esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Media  
Artificial media commonly used with Enterobacteriaceae are tryptone soya agar (TSA) or tryptone soya broth (TSB), which were both sourced from Oxoid Basingstoke, Hampshire, England. Bacteria were first grown for 24 h on TSA at 29 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C. Consequently, a loop of a colony was added to 100 mL of TSB in a 250 mL mason jar (Tablefair(r), Auckland, New Zealand) and incubated on a rotary shaker at 30 \({}^{\circ}\)C and 100 rpm for 18 h. A standard concentration of 5 \(\times\) 108 cells/mL (determined via spectrophotometry) was used for inoculation on broth media. The pH of cultures was measured using an electronic pHmeter. The control TSB had a pH of 6.84, and the addition of bacteria increased the pH to 7.74. In addition, we tested the effect of pH on MVOCs production by the bacteria, as pH was previously reported to significantly influence the type of compounds released by food baits, and it consequently impacts the attraction of female QFF [18; 25; 26]. Given that the selected Enterobacteriaceae can be found both on host plants and within the gut of fruit fly (i.e., bacteria experience different pH conditions), the pH of the media containing the four inoculated bacteria was adjusted to 3, 5, 7, 9, and 12, using 12.4 M (38%) hydrochloric acid or 10 M (40%) sodium hydroxide solution in a dropwise manner until reaching the appropriate pH [26]. The pH was re-checked 24 h after adjustment and before the SPME collection commenced.  
Fruits were also inoculated with bacteria to investigate the interaction between bacterium species and natural substrates. Mature green kiwifruit cv. 'Hayward' (_Actinidia delicosa_), apples cv. 'Royal Gala' (_Malus domestica_), and oranges cv. 'Navel' (_Citrus sinensis_), purchased at the local supermarket, were surface sterilized by immersion in 85% ethanol for 2 min and then washed with distilled water for 2 min. Fruits were then left under UV light (100-400 nm) for 2 h, before being inoculated with CF, EC, KO, and PA. Each fruit was inoculated five times with 10 \(\upmu\)L of the same bacterial solution (5 \(\times\) 10\({}^{8}\) cells/mL), 3 mm in depth, using a needle to mimic insect oviposition ([PERSON] et al. 1994). Control fruits were inoculated with 10 \(\upmu\)L of distilled water only. Incubating conditions were the same as used for bacterial growth (i.e., 29 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C). To confirm bacteria growth on fruits, a slice (ca. 1 cm\({}^{2}\)) of the flesh from the inoculation site was macerated with TSB and streaked onto TSA after the volatile collection and bacterial growth were assessed.
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 5 Results and Analysis
### Experimental Setup  
In this paper, a multicore system with the proposed decision tree-based adaptive reconfigurable cache is built in the GEM5 simulator [22]. GEM5 is an open-source platform that is widely used in computer architecture research, encompassing system-level architecture as well as processor microarchitecture. It provides basic simulation objects,  
Figure 9: State transition diagrams: (**a**) state transition diagram of adaptive controller; (**b**) state transition diagram of performance monitor.  
including a CPU, cache, on-chip network, memory, and other basic components, and it allows researchers to model modern computer hardware at the clock cycle level. It can also boot the Linux operating system to run a full-system simulation. Figure 10 depicts the abstraction level of the simulation system, with the real device in the solid line and the simulated device in the dashed line. The GEM5 simulator runs on real hardware with a host operating system. The multi-core system with the proposed reconfigurable cache is simulated by the GEM5, which corresponds to the simulated hardware in the figure. The benchmark runs in the GEM5 in full-system mode.  
The detailed configuration of the GEM5 simulator is summarized in Table 1. The architecture of the build system is shown in Figure 11. The PARSEC benchmark [23] and SPLASH-2 benchmark [24] are employed to evaluate the performance of the proposed cache scheme. During simulation, only one application of the benchmark runs each time. Multiple threads with the same number of system cores are created for the running application and each thread is bound to a core of the system. All threads run in parallel. The simulation is terminated after the application enters the parallel stage and runs for 10 s (1 \(\times\) 10\({}^{10}\) clock cycles). In this paper, the simulation is performed with reconfiguration periods of 5 million clock cycles (C.C.), 10 million cycles (C.C.), 30 million cycles (C.C.), and 50 million cycles (C.C.) in the full-system mode. The decision-making period accounts for 10% of the reconfiguration period. The reconfiguration overhead is 500 clock cycles (C.C.).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**CPU**} & **ISA: X86-64** \\ \cline{2-3}  & **8 Cores/1 GHz** \\ \hline \multirow{3}{*}{L1 cache} & private, fixed parameters \\  & L1-I: 64 B/32 KB/4-way set-associative/1 clock cycle \\  & L1-D: 64 B/32 KB/4-way set-associative/1 clock cycle \\ \hline \multirow{3}{*}{L2 cache} & Shared, associativity reconfigurable \\  & 64 B/4 MB/16,8,4,2,1-way set-associative/10 clock cycle \\ \hline \multirow{2}{*}{Interconnect} & Coherent Bus \\ \hline \multirow{3}{*}{Main memory} & DDR3\_1600\_8 \(\times\) 8 \\  & 4 GB/50 clock cycle \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the GEM5 simulator.  
