esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Calcination** & \multicolumn{3}{c}{**Calcination**} & \multicolumn{3}{c}{**Calcination**} \\
**Temperature of the** & \multicolumn{3}{c}{**MgO**} & \multicolumn{3}{c}{**10 MF**} & \multicolumn{3}{c}{**25 MF**} \\
**Immobilizing Agent, \({}^{\circ}\)C** & \multicolumn{3}{c}{**Top**} & \multicolumn{3}{c}{**Top**} & \multicolumn{3}{c}{**Top**} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Photographs of pellets consisting of electrolyte and immobilizing agents: MgO, 10 MF, or 25 MF, calcined at 500, 600, and 700 \({}^{\circ}\)C. The amount of electrolyte: 40 wt.%. A particularly important parameter for cell operation is the ionic conductivity of molten electrolyte-containing pellets (Figure 3). In this respect, magnesia impregnated with MgF\({}_{2}\) is clearly the best choice. An exceptionally high ionic conductivity was observed for the 25 MF system--it was threefold higher than in the case of bare MgO. The reason for this fact can be the presence of MgF\({}_{2}\), directly contacting the electrolyte. Additionally, the ionic conductivity increased with a rising filler calcination temperature. However, Table 2 shows that at the same time, the electrolyte absorption declined, which is adverse for the operation of the entire electrolyte-filler system. The exceptionally high ionic conductivity makes it possible to use a greater amount of the filler and to reduce the amount of expensive electrolyte without any deterioration in the operation of the high-temperature cell. This should improve the absorption of the electrolyte and reduce the possibility of its leakage during the operation of the system. The analysis of the pellet state after the electrolyte leakage test (Table 3) indicates the appearance of leakages during the electric tests at 400 \({}^{\circ}\)C. In this respect, the tested fillers were similar. The application of fillers calcined at higher temperatures clearly increased the leakage. In summary, the impregnation of MgO with MgF\({}_{2}\) had a particularly advantageous effect on the mechanical strength and ionic conductivity of the electrolyte-filler pellets. The mechanical strength is of key importance in the process of cell assembly, whereas the increased conductivity of the molten electrolyte reduces the electric power lost during cell operation. An additional asset is that MgF\({}_{2}\) does not absorb CO\({}_{2}\) in contradistinction to MgO, which easily absorbs both carbon dioxide and water. That is why the covering of magnesium oxide with magnesium fluoride protects MgO from hydrolysis and the formation of MgO\({}_{3}\) on the surface. It should be mentioned that the latter two processes are highly undesirable because at the cell operation temperature, water vapor evolves and magnesium carbonate decomposes. For this reason, we believe that the impregnation of MgO with magnesium fluoride favorably modifies the properties of magnesium oxide used as a filler in high-temperature cells.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 6 Simulations and Results
### Results
RF maintained its position at the first rank, whereas MLP secured the last rank among all the classifiers. The scores are listed with the ranks in Table 3 with the explanation provided below it. Figure 8: PNN detection accuracy of fault types. Figure 7: RF detection accuracy of fault types. In Figure 11, a comparison of TPR among SVM, RF, MLP, SGD, CNN, and PNN is given. SVM gave 0.166 TPR, which was higher relative to other classifiers, but clearly, RF beat all other classifiers with 0.177 TPR. This indicates that RF accurately detected more actual positives. In Figure 12, the F1-scores of all the classifiers against each fault type are compared with each other. To create a balanced classification model with the ideal values of recall and precision, the F1-score was maximized. In the bar graph, RF had the highest value of F1-score in all the fault types, giving an average of 93.15%. Therefore, RF outperformed all the classifiers in our proposed scenario. Figure 11: TPR of SVM, RF, SGD, MLP, CNN, and PNN. Figure 10: MCC score comparison of SVM, RF, SGD, MLP, CNN, and PNN classifiers. Figure 9: DA comparison of SVM, RF, SGD, MLP, CNN, and PNN detection of four types of fault.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 5 Future Prospects
### Genetic Modifications of Microalgae for Salt Tolerance and Carotenoid Accumulation
At present, several microalgal genomes sequences are available in the public databases, such as _C. reinhardtii_, _Chlorella variabilis_, _Nanochloropsis graditana_, _C. zofingiensis_, etc. [119]. This offers worthy opportunities for precise engineering technology. Novel molecular tools have been applied to modify microalgal traits, including advanced genome editing systems, such as clustered regularly interspaced palindromic repeats and associated proteins (CRISPR-Cas), transcription activator-like effector nuclease (TALEN), zinc-finger nuclease (ZFN) and RNA interference (RNAi) [10; 116]. For example, knocking down zeaxanthin epoxidase gene (_zep_) in C. _reinhardtii_ by using DNA-free CRISPR-Cas9 RNP-mediated mutagenesis can increase zeaxanthin content by 56-fold [120].
