esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sensor Platform LSI Network System
### Sensor Platform LSI
#### 2.2.1 Collision Avoidance  
To implement Carrier Sense Multiple Access in addition of Collision Avoidance (CSMA/CA), we used the dual-driving regular I/O cell (CMOS type) with Schmitt-trigger input, which enables the sensor platform LSI to send data packets on the bus and listen to the bus line simultaneously. The nominal values of the low to high and high to low threshold values of the Schmitt-trigger are 1.54 V and 1.17 V, respectively. Before sending a data packet on the bus, a sensor platform LSI checks the bus status for four cycles in its data sending clock (e.g., 4 us in 1 MHz transmission speed). During these four checking cycles, if the bus logic level judged by the LSI's I/O cell is logic 0, the LSI regards the bus status as free, or busy otherwise. When the bus status is considered as free by the LSI, it starts sending the data packet. When the bus status is considered as busy by the LSI, it will wait for a certain time interval according to the adaptation function explained in Section 2.2.2. In addition, the maximum number of continuous zeros in a data packet is 2 because of the 4B5B encoding. Therefore, a sensor platform LSI doesn't have access to the shared bus, when the bus is transmitting a data packet.  
When many sensor platform LSIs operate simultaneously on a shared bus line with the same data transmission speed, several LSIs may start checking the bus status at the same time. Then, the bus status may be regarded as free by two or more LSIs that will start sending data packets at the same time, and data collision will happen. To reduce data collision, the sensor platform LSI performed the collision avoidance function to stop sending data packets once the data collision happens. Then, the bus line can soon be spared for transmitting other data packets. To perform the collision avoidance function, the LSI samples the bus voltage level at every positive edge of the on-chip clock (64 MHz in design) when sending data packets. At the sampling point, if the current bus logic level judged by the LSI's I/O cell is not the same as the logic level which the LSI sent on the bus, the LSI regards this situation as data collision and stops sending data. As shown in Figure 4, when the \"BUS check input\" and \"Output to BUS\" are in different logic levels, the exclusive OR (XOR) gate generates a rising edge which stops data transmission by a rising edge detection circuit. We adopted one of the simplest data collision avoidance scheme mentioned above and will use this sensor platform for the applications where critical real time control is not required.
## 2 Description of Experimental Setup
### Experimental Protocol  
The tests were performed for different structures of metallic regenerators (Str_30%, Str_35% and Str_40%) and two lengths (\(L_{1}\) = 60 mm and \(L_{2}\) = 70 mm). These tests were operated with ten frequencies from 0.65 Hz to 5.88 Hz, two strokes of the piston (\(C_{1}\) = 64.4 mm and \(C_{2}\) = 128.4 mm), and two thermal axial temperature gradients (\(\Delta T_{1}\) = 70 \({}^{\circ}\)C/10 \({}^{\circ}\)C; \(\Delta T_{2}\) = 40 \({}^{\circ}\)C/10 \({}^{\circ}\)C). Table 2 includes all the tested parameters in our study. Experiments were conducted for each combination of parameters for a given regenerator. The results are discussed in the next section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Structure of Regeneration**} & \multicolumn{3}{c}{**Total Relative Percent Uncertainty**} \\ \cline{2-4}  & & **Pressure** & **Velocity** \\ \hline Str\_30\% & \(\pm 0.63\) to \(\pm 0.70\%\) & \(\pm 1.16\) to \(\pm 2.33\%\) \\ Str\_35\% & \(\pm 0.60\) to \(\pm 1.6\%\) & \(\pm 2.30\%\) \\ Str\_40\% & \(\pm 0.61\) to \(\pm 0.95\%\) & \(\pm 2.30\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total relative percent uncertainty of experiment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Structure of Regeneration**} & \multirow{2}{*}{**Porosity**} & **Length** & **Stroke** & **Frequency \(Fr\)** & \multirow{2}{*}{\(Re_{\omega}=\frac{wD_{\omega}^{2}}{v}\)} & **Thermal Condition** \\  & \(\varepsilon\) & **\(L\) (mm)** & **\(C\) (mm)** & **(Hz)** & **\(T_{Rot}\)-\(T_{Cold}\) (\({}^{\circ}\)C)** \\ \hline Str\_30\% & 0.3 & 60 or 70 & 64.4 & 0.65–5.88 & 0.0083–0.0732 & \\  & & & or & (10 values) & 0.0145–0.125 & 70 \({}^{\circ}\)C–10 \({}^{\circ}\)C \\ Str\_35\% & 0.35 & 60 or 70 & 128.4 & & 0.0145–0.125 & or \\  & & & & & 40 \({}^{\circ}\)C–10 \({}^{\circ}\)C \\ Str\_40\% & 0.4 & 60 or 70 & & & 0.0164–0.139 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test conditions for alternating flow.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 6 Conclusions  
In this paper, we studied broadcasting in windmill graphs and stars of cliques, a generalization of windmill graphs with arbitrary clique sizes. Our most significant contribution is the development of an \(O(n\cdot\log n)\) algorithm to determine the broadcast time of any orig  
Figure 3: Path-connected cliques with \(k=4\) and \(4\) cliques of sizes \(4,3,5,4\).  
