esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatically Injecting Robustness Statements into Distributed Applications
## 1 Introduction
However, these approaches conceal the parallelized code from developers and were not aimed at distributed applications. Our approach, along with the corresponding tool, employs data dependence analysis (following [PERSON]'s conditions [20]) to determine the appropriate code transformations and synchronization necessary for a reliable distributed and parallel execution. First, the source code is analyzed using the Javaparser library to detect blocking remote calls. Next, data dependence analysis is performed to find instructions depending on the result of the remote calls. Specifically, two statements have a data flow dependence when the output set (i.e., the set of variables written) of the first instruction overlaps with the input set (i.e., the set of variables read) of the second instruction [20; 21; 22]. Finally, the program's dependencies are updated according to the concurrency and failure handling library used, ensuring the integrity of the application. All of the above steps are executed automatically in a given source code. We performed several experiments to evaluate the benefits of our approach and analysis. An application was selected to apply the correspondent refactoring, and its performances were assessed using specific benchmarks, demonstrating an effective reduction in execution time. The main contribution of this paper is a comprehensive approach that automatically transforms a Java source code with blocking remote calls into a parallel and fail-safe code. By leveraging advanced static analysis techniques and data dependence analysis based on [PERSON]'s conditions, our method identifies and refactors specific fragments of code. This transformation not only improves performance but also enhances the resilience of distributed systems by incorporating robust concurrency and failure-handling mechanisms. The remainder of this paper is structured as follows. Section 2 reviews the state-of-the-art approaches and compares existing approaches with our proposed method. Section 3 introduces our general approach and presents a high-level architecture for analyzing and transforming the code. Section 4 describes the methodology for automatically transforming blocking calls into asynchronous calls and checking data dependencies among instructions. Section 5 presents the analysis and transformation performed on a sample application,along with the execution results. Section 6 discusses the obtained results and the limitations of our approach. Finally, conclusions are drawn in Section 7.
###### Abstract  
One of the most important limitations of mammalian cells-based bioprocesses, and particularly hybridoma cell lines, is the accelerated metabolism related to glucose and glutamine consumption. The high uptake rates of glucose and glutamine (i.e., the main sources of carbon, nitrogen and energy) lead to the production and accumulation of large amounts of lactate and ammonia in culture broth. Lactate and/or ammonia accumulation, together with the depletion of the main nutrients, are the major causes of apoptosis in hybridoma cell cultures. The KB26.5 hybridoma cell line, producing an IgG\({}_{3}\), was engineered with BHRF1 (KB26.5-BHRF1), an Epstein-Barr virus-encoded early protein homologous to the antiapoptotic protein Bcl-2, with the aim of protecting the hybridoma cell line from apoptosis. Surprisingly, besides achieving effective protection from apoptosis, the expression of BHRF1 modified the metabolism of the hybridoma cell line. Cell physiology and metabolism analyses of the original KB26.5 and KB26.5-BHRF1 revealed an increase of cell growth rate, a reduction of glucose and glutamine consumption, as well as a decrease in lactate secretion in KB26.5-BHRF1 cells. A flux balance analysis allowed us to quantify the intracellular fluxes of both cell lines. The main metabolic differences were identified in glucose consumption and, consequently, the production of lactate. The lactate production flux was reduced by 60%, since the need for NADH regeneration in the cytoplasm decreased due to a more than 50% reduction in glucose uptake. In general terms, the BHRF1 engineered cell line showed a more efficient metabolism, with an increase in biomass volumetric productivity under identical culture conditions.  