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 3 Graph-Based Nearest Neighbor Search  
We now outline the nearest neighbor search problem underlying our retrieval task and a search procedure using graph-based data structures [38]. Then, we describe the HNSW graph, which we use later as an index in our experiments.
# Fast Reaction to Sudden Concept Drift in the Absence of Class Labels
###### Abstract  
A data stream can be considered as a sequence of examples that arrive continuously and are potentially unbounded, such as web page visits, sensor readings and call records. One of the serious and challenging problems that appears in a data stream is concept drift. This problem occurs when the relation between the input data and the target variable changes over time. Most existing works make an optimistic assumption that all incoming data are labelled and the class labels are available immediately. However, such an assumption is not always valid. Therefore, a lack of class labels aggravates the problem of concept drift detection. With this motivation, we propose a drift detector that reacts naturally to sudden drifts in the absence of class labels. In a novel way, the proposed detector reacts to concept drift in the absence of class labels, where the true label of an example is not necessary. Instead of monitoring the error estimates, the proposed detector monitors the diversity of a pair of classifiers, where the true label of an example is not necessary to determine whether components disagree. Using several datasets, an experimental evaluation and comparison is conducted against several existing detectors. The experiment results show that the proposed detector can detect drifts with less delay, runtime and memory usage.  
concept drift; data stream mining; semisupervised environment +
Footnote †: journal: Applied sciences
## 5 Experiment
### Experimental Setup of Imaging  
The experimental procedure, as depicted in Figure 11, involves the following steps: Step 1. Data Preparation: The training data and test data are adjusted to create nine different cases based on the image size (32 \(\times\) 32, 64 \(\times\) 64, 128 \(\times\) 128) and interpolation method (nearest neighbor, bilinear, and bicubic).  
Step 2. Model Training: The adjusted training data is used to train a CNN-based deep learning model. As a result, nine different models are generated, each corresponding to a specific combination of image size and interpolation method. During the training process, a batch size of 128 is used, and the learning is repeated for 200 epochs.  
Step 3. Model Evaluation: Evaluation of the models is performed separately for each image size. First, a sample image is tested to verify the model's performance. Then, the test is conducted using malware samples. By following this experimental procedure, we can assess the detection performance of the CNN-based models under various settings, such as different image sizes and interpolation methods.