inator in a star of cliques, proving its optimality. This result is foundational for designing efficient algorithms in other clique-based network topologies, such as block graphs.  
We also examined path-connected cliques, proposing an algorithm that optimizes broadcast time based on clique size and adjacency, addressing challenges in path-dependent networks like hierarchical and block graphs.  
While our work advances broadcasting algorithms for clique-centric topologies, it is limited to these structures. Future research could extend these methods to hybrid or generalized networks and explore scalability for large distributed systems.  
Our contributions offer theoretical and practical insights, enhancing the efficiency of communication protocols in specialized network designs.  
Conceptualization, A.A. and H.A.H.; methodology A.A. and H.A.H.; investigation A.A.; writing--original draft A.A.; writing--review and editing A.A. and H.A.H.; supervision, H.A.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Comparison to Other Algorithms  
When applying K-medoids and MiniBatch K-means to the same database, there were variations from the standard K-means, which will be explained here. With internal indices, there was a recommendation of four clusters by the silhouette index, eight or nine clusters by the Calinski-Harabasz index, and three clusters by the Davies-Bouldin index. In this case, the internal validation indexes did not provide an equivalent value in the number of clusters. Therefore, unlike the result of standard K-means, K-medoids, and MiniBatch K-means performed lower overall stability starting at four clusters. For standard K-means, stability remained high until solutions with six clusters.  
The lower stability in other algorithms occurs because they suffer more from the repeated iterations and initializations required by stability methods. For example, K-medoids takes as centroids the very points present in the dataset and may suffer multiple divergences over too many runs of the algorithm, because as the dataset presents many points, the initialization and subsequent execution may vary.  
In the case of MiniBatch, the algorithm randomly obtains a subset of the data to perform cluster assignment, further increasing the variability between solutions and contributing to lower overall stability. With the overall stability reduced, the stability per cluster follows this trend, showing more variation in most solutions. Considering K-medoids and MiniBatch K-means, there were few clusters that remain with high stability across all solutions.  
The SLSa results presented in Figure 8 show the stability drop by demonstrating the history of each cluster in each solution. Note that in comparison with the result referring to K-means (Figure 6), the two algorithms presented many more \"Splits\" and \"Joins\" among the members of each solution, contributing to a larger number of clusters with an inadequate entropy level, while K-means exhibits this behavior only after the amount of six clusters.  
Figure 7: Clusters of solutions 4, 5, and 6 (each color represents a different cluster).  
The external validation indexes prove to be not only a promising tool for choosing the number of clusters but also for comparing different clustering algorithms. Overall, K-means demonstrated higher reliability compared to K-medoids and MiniBatch K-means, showing higher stabilities throughout the analysis process.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 5 LOS Velocity Calibration and Uncertainties
### Measurement Uncertainties Assessment Procedure  
#### 5.6.1 Definition of uncertainty and the GUM methodology  
The VIM [11] defines uncertainty as a:  
_non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand, based on the information used_.  
Figure 9: Calibration relation results. **Left column**: 5B; **Right column**: ZDM; **Top row**: 10-min data; **Bottom row**: binned data, including expanded uncertainties in error bars.  
In essence, the measured quantity value is only an approximation of the unknowable true value, i.e., only a best estimate. The uncertainty of a measured quantity value defines the interval centered on the best estimate and within which the true value lies with a certain probability.  