hybridoma; genome-scale metabolic model; antiapoptotic gene; mitochondrial transport; BHRF1 +
Footnote †: journal: Applied Sci. 2021
## 3 Lds-f-Ofdm System Model  
Based on the same LDS spreader used in LDS-UFMC, LDS-F-OFDM applies Filtered-OFDM waveform on the users' spreaded signals and transmits them over the channel. The LDS based on f-OFDM block diagram is presented in Figure 3. As depicted, the signal is mapped into a time-frequency grid and then the pilots are inserted for channel estimation. Afterwards, a N-point IFFT is performed to transfer the grid from the frequency domain to the time domain, data is then serialised and CP is added to combat Inter Symbol Interference (ISI) effect. Like UFMC, the main advantage of f-OFDM is its compatibility with OFDM, with a difference of passing the signal through a long length filter before the transmission. Extended over the CP, the filter helps to lower the high OOB radiation while maintaining the OFDM orthogonality between the subcarriers. Usually, a truncated sinc window is used as a shaping filter, however in our system we have chosen to implement other types of filters for a better comparison. For LDS-F-OFDM, we consider all the subcarriers as one subband to lower the overall complexity. Therefore, our transmitted signal is an OFDM modulated signal passed through an appropriate shaped filter. Without loss of generality, the signal expression can be written as follows:  
\[\mathbf{x_{LDS-F-OFDM}[n]}=x_{LDS-OFDM}[n]\times f_{TX}[n] \tag{14}\]  
where \(f_{TX}(n)\) represents the well-designed filter and \(\mathbf{x_{LDS-OFDM}[n]}\) is the discrete LDS-OFDM signal after the spreading and modulation, it is represented as follows:  
\[\mathbf{x_{LDS-OFDM}[n]}=\sum_{q=0}^{N-1}x^{q}e^{i2\pi nq/N} \tag{15}\]  
The transmitted signal is given by (16):  
\[\mathbf{x_{LDS-F-OFDM}[n]}=\sum_{q=0}^{N-1}x^{q}e^{i2\pi nq/N}\times f_{TX}[n] \tag{16}\]
## 3 Results and Discussion
### Enzyme-Assisted Extraction (EAE)
Figure 1: Effect of enzymatic extraction time on TPC. Symbols: (\(\bullet\)) Alcalase 2.4L FG, (\(\Circinus\)) Cellic CTec2, (\(\blacktriangle\)) Viscozyme L, (\(\triangle\)) Cellic HTec2, (\(\blacktriangledown\)) Bioprep 3000L. Dashed lines are the non-linear fitting curves (Equation (2)). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Enzyme** & \begin{tabular}{c} **C\({}_{e}\)** **(mg g\({}^{-1}\)** \\ **Rosemary)** \\ \end{tabular} & \
\begin{tabular}{c} **k (g\({}^{-1}\) Rosemary\(\cdot\)mg\({}^{-1}\)** \\ **min\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **h (mg\(\cdot\)g\({}^{-1}\)** \\ **Rosemary\(\cdot\)min\({}^{-1}\))** \\ \end{tabular} \\ \hline Cellic Htec2 & 7.4 & \(3.6\times 10^{-3}\) & \(2.0\times 10^{-1}\) \\ \hline \multicolumn{4}{c}{(R\({}^{2}=0.991,\,p<0.0001\))} \\ \hline Cellic Ctec2 & 8.0 & \(2.6\times 10^{-3}\) & \(1.6\times 10^{-1}\) \\ \hline \multicolumn{4}{c}{(R\({}^{2}=0.997,\,p<0.0001\))} \\ \hline Viscozyme L & 8.0 & \(2.4\times 10^{-3}\) & \(1.6\times 10^{-1}\) \\ \hline \multicolumn{4}{c}{(R\({}^{2}=0.996,\,p<0.0001\))} \\ \hline Bioprep 3000L & 10.9 & \(1.1\times 10^{-3}\) & \(1.2\times 10^{-1}\) \\ \hline \multicolumn{4}{c}{(R\({}^{2}=0.996,\,p<0.0001\))} \\ \hline Alcalase 2.4L FG & 10.7 & \(1.1\times 10^{-3}\) & \(1.2\times 10^{-1}\) \\ \hline \multicolumn{4}{c}{(R\({}^{2}=0.995,\,p<0.0001\))} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Kinetic parameters of EAE. The pectinolytic enzyme (Bioprep 3000L) led to the extract with the highest antioxidant activity, and therefore it was used for further experimentation. The possibility of using distilled water instead of aqueous buffer for the EAE with Bioprep 3000L was investigated. To that end, the extraction was repeated with distilled water, and the TPC values were recorded for 24 h (Figure 2). As it can be observed from Figure 2, there was a burst release of phenols for the first 3 h of the extraction. Further increase in incubation time resulted in a progressive reduction in TPC. Long enzyme treatment time, 24 versus 3 h, had a significantly negative effect on the phenol recovery. According to [PERSON] et al. (2018), this negative effect of time indicates that phenols were probably degraded during the long enzyme treatment. Moreover, for the first 6 h of extraction with pectinase, the TPC was not significantly higher when aqueous buffer was used. Therefore, distilled water was used for a 24-h extraction of rosemary with twice the amount of the enzyme in order to investigate the influence of the enzyme loading. Increasing the amount of enzyme did indeed slightly increase the TPC value of the extracts.