Figure 10: Abstraction level of the simulation system.
## 3 Results
### Effect of Developmental Stages on RcTPS Expression  
There are nine _RcTPS_ genes belonging to three subfamilies (TPS-a, -b, and -g) expressed in MU opening petals, whereas the _RcTPS8_ and _RcTPS9_ expressed only in the buds about to open (S3) and hardly expressed in the petals of open flowers. These petal-expressed _RcTPSs_ exhibited great differences in the expression levels and patterns (Figure 5a). _RcGDS_, which was responsible for catalyzing the synthesis of germacrene D as the only product, exhibited the highest average expression level. _RcTPS32_ and _RcTPS33_, which encoded the same protein sequences, exhibited the second and third highest expression levels, respectively. The expression levels of other _RcTPSs_ in petals and buds was relatively low.  
The oscillations in _RcTPS_ expression at different developmental stages of MU petals were analyzed. These nine genes exhibited three expression patterns. The first group comprised _RcLINS_, _RcTPS18_, _RcLIN-NERSI_, and _RcGDS_, whose expression peaked in the buds about to open (S3) or in the early opening flowers (D1) and declined in old flowers. The second group comprised _RcLIN-NER33_, _RcTPS32_, _RcTPS33_, and _RcTPS46_, whose expression levels increased when the flowers opened and peaked in old flowers. The third group comprised _RcTPS39_, whose peak expression was on the second day after flowering (D2) (Figure 5b). These petal-expressed _RcTPSs_ were selected to further validate the RNA-seq results in five samples using qRT-PCR. The results confirmed the differential TPS gene expression patterns in different samples (Figure S4). The concordance between qRT-PCR and RNA-seq results demonstrated the reliability of RNA-seq data in the present study.  
Figure 4: Collinearity analysis of TPS genes among _R. chinensis_ and other representative plant species. Gray lines in the background indicate the collinear blocks between rose and other plant genomes. The color and black lines highlight the syntenic TPS gene pairs.  
Figure 5: Expression profiles of _RcTPSs_, _RcTPTs_, and _RcNUDX1_ genes at different flower developmental stages. **(a)** Terpene biosynthesis pathways in rose [23; 46] and expression levels of _RcTPSs_, _RcTPTs_, and _RcNUDX1_ genes. The genes marked with asterisk (*) indicated that the average FPKM of all members of this family was less than 1 in five MU samples. Clustering_distance_rows’euclidean’, clustering_method ‘complete’. **(b)** The gene expression patterns in different MU samples. Bars represent the standard error (\(n=3\)). The genes with different expression patterns are illustrated in different colors. Different lowercase letters indicate statistically significant differences among samples at different developmental stages (ANOVA text, \(p<0.05\)).
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.4 Edge Pre-Processing
[PERSON] and [PERSON] [63] conduct an experiment testing several feature selection search methods on the Gas Sensor Array Drift dataset, confronting a conventional decision tree algorithm (C4.5) with a data stream mining decision tree algorithm, called Hoeffding Tree (HT), and conclude that fog computing using HT coupled with Harmony feature selection could reach good accuracy, low latency and scalability for this dataset. [PERSON] et al. [89] present a new approach for novelty detection in sensor signals based on [PERSON]'s test, which tests the homogeneity of variances of samples taken from the same population and combined with other statistical and autocorrelation features. That is done by extracting seven statistical features into an NN used for event detection (novelty detection), and the computation is carried out at the fog layer. In order to eliminate noise before extracting features, EMD (Empirical Mode Decomposition) is used. The use of deep learning in edge applications such as augmented reality, real-time video analytic, and others to support decision making is covered by [PERSON] et al. [33]. The authors emphasize very high accuracy levels that were achieved with DNNs and CNNs. The proposed approach performs initial processing of the data close to the source at fog nodes, resulting in a significant reduction in the data that are transferred and stored in the cloud. [PERSON] et al. [75] provide a framework for edge computing and analytics architecture applied to vehicular networks. The paper presents a discussion about different machine learning techniques to implement an edge analytics framework in Intelligent Transportation Systems (ITS). This framework allows the transmission of only a summary or basic results extracted from big data to the cloud instead of transmitting the whole amount generated. According to the paper, the framework provides low latency and high reliability. A similar technique applied to mobile crowd sensing (MCS) was approached by [PERSON] et al. [74], where raw data such as images or video clips are processed on edge nodes by implementing preliminary filtering, while authentication verification and relevance identification is performed at the border of the network. Unqualified, adulterated and irrelevant information is detected and filtered out, and only the useful sensory information that is preserved is aggregated and uploaded to the sensing platform. In this way, the total amount of data that must be delivered to the sensing platform can be significantly reduced. Another example is found in the work of [PERSON] and [PERSON] [84] where the authors use a CNN model to recognize emotions through video and speech. In the proposed framework, pre-processing of video and sound is performed at the edge. In addition, the trained parameters of the CNN model are retrieved from the cloud and used at the edge for testing.