# Comparison of Damage Indexes for Assessing Seismic Fragility of Bearings in an Offshore Bridge
## 4 Results and Discussion
### Incremental Dynamic Analysis
Additionally, piers 1# and 4# are adjacent to rigid frame piers, resulting in a greater restraint effect than pier 5#. In general, the IDA curves of each bearing exhibit basically similar characteristics. For instance, the region bounded by the curve and the PGA axis is almost consistent across the different bearings. While the  
Figure 8: IDA curves for bearings 1#-6# at the beginning of the service period. relative displacements of bearings on different piers may vary during their service periods, the maximum difference does not exceed 1.5%. Therefore, it is reasonable to assume that the IDA curves of bearings 1#-6# exhibit similar behavior. To further analyze the relationship between the classification of damage states and PGA under different algorithms, bearing 5# with a larger relative displacement was selected  
Figure 10: IDA curves of the bearings 1#-6# (**a**)–(**f**) during the whole life cycle and (**g**) comparative results of bearings 1#, 3#, and 5#. Figure 9: Ground motion of Cape Mendocino. as an example after 50 years of bridge service and subjected to the Cape Mendocino ground motion. Table 8 shows the results of the analysis. Table 8 shows that the coverage ranges of PGA for the shear strain, relative displacement ductility ratio, and relative displacement obtained based on the theoretical method are greater than those using the empirical method in the service period. As an example, if the relative displacement is adopted as the criterion, the coverage of PGA associated with minor damage for the theoretical method (Case 2) is about 267% of that for the empirical method (Case 1), and the latter (Case 1) is 75% of the average value (Case 3) of the two. If the relative displacement ductility ratio is adopted as the criterion, the coverage for the theoretical method (Case 6) is about 167% of that for the empirical method (Case 5), and the latter is about 90% of the average value (Case 7) of the two. Additionally, compared with the relative displacement, the PGA values corresponding to the different damage states using the relative displacement ductility ratio are larger. After 50 years of service for the bearing, the PGA values for Case 7 are 121% (minor damage), 120% (medium damage), 108% (serious damage), and 103% (complete destruction) compared to those of Case 3, respectively. That is, when the damage degree for bearing is small, the difference between them is as high as 20%, whereas when the damage degree increases, the gap between the two is small, at approximately 5%. Since the quantification method of the damage index based on the relative displacement ductility ratio incorporates the principle of shear strain, Case 4 will not be specifically discussed here. In summary, the selection of different damage indexes significantly affects the assessment of the bearing's damage state throughout the operational lifespan. The analysis framework is presented in Figure 11.  
Figure 11: Applicability analysis process of bearing based on time-varying fragility.
# ICT in Rural Areas from the Perspective of Dairy Farming: A Systematic Review
## 3 Results
### Smart Farming (12 Documents)  
The term \"Smart Farming\", as defined by [85; 86], tries to incorporate advanced technologies such as sensors, artificial intelligence, and robotics to farms as a medium of increasing the efficiency of the production of food and to minimize the use of resources. The tasks of farm management, with the use of these technologies, are not only based on the positioning, but also take into account improved data and the context in real-time [15]. From the farmers' point of view, Smart Farming should provide them with an assured value in the form of a better making of decisions for a more efficient farm business management [87], since due to the market demands, it is necessary to optimize the management to achieve a better quality of the product [88]. In this way, the application of these new technologies to farms allows the development of specific models for them, providing the individual farmer with the capacity to plan their activities in response to the changing circumstances [15; 89; 90].  
In this field, it is important to highlight that research led by [91] in Italy, reflected the acknowledgement from the farmers towards the application of the new technologies as the only way to remain competitive and to improve sustainability. It is worth mentioning that interviews conducted by [92] to 817 German farmers showed clear evidence of the importance of technology (more concretely of smart phones) in agriculture as facilitating elements which also improved many operative procedures, as it can be combined with agricultural accuracy technologies. Despite all the aforementioned evidence, the study also shows that the rate of the use of technology is still lower than expected since there are factors that hinder the process of implementation, such as age, education, or the size of the farm. Therefore, due to this clear tendency to the incorporation of technologies in this field, interventions at niche level will be necessary to improve the farmers' organism and their local networks in these transactions, as well as the cooperative design of new institutions at a regime level in order to facilitate the fair and transparent assignation of risk and benefit in the information of agricultural data, thus being able to fulfil the demands of the market, as shown in studies performed by [18] of 28 farmers in Australia.  
Hand in hand with \"Smart Farming\", the phenomenon of \"Big Data\" appears: Massive volumes of data covering a wide range of fields, which can be captured, analyzed, and used in the decision-making process [17]. These are being used to provide predictive information in agricultural operations, to propel operative decisions in real time and to redesign commercial processes, playing a key role in the current networks of the food supply chain [85; 93]. In fact, more precisely in the area of dairy production, they are contributing to the improvement of productivity and profitability [18].