## 4 The Energy Balance Problem
### The Model and the Problem  
The basic model \(M_{0}\) (see section 1.3) is assumed. Additionally, we assume that the transmission range of sensors can vary with time (in fact, for each sensor our protocol may use only two different ranges: \(R\) and \(i\cdot R\), where \(i\) is a measure of the sensor's distance to the sink).  
We virtually \"cover\" the network area by a cycle sector of angle \(\phi\) (see Figure 8). Similar ring formation by flooding of beacon messages with hop count has been proposed in [54]. The cycle sector is divided into \(n\) ring sectors or \"_slices_\". The first slice has radius \(R\) (i.e. the sensors' transmission range). Slice \(i\)\((2\leq i\leq n)\) is defined by two cycles sectors, one of radius \(i\cdot R\) and the other of radius \((i-1)\cdot R\). We do not address what is the largest allowable transmission range (e.g. the largest \(i\) above); this would be limited by technology and interferences. However, taking a sufficiently large angle \(\phi\) and/or by hierarchically taking multiple sectors, we can cover the whole area without increasing \(i\) beyond the maximum allowable limit.  
In the analysis, we only count (for simplicity) energy spent during transmissions. Since however in our protocol (see next section) there is one receipt for each transmission, it is clear that even when energy during receipt is more or less the same as energy during transmissions, the analysis can be extended easily to the full case (counting both transmissions and receipts). We do not consider energy aspects due to duty-cycling, i.e. we assume that an appropriate power saving scheme is resolving this.  
**Definition 2**: The area between two consecutive cycle sectors is called _a ring sector_ (or \"slice\"). Let \(T_{i}\)\((1\leq i\leq n)\) be the \(i\)-th ring sector of the network.  
\(T_{1}\) stands for the ring sector with center the sink and radius equal to \(R\).  
**Definition 3**: Let \(S_{i}\) be the area size of the ring sector \(T_{i}\) of the network \((1\leq i\leq n)\).  
We wish to solve the \"_energy balanced data propagation problem_\", i.e. to propagate data to the sink in such a way that the \"average\" energy dissipation in each sensor is at each time the same. The average energy dissipation per sensor is taken to be the fraction of the total energy spent by sensors in a ring sector over the number of sensors in that sector. Because of our assumption that the number of sensors in an area is proportional to the area size, the average energy dissipation per sensor is calculated by dividing the total energy spent in a sector by the sector size.  
We do not study here medium access aspects, assuming the existence of underlying MAC protocol.  
Figure 8: Sensor Network with \(n\) ring sectors, angle \(\phi\) and ring “width” \(R\)
## 2 Classifiers and Symbolic Regression via Genetic Programming
### Support Vector Machines  
Support Vector Machines (SVMs) are very powerful techniques with various desirable properties for scientific applications [30; 31; 32; 33; 34]. Probably their main use is as binary classifiers, and it is in this capacity that they have been deployed in the present work. Conceptually, the specificity of SVM consists of mapping the inputs to be classified into a high dimensional space. This projection is achieved with already available mathematical operators called kernels, which in the most general versions of the classifiers are fully non-linear. The mapping into these high dimensional, or even infinite dimensional, spaces allows a better separation between the classes, which the SVM achieves by constructing a hyperplane in terms of the features selected by the user. The best hyperplane, the one which minimises the risk of misclassification, is derived by maximising the margins between the closest points of the two classes, called Support Vectors (SVs), and the hyperplane itself. The rule to classify a feature vector \(\mathbf{x}\) as disruptive or non-disruptive depends on the sign of this distance to the hyperplane [35].  