In this paper, \(V_{LOS}\) uncertainties are assessed using the GUM methodology [12]. The main steps are summarised hereafter:  
1. define the measurement model: \(y=f\left(x_{1},\ldots,x_{n}\right)\) where \(y\) is the best estimate and \(x_{1},\ldots,x_{n}\) are the input quantities;
2. list the input quantities and determine their uncertainties \(u_{x_{1}},\ldots,u_{x_{n}}\);
3. evaluate covariances between the uncertainties of the input quantities;
4. calculate the measured value \(y\);
5. combine the uncertainties on the input quantities using the law of propagation of uncertainties to obtain \(u_{c,y}\);
6. derive and report the expanded uncertainty \(U_{y}=k\cdot u_{c,y}\) where \(k\) is the coverage factor (see definition 2.38 in [11]).  
Note: the notations employed here for uncertainties of an arbitrary quantity \(x\) are: \(u_{x}\) standard uncertainty (i.e., coverage factor \(k=1\)); \(u_{c,x}\) combined uncertainty; \(U_{x}\) expanded uncertainty.  
The GUM methodology relies on the law of propagation of uncertainties (see Section 5.2.2 in [12]) given by :  
\[u_{c,y}=\sqrt{\sum_{i=1}^{N}\sum_{j=1}^{N}\frac{\partial f}{\partial x_{i}} \frac{\partial f}{\partial x_{j}}u_{x_{i}}u_{x_{j}}r_{ij}} \tag{5}\]  
where \(x_{i}\) and \(y\) are respectively the input quantities and output of the measurement model \(f\), \(r_{ij}\) is the correlation coefficient between the uncertainty distributions of \(x_{i}\) and \(x_{j}\) with \(i,j\in[1,N]\). Equation (5) is the most general form of the law of propagation of uncertainties. For uncorrelated input quantities, in other words independent variables (see Section 5.1.2 in [12]), \(r_{ij}=0\) when \(i\
eq j\) and Equation (5) is simplified to:  
\[u_{c,y}=\sqrt{\sum_{i=1}^{N}\left(\frac{\partial f}{\partial x_{i}}\right)^{2 }u_{x_{i}}^{2}} \tag{6}\]
## 3 Results and Discussion
### Composition of Amino Acids in Raw and lyophilized Meat By-Products  
The content of amino acids was similar in raw and dried meat by-products (Table 2). The composition of essential amino acids was not changed after lyophilization, except for decreased Leu and Thr in porcine samples and Leu, Thr, and Met in ovine samples (\(p<0.05\)). In other cases, the differences in amino acids were not significant. [PERSON], [PERSON], and [PERSON] [35] observed that freeze-drying slightly reduced the levels of some amino acids in lyophilized fish: isoleucine, threonine, and phenylalanine. A difference in the biological matrix may be a possible explanation for this result.  
However, after freeze-drying, the amounts of lysine, methionine, and valine decreased by 1.95, 1.61, and 1.25%, respectively. Our results correspond to those of [PERSON] et al. [36], confirming that this drying method may be appropriate for relevant products depending on the targeted compounds, such as amino acids, although amino acids are sensitive to processing methods depending on the material, type of amino acid, and method [37; 38].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **Meat** & **Ovine Meat** & **Lyophilized Ovine** & **Porcine Meat** & **Lyophilized Porcine** & **Sp.** & **Meth.** \\ \cline{3-8}  & **By-Products** & **By-Products** & **Meat By-Products** & **By-Products** & **Meat By-Products** & **Sp.