# A High Dynamic Weak Spread Spectrum Signal Acquisition Strategy Based on Iterative Local Search
## 4 Experiments and Results
The coherent integration time of the parallel code phase search based on the prior information assistance strategy is 4 ms, and the Doppler information is provided by the external device; the Doppler error ranges from 8.6 Hz to 3.9 kHz. The ILS improved parallel code phase acquisition algorithm uses the pres  
Figure 11: Acquisition time for the phase of the ILS parallel code under different dynamic conditions. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Real Number** & **Real Number** \\  & **Addition Operands** & **Multiplication Operands** \\ \hline Before improving strategy & \(3.5765\times 10^{11}\) & \(2.4995\times 10^{11}\) \\ After improving strategy & \(9.0690\times 10^{10}\) & \(6.3362\times 10^{10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of computations. The experiment results are shown in Figure 12. When the Doppler change rate is above \(-200\) Hz/s, the traditional parallel code phase acquisition algorithm can hardly achieve a successful acquisition. When the Doppler change rate reaches \(-10\) kHz/s and the SNR is above \(-36\) dB, the acquisition success probability of the ILS improved parallel code phase acquisition algorithm is still above \(99.9\%\). Therefore, the adaptability of the parallel code phase search algorithm improved by the iterative local search strategy to the dynamic is improved obviously. In addition, as shown in Figure 12, for the acquisition strategy assisted by prior information, its acquisition effect is closely related to the accuracy of the prior information. When the Doppler error provided by the prior information is \(50\) Hz, the acquisition effect is better. However, when the prior information error is \(150\) Hz, the acquisition success probability decreases significantly. The ILS acquisition strategy proposed in this paper can obtain more stable acquisition results without relying on the prior information provided by an extra device.
## 2 Materials and Methods
### Mathematical Modeling Part
#### 2.2.2 High-Temperature Heat Pump Modeling  
The heat pump's modeling is based mainly on the use of energy balances in the devices of the heat pump separately.  
The heat input rate in the evaporator device from the solar field (Q\({}_{\rm hp}\)) can be written as  
\[\rm Q_{hp}=m_{hp}\cdot(h_{1}-h_{4}) \tag{11}\]  
The temperature of the state point (1) is found as below:  
\[\rm T_{1}=T_{evap}+\Delta T_{sh} \tag{12}\]The superheating in the present work is selected to be at \(\Delta\mathrm{T}_{\mathrm{sh}}=10\) K. Moreover, the minimum temperature difference in the evaporator between the two streams is chosen to be at 5 K.  
The condenser device is responsible for the heating load production (\(\mathrm{Q}_{\mathrm{heat}}\)):  
\[\mathrm{Q}_{\mathrm{heat}}=\mathrm{m}_{\mathrm{hp}}\cdot(\mathrm{h}_{2}-\mathrm{ h}_{3}) \tag{13}\]  
The fluid in the condenser outlet is selected to be saturated liquid because there is no subcooling in the present study. Moreover, in the main analysis, the condenser temperature is selected at \(\mathrm{T}_{\mathrm{cond}}=70\)\({}^{\circ}\)C in the present work to be able to feed radiative terminal units for heating.  
The electricity demand in the heat pump's compressor (\(\mathrm{P}_{\mathrm{el,com}}\)) can be found below:  
\[\mathrm{P}_{\mathrm{el,com}}=\mathrm{m}_{\mathrm{hp}}\cdot(\mathrm{h}_{2}- \mathrm{h}_{1}) \tag{14}\]  
The compression process is modeled with the isentropic efficiency (\(\mathrm{\eta}_{\mathrm{is}}\)). This parameter is expressed with the next formula:  
\[\mathrm{\eta}_{\mathrm{is}}=\frac{\mathrm{h}_{2,\mathrm{is}}-\mathrm{h}_{1}}{ \mathrm{h}_{2}-\mathrm{h}_{1}} \tag{15}\]  
The state point (2,is) is found by the intersection of the isentropic line of \(\mathrm{s}=\mathrm{s}_{1}\) with the line of high-pressure \(\mathrm{p}=\mathrm{p}_{\mathrm{h}}\). More details regarding the modeling of the isentropic efficiency can be found in Ref. [33].  