Given the high generalisation capability that they achieve by implementing the margins, SVMs are very powerful tools and have performed extremely well in the case of disruption prediction, as demonstrated in real time for example by APODIS. Their hyperplane can therefore be considered a particularly good approximation of the boundary between the disruptive and not disruptive regions of the operational space. On the other hand, their mathematical representation of the boundary bears no resemblance to the underlying dynamics of the physical phenomenon under investigation and is extremely non-intuitive. In the case of disruptions on JET, the equation of the hyperplane can easily comprise hundreds of support vectors and, consequently, the equation of the hyperplane presents an equal number of addends.  
Recently, various approaches to derive probabilities from SVMs have emerged and have provided interesting results. The availability of classifiers, which can output a probability, would be extremely useful in most applications. Since traditional SVMs provide only a distance to a hyperplane, their standard version requires a reformulation of their models, so that a probability can be associated to their predictions. A popular alternative takes advantage of the Bayes rule and this is the approach adopted for the present work [34].
# Trepenoid Biosynthesis Dominates among Secondary Metabolite Clusters in _Mucoromycotina_ Genomes
## 3 Results and Discussion
### Presence of Bacterial-Like SMCs  
We investigated the presence of genes of likely bacterial origin using the Alien Index concept (see Section 2.4). Apart from the clearly bacterial metabolite types (bacteriocins/ectoine), our results also support evidence of bacterial origins for candidate clusters among most NRPS-, PKS- and hybrid PKS/NRPS-containing clusters.  
Conversely, the candidate NRPS-like clusters appear to be of fungal origin, with the exception of FAA-containing clusters (_Glomerales_, _Diversiporales_), where the similarity of the core gene to the mycobacterial template was noted. Likewise, the clusters implicated in lysine (LYS2, single outlier) and siderophore (SID) biosynthesis were highly similar to their counterparts in _Dikarya_. In the case of terpenoids, no evidence of bacterial origin was retained in the AI values, except for a weak signal in the case of TS2-containing clusters among _Diversiporales_.  
Putative bacteriocin biosynthesis clusters contained a domain of unknown function (DUF692, PF05114) coding ORF and displayed high sequence similarity to _Stentrophomonas maltophilia_ and _Pseudomonas_ sp. sequences. In consequence, their occurrence in fungal genomes is likely from a bacterial source, either as contaminations or associated bacteria. The ectoine cluster present in the _R. oryzae_ genome is the most similar to sequences from _Achromobacter marplatensis_, which is a member of _Burkholderiales_. The bacteria from this order are known as endohyphal partners of _Mucoromycota_ representatives [18]. Therefore, we suppose that this SMC is likely of EHB origin. However, it remains an open question whether the abundance of _Mortierelles_ and _Diversiporales_ SMCs is dependent on their symbiotic status with endohyphal bacteria. Some previously named and characterised SMCs have clear sequence similarity to bacterial sequences [12]; others have a patchy taxonomic distribution without a clear evolutionary history. Other early-diverging fungi, _Basidiobolales_, seem to have acquired diverse SMCs from co-occurring bacteria [39]. The genomes of _Gigaspora margarita_ and its _Candidatus Glomeribacter gigasporanum_ symbiont encode NRPS-PKS hybrid clusters with genes of clear bacterial origin [15]. Interestingly, the sequence signatures are different for the SMC of the host and the symbiont [15]. The authorspostulate an HGT scenario from unrelated bacterial donors leading to the emergence of the hybrid SMC in the associated bacteria and host fungi. Moreover, similar SMCs are present in related fungal isolates, pointing at a vertical inheritance following the SMC's formation.  
The supporting evidence for the discussed results is included in the Supplementary Table S3 (Alien Index values for every cluster). Additionally, we have included a graphical summary of AI values broken down by taxa and cluster/core gene family in form of Supplementary Figure S1.