## 6 Discussion
The conclusions drawn regarding the DLMU scheme performance, based upon its application to a series of benchmark STO problems from the literature, as well as its comparison to SIMP, DLTOP and the novel DLSCALE approach also proposed by the authors [28], are summarized hereafter: (i) DLMU achieves a remarkable acceleration of SIMP, of the order of 40-80%; (ii) the acceleration of SIMP offered by DLMU becomes more pronounced with increasing mesh density, as a result of the enhanced reduced order modeling efficiency (acceleration increases by 15-65% between 75,000 and 200,000 FEs); (iii) DLMU does not compromise the solution quality offered by SIMP, irrespective of the employed mesh density (less than 1% reduction of objective function value for all test examples and mesh densities); (iv) DLMU, DLTOP and DL SCALE outperform SIMP, achieving similar acceleration values of the order of 35-80%, with negligible impact on the solution quality (less than 2% reduction of objective function value for all test examples and mesh densities); (v) in the majority of cases (test examples B, C, D, E), DLMU marginally underperforms DL SCALE; and (vi) in all cases DLMU and DL SCALE outperform DLTOP, which highlights the efficiency enhancement stemming from the employment of a reduced order model framework. The future work is oriented towards the development of a web application where the two trained machine learning components of the proposed scheme will be provided for free. With regard to the topology optimization part, in order to replicate the results presented above, TOP88 needs to be used (freely available). With regard to the deep learning part of the work, the reader should contact the corresponding author ([EMAIL_ADDRESS]) for providing the trained DBM networks. Conceptualization, N.A.K. and N.D.L. ; methodology, N.A.K. and N.D.L. ; software, N.A.K. ; validation, N.A.K. ; formal analysis, N.A.K. and A.N.N. ; investigation, N.A.K. ; writing--original draft preparation, N.A.K., A.N.N. and N.D.L. ; writing--review and editing, N.A.K., A.N.N. and N.D.L. ; visualization, N.A.K. ; supervision, N.D.L. ; project administration, N.D.L. ; funding acquisition, N.D.L. All authors have read and agreed to the published version of the manuscript. This research was funded by European Union funds grant number 101007595. Not applicable. This research has been supported by the ADDOPTML project: \"ADDitively Manufactured OPTimized Structures by means of Machine Learning\" (No: 101007595) belonging to the Marie Sklodowska-Curie Actions (MSCA) Research and Innovation Staff Exchange (RISE) H2020-MSCA-RISE-2020. The authors declare no conflicts of interest.
## 4 Preliminaries
### Bloom Filter  
The Bloom filter [24] is a space-efficient probabilistic data structure used to examine the membership of an element in the set. The Bloom filter consists of a bit array of \(m\) bits, initially all set to 0, and \(k\) different hash functions used to map an element into one of positions in a bit array with a random uniform distribution. When adding an element to the Bloom filter, \(k\) hashes of an element are calculated using \(k\) hash functions and the bits on the position of \(k\) hashes are set to 1. To test whether an element is a member of the set, \(k\) hashes of an element are computed with \(k\) hash functions. If any bit on the position of \(k\) hashes is 0, the element is definitely not a member of the set. If all bits are 1, the element is regarded as a member of the set. Unfortunately, a false positive, indicating that a non-member element can pass the membership test, can occur due to the limited size of the Bloom filter and the duplicate occurrence of hash functions between different elements. Figure 4 illustrates a Bloom filter.
## 3 Results and Discussion
### Valorization of Prickly Pear Juice Geographical Origin Based on Mineral Content