The pressure reduction in the throttling valve is performed in an ideal way and thus it can be written as  
\[\mathrm{h}_{4}=\mathrm{h}_{3} \tag{16}\]  
The definition coefficient of performance (\(\mathrm{COP}_{\mathrm{hp}}\)) for heating production in the heat pump is given according to the next formula:  
\[\mathrm{COP}_{\mathrm{hp}}=\frac{\mathrm{Q}_{\mathrm{heat}}}{\mathrm{P}_{ \mathrm{el,com}}} \tag{17}\]  
At this point, it has to be added that there is an extra electricity consumption from the auxiliary electrical resistance (\(\mathrm{P}_{\mathrm{el,aux}}\)). Therefore, the total electricity (\(\mathrm{P}_{\mathrm{el,tot}}\)) consumption can be written as below:  
\[\mathrm{P}_{\mathrm{el,tot}}=\mathrm{P}_{\mathrm{el,com}}+\mathrm{P}_{\mathrm{el,aux}} \tag{18}\]  
The system's coefficient of performance (\(\mathrm{COP}_{\mathrm{sys}}\)) is given according to the next expression:  
\[\mathrm{COP}_{\mathrm{sys}}=\frac{\mathrm{Q}_{\mathrm{heat}}}{\mathrm{P}_{ \mathrm{el,tot}}} \tag{19}\]
# BTCAM: Attention-Based BiLSTM for Imbalanced Classification of Political Inquiry Messages
## 3 Methodology
### Preliminaries
#### 3.1.2 Topic Modeling  
Topic modeling is the process of learning from unlabeled data to uncover latent topics and their similarities, which is often accomplished using probabilistic models. Latent semantic analysis (LSA), which was the first attempt to mine semantic structure in text collections, was developed by [PERSON] et al. [33] to tackle the issue of polysemy in semantic retrieval. Probabilistic latent semantic analysis [34] is the first unsupervised learning technique to employ probabilistic generative models for topic analysis of texts. However, the PLSI algorithm has a tendency toward overfitting since the number of parameters increases linearly with topics. As a result, [PERSON] [35] proposed latent Dirichlet allocation (LDA), which is now one of the most widely used topic modeling methods. As shown in Figure 3, LDA is a three-layer Bayesian model, including a document layer, topic layer, and word layer. Among them, the document layer represents the entire document collection, and each document is a collection of a series of words. The topic layer represents the underlying topics in a collection of documents. Each topic is a probability distribution of a set of words, indicating the likelihood of each word appearing under this topic. Each document is considered to be generated from a mixture of these latent topics. The word layer represents each word in the document. Each word is drawn from a specific topic, which is drawn from the topic distribution of documents.  
\[\text{Dir}(\mu\mid\alpha)=\frac{\Gamma(\alpha_{0})}{\Gamma(\alpha_{1})\dots \Gamma(\alpha_{t})}\prod_{t=1}^{T}\mu_{t}^{a_{t}-1} \tag{1}\]  
where \(0\leq\mu_{t}\leq 1,\sum_{t}\mu_{t}=1,\alpha_{0}=\sum_{t=1}^{T}\alpha_{t}\), and \(\Gamma\) is the gamma function.  
\begin{table}
\begin{tabular}{l l} \hline
**Symbol** & **Description of Symbol** \\ \hline \(x\) & The embedding of messages \\ \(y\) & The label of messages \\ \(M\) & Collection of documents \\ \(N\) & Length of document \\ \(\alpha\) & Dirichlet prior for the topic distribution per message \\ \(\beta\) & Dirichlet prior for the word distribution per topic \\ \(\theta\) & Polynomial distribution of topics in certain message \\ \(\theta\) & Polynomial distribution of words in certain topic \\ \(Z\) & The words of messages \\ \(W\) & The topics of messages \\ \(T\) & Collection of topics \\ \hline \end{tabular}
\end{table}
Table 1: Summary of notation.