimicrobial activity compared to BioAktTM (\(p<0.05\)) and 1.5% NaOCl (\(p<0.05\)). (4) Conclusion: BioAktTM showed an optimal balance between antimicrobial activity and cytotoxicity.  
BioAktTM; phytic acid; cytotoxicity; antimicrobial activity; laboratory study
## 4 Results and Analysis
### Experimental Analysis Based on CEC2017  
For numerical validation, a set of 29 benchmark functions from the CEC2017 test suite was employed, which are widely utilized for assessing the performance of metaheuristic algorithms. These functions are categorized into four groups: the unimodal functions range from F1 to F3, multimodal from F4 to F10, hybrid from F11 to F20, and composite from F21 to F30. The proposed COSGO algorithm was benchmarked using these functions and compared against several existing methods, including GWO, SCSO, the WOA, AGWO-CS, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Parameter** & **Value** & **Algorithm** & **Parameter** & **Value** \\ \hline \multirow{3}{*}{GWO} & \(a\) & [2, 0] & \multirow{3}{*}{SCSO} & Sensitivity range (\(r_{G}\)) & [2, 0] \\ \cline{2-5}  & A & [2, 0] & & Phase control range (\(R\)) & [\(-2r_{G}\), \(2r_{G}\)] \\ \hline AGWO\_CS & \(a\) & \(2-\left(cos(rand)\cdot\frac{l}{Max_{lat}}\right)\) & & \(a\) & [2, 0] \\ \hline \multirow{3}{*}{PSOGSA} & \(G\)0 & 1 & \multirow{3}{*}{WOA} & \(A\) & [2, 0] \\ \cline{2-5}  & C1 & 0.5 & & \(C\) & 2.rand (0, 1) \\ \cline{1-1} \cline{2-5}  & C2 & 1.5 & & \(l\) & [\(-1\), 1] \\ \cline{1-1} \cline{2-5}  & & & & \(b\) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The simulation parameters. and the PSOGSA. Table 2 summarizes the experimental outcomes in terms of mean, worst, best, and standard deviation values. In this way, the Friedman ranking presented in Table 3 was used to compare the performance of COSGO to metaheuristic algorithms. For each benchmark function, the algorithms are ranked based on their performance, with the best-performing algorithm. These rankings were then averaged over all problems to compute the overall Friedman rank for each algorithm. A lower average rank indicates better overall performance. The Friedman test is a non-parametric statistical method that helps determine whether the observed differences in ranks among the algorithms are statistically significant. Based on Table 3, it is observed that the average ranking of the COSGO algorithm is lower than the other algorithms. It means the COSGO algorithm performance is better than the others.
# A Rotating Object Detector with Convolutional Dynamic Adaptive Matching
## 2 Related Work
### Dynamic network  
A network that adjusts its structure or parameters based on different models during inference is called dynamic network [18; 19], which is an emerging topic in deep learning. Compared to static network, a dynamic network improves the inference efficiency and compatibility of the model. From the perspective of data processing and training parameter optimization, the dynamic network is mainly divided into two categories: spatial dynamic networks and sample dynamic networks.  
**Spatial dynamic networks** focus on areas with the highest correlation with the target and perform spatial adaptive inference on these areas, while areas with little correlation with the target can be ignored. [PERSON] [20] proposed a resolution adaptive network (RANet), which contains multiple deep subnets with different weights. The samples are first identified from the subnet with the smallest weight. If the results meet the conditions, the sample exits the network early. Otherwise, the samples are input to a subnet with higher weights for identification, achieving a balance between accuracy and computational complexity and reducing spatial redundancy. [PERSON] [21] proposed a sparse label assignment strategy (SLA) that selects high-quality sparse anchors based on the fixed IoU, dynamically balancing the inconsistency of classification and regression during training.  
**Sample dynamic networks** can be subdivided into dynamic architecture and dynamic parameters based on different data processing methods. The dynamic architecture dynamically adjusts the network structure based on different input samples, effectively reducing redundant calculations. BranchyNet [22] has an additional branch classifier that allows test samples to exit the network early through these classifiers. SkipNet [23] is a residual network with gating units that selectively skip unnecessary convolution layers during inference, significantly reducing the inference time of the model. When the input data are fixed, the network parameters can be adjusted to improve the feature extraction effect, which will lead to a small increase in computational costs. [PERSON] [24] proposed pixel-adaptive convolution (PAC), which multiplies filter weights with convolution kernels with spatial variations to address the limitations of the content adaptation layer. The proposed method with dynamic parameter specificity improves the feature extraction ability of the backbone by transforming the sampling positions of the convolutional kernel. The detection effect on rotating targets is particularly significant.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Comparison to Previous Works  
In this subsection, the proposed positioning strategy is compared to different works in the literature where also multiple UAVs are deployed as ABBSs. Specifically, the selected works are by [PERSON] et al. (2012) and [PERSON] et al. (2016) as they are the only ones that fit our evaluated scenario.  
To obtain a fair comparison, some adaptations have been carried as it will be explained later in this subsection. It is important to remark that these algorithms do not take into account backhaul constraints considerations. According to this and, in order to evaluate the constraints imposed in the proposed strategy, two stages are carried out. Firstly, the individual bandwidth (\(B_{i}^{BH}\)) of each backhaul link is computed in such a way that Equation (24) is fulfilled using the minimum required bandwidth. Then, all these bandwidths are added and compared to \(B_{BH}^{total}\) to evaluate Equation (25). Therefore, Equation (24) is fulfilled in all the links and the evaluation of Equation (25) defines if the specific strategy accomplishes the total backhaul bandwidth limitation. Finally, in order to compare these algorithms in terms of user rate, the average user rate in the entire scenario is also computed.
## 5 Results and Discussion
### Fault Location
The output performance indices of DBN were as follows: MSE = 2.1116, RMSE = 1.4531, NRMSE = 0.0182, MAPE = 2.7047, and R-value= 0.9993. A sample result corresponding to fault location with different fault locations are shown in Figure 8:1
## 3 Results and Discussion  
### Functionality and Repeatability  
The evaluation criterion in terms of functionality is the ability of this approach to generate a heat map representing the electromagnetic (EM) signal intensity, which is based on the knowledge of the 3D form of the simulation environment, Figures 10a and 11a. This approach is validated against the heat map generated by the I-Prop tool, which uses the Multi-Wall method, Figures 10b and 11b. The functionality fulfills the evaluation criteria.  
The repeatability is verified by generating two different heat maps, i.e., for antennas 1 and 2, while keeping the same parameter settings. The simulated heat map results are similar (equivalent) to the simulations generated by the Multi-Wall method for both scenarios.