** & **Meth.** \\ \hline \multirow{4}{*}{Proteins} & Heart & Mean \(\pm\) SD & Mean \(\pm\) SD & Mean \(\pm\) SD & Mean \(\pm\) SD & \multirow{4}{*}{*} & \multirow{4}{*}{NS} \\  & Heart & 17.2 \(\pm\) 0.02 & 16.9 \(\pm\) 0.01 & 15.8 \(\pm\) 0.02 & 15.4 \(\pm\) 0.01 & & \\ \cline{1-1}  & Liver & 19.6 \(\pm\) 0.03 & 19.3 \(\pm\) 0.01 & 16.6 \(\pm\) 0.3 & 16.3 \(\pm\) 0.01 & * & NS \\ \cline{1-1}  & Lung & 15.1 \(\pm\) 0.02 & 14.8 \(\pm\) 0.01 & 12.9 \(\pm\) 0.01 & 12.3 \(\pm\) 0.02 & * & NS \\ \cline{1-1}  & Kidney & 15.6 \(\pm\) 0.01 & 14.8 \(\pm\) 0.01 & 13.8 \(\pm\) 0.01 & 13.4 \(\pm\) 0.02 & * & NS \\ \hline \multirow{4}{*}{Fat} & Heart & 4.4 \(\pm\) 0.02 & 4.2 \(\pm\) 0.01 & 8.4 \(\pm\) 0.02 & 8.3 \(\pm\) 0.01 & * & NS \\ \cline{1-1}  & Liver & 4.3 \(\pm\) 0.01 & 3.92 \(\pm\) 0.01 & 3.8 \(\pm\) 0.02 & 3.56 \(\pm\) 0.01 & * & NS \\ \cline{1-1}  & Lung & 1.79 \(\pm\) 0.02 & 1.56 \(\pm\) 0.01 & ND & ND & * & NS \\ \cline{1-1}  & Kidney & 1.1 \(\pm\) 0.01 & 0.7 \(\pm\) 0.02 & ND & ND & * & NS \\ \hline \hline \end{tabular} Sp., species effect between ovine and porcine meat-by products; \(p>0.05\); \({}^{*}p<0.05\). Meth., method effect between raw and lyophilized meat by-products; \(p>0.05\); \({}^{*}p<0.05\). ND, not detected.  
\end{table}
Table 1: Protein and fat content in raw and lyophilized meat by-products (%).
## 4 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Component 1 (Embedding)**} & **Component 2** & \multirow{2}{*}{**Component 3 (Clustering)**} & \multirow{2}{*}{**Coherence Score**} \\  & & **(Dimension Reduction)** & & \\ \hline BERTopic & BERT & Kernel PCA & BIRCH & 0.7071 \\ BERTopic & BERT & Kernel PCA & DBSCAN & 0.7528 \\ BERTopic & BERT & Kernel PCA & HDBSCAN & 0.4294 \\ BERTopic & BERT & Kernel PCA & Agglomerative Clustering & 0.4165 \\ BERTopic & BERT & Kernel PCA & OPTICS & 0.4902 \\ BERTopic & BERT & Kernel PCA & Spectral Clustering & 0.8451 \\ BERTopic & **BERT** & **Kernel PCA** & **K-means** & **0.8463** \\ BERTopic & BERT & ISOMAP & K-means & 0.4951 \\ BERTopic & BERT & PCA & K-means & 0.5251 \\ BERTopic & BERT & SVD & K-means & 0.5283 \\ BERTopic & BERT & UMAP & K-means & 0.5340 \\ BERTopic & BERT & UMAP & Spectral Clustering & 0.6267 \\ BERTopic & BERT & UMAP & MeanShift & 0.7522 \\ BERTopic & BERT & UMAP & HDBSCAN & 0.6705 \\ BERTopic & FinBERT & UMAP & HDBSCAN & 0.6667 \\ BERTopic & SBERT & UMAP & HDBSCAN & 0.6678 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: BERTopic coherence score. Figure 2: BERTopic terms. not specified. This result is consistent with the study of [PERSON] et al. [55], [PERSON] et al. [13], [PERSON] [41], [PERSON] and [PERSON] [54], and [PERSON] [53] that showed BERTopic outperformed other models. Most importantly, the combined approach of KernelPCA and K-means has been shown to outperform other combined dimensionality reduction and clustering techniques. For example, [PERSON] et al. [65] performed a comparative study for solar irradiance forecasting and showed the combination of KernelPCA and K-means clustering outperforms combined KernelPCA and spectral clustering, KernelPCA and BIRCH, and KernelPCA and agglomerative clustering.