# Deep Learning-Based Sound Source Localization: A Review
## 3 General Principles of Deep Learning-Based Sound Source Localization
### Model Architectures  
The neural network architectures for SSL integrate deep learning-based feature extraction with physical models from traditional signal processing, forming core frameworks such as CNN, CRNN, Transformer, GFGRU (Gated Feedback Gated Recurrent Unit), and multi-task networks. These architectures demonstrate significant advantages in complex scenarios including indoor environments, UAV monitoring, and underwater applications.
## 3 Development of the Proposed Agricultural Drone Battery Cell Balancing Algorithm
### Fuzzy Controller Design for Battery Module Balancing
#### 3.3.1 Fuzzification  
In fuzzification, the SOC difference between the SOC and cell of each battery cell, two input values of the fuzzy controller, is converted into a new fuzzy value that can be handled by a fuzzy controller through a membership function.  
During the conversion to a fuzzy value, the two input values of the controller are defined as language variables using the language description in Table 3. As shown in the table, the applied language variable is defined in five steps for the two input and output values of the fuzzy controller.  
The SOC of the battery is divided into five levels representing a range of 20-80%. The reason for this configuration is because at values above 80%, lithium batteries become unstable and have an increased risk of explosion, while values below 20% are considered very small and within the cut-off range of the battery.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language Variable** & **Applied Value** & **Language Description** \\ \hline VL & 80 & Very Large \\ L & 65 & Large \\ N & 50 & Normal \\ S & 35 & Small \\ VS & 20 & Very Small \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definition of the language variable for the SOC of a battery cell.  
Figure 7: Definition of the membership function.  
The SOC difference between battery cells is defined using the language variables shown in Table 4.  
The maximum SOC difference is set to 20%, the discharge current amount is increased when the difference is 20% or more, and the discharge current amount is decreased when the difference is 0%, which will ensure that the batteries will operate in their stable range. The membership function for these language variables is shown in Figure 8.
## 6 Experimental Results
### Comparison with QLHH  
This section aims at comparing the current hyper-heuristic QLHH-II to the one it extends, i.e., QLHH. To compare both models, QLHH and QLHH-II have been trained on instances from class (\(15\times 15/10\)). QLHH uses the parameterisation provided used by [PERSON] et al. (2019) while QLHH-II uses the parameterisation obtained after the factorial experiment described above. The generated heuristic has then been executed on several  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{3}{c}{**Multi-Objective Metrics**} & \multicolumn{3}{c}{**Parameters**} & \multicolumn{3}{c}{**Multi-Objective Metrics**} \\ \(|\mathcal{B}|\) & \(\mathbf{\alpha}\) & \(\mathbf{p}\) & HV & IGD & \(\mathbf{\Delta}\) & \(|\mathcal{B}|\) & \(\mathbf{\alpha}\) & \(\mathbf{p}\) & HV & IGD & \(\mathbf{\Delta}\) \\ \hline
16 & 0.01 & 8 & \(3.563\times 10^{0}\) & \  
instances, ten times, from the same class. The solutions are represented according to both swarm metrics for two instances in Figure 8.  
The results shown in Figure 8 are representative of the behaviour in other instances. It shows the Pareto fronts obtained by executing both generated heuristics ten times on two instances from the class (\(15\times 15/10\)). QLHH generated a heuristic that provides solutions with a low number of connected components, around 1.5. This is due to the fact that the scalarisation weights enhance the connectivity objective without making UAVs cover new vertices until a point where the weight for the coverage rate becomes too large. This explains why the coverage speed is extremely high for solutions obtained by the heuristic generated by QLHH. The heuristic generated by QLHH-II provides a poorer connectivity since the number of connected components is around 3, but also a much better coverage speed. For any instance, UAVs need between 100 and 150 units of time to cover 95% of the environment graph, while they need between 10,000 and 30,000 with the heuristic generated by QLHH.  
By looking at Figure 8, QLHH-II may seem to focus on the coverage objective only, but this is not the case. This idea is induced by the good connectivity obtained by the heuristic generated by QLHH in comparison, but the solutions produced by the latter heuristic are practically unusable. Consequently, the heuristics generated by QLHH will not be used as a comparison for the stability experiment detailed below.