IoT helps cultivate the soil using cheap, easy-to-install devices to increase farm productions to meet the world's food demands based on precision agriculture [17; 18; 19], intelligent irrigation control [20; 21; 22], and greenhouse monitoring [23; 24; 25]. This technology can be applied in open and closed spaces to optimize consumables and supplies (fertilizers, pesticides, soil, and water) by setting automated alarms and threshold values for each resource. Suppose one collected data is out of the threshold value. In that case, a warning will be triggered, thus detecting problems affecting production and reducing the excessive consumption of supplies shortage risk. On the other hand, plant monitoring is imperative to detect diseases early and implement mechanisms to prevent their spread and production losses [26]. Traditional methods focus on either I) expert visual estimation or II) laboratory studies [27]. The first method is subjective and therefore has low accuracy. The second method identifies morphological characteristics to detect the disease. The main problem with these methods is that they are expensive, time consuming to process, and their detection efficiency is low. In recent years, plant disease detection automatization has integrated technologies such as image processing [28; 29], pattern recognition [30; 31], and computer vision [32; 33]. This paper presents SAgric-IoT, a technology platform based on IoT and CNN for monitoring physical variables and plant disease detection for precision farming. The platform uses communication algorithms for data monitoring and analysis for precision control of the production environment. In addition, it uses convolutional neural networks for plant disease identification and classification. We aim to develop a robust, low-maintenance, and low-cost IoT platform to control and optimize crop production. The remaining paper sections are as follows. Section 2 reviews IoT and precision agriculture. Section 3 describes the proposed technological platform in detail. Section 4 explains the system evaluation, describes the scenario, assessment, and results obtained from a small-scale network, and Section 5 summarizes our work and proposes future research.
## 4 Result Analysis  
The dataset of the concrete piston life prediction shown in Table 3 is randomly divided into a training set and a test set according to a ratio of 8:2. The three algorithms of MLR, SVR, and RFR are used to calculate the life coefficient \(\alpha\) using the data of the training set. The derived \(\alpha\) is then used to predict the life of the parts in the test set using the Formula (1) program in Python and invoking toolkits to calculate, analyze, and draw. The predicted life of the concrete piston calculated by each model is compared with the actual working life, as shown in Figures 6-8.  
As can be seen from Figures 6-8, among the three prediction models, the SVR model has the best prediction effect.  
The root mean square error (RMSE), as shown in Formula (7), is used to evaluate the prediction results.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i}^{n}(\hat{y}_{i}-y_{i})^{2}} \tag{7}\]  
where \(\hat{y}\) is the predicted capacity value, and \(y\) is the real capacity value.  
Figure 6: MLR model.  
Figure 7: SVR model.  
The RMSE refers to the square root of the mean of the square of all the errors in the estimated number \(n\). A smaller RMSE value indicates a more accurate prediction.  
In order to make a detailed comparison and analysis of the prediction accuracy of each model, a five-fold cross-validation is carried out. The dataset is divided into five subsets on average. Four subsets are selected as the training set and the remaining subset as the test set each time. A total of five validation calculations are carried out, and the RMSE values of each model are obtained, as shown in Figure 9. As can be seen from Figure 9, the prediction errors of each model are generally stable, among which the RMSE value of the SVR model is the lowest and the prediction effect is the best, so we chose the SVR model to predict the RUL of the concrete piston online.
## 4 Experimental Methodology
### Performance Evaluation of WvEnSL against Classical Supervised Algorithms  
Next, we compare the classification performance of the proposed algorithm against the classical supervised classification algorithms: SMO, C4.5 and \(k\)NN. Moreover, we compare the performance of iCST-Voting against the ensemble of classifiers (Voting) which combines the individual predictions of the supervised classifiers utilizing a simple majority voting strategy. It is worth noticing that  
* we selected WvEnSL\({}_{3}\) from all versions of the proposed algorithm since it presented the best overall performance.
* all supervised algorithms were trained using with 100% of the training set while WvEnSL\({}_{3}\) was trained using \(R=40\%\) of the training set.  
Table 6 presents the performance of the proposed algorithm WvEnSL\({}_{3}\) against the supervised algorithms SMO, C4.5, \(k\)NN and Voting on Pneumonia dataset, Tuberculosis dataset and CT Medical dataset. As above mentioned, the highest classification performance for each labeled ratio and performance metric is highlighted in bold. The aggregated results show that WvEnSL\({}_{3}\) is the most efficient algorithm since it illustrates the best overall classification performance. More specifically, WvEnSL\({}_{3}\) exhibits the highest \(F_{1}\)-score and classification accuracy on Pneumonia and Tuberculosis datasets, while for CT Medical dataset, WvEnSL\({}_{3}\) reports the second best performance, considerably outperformed by C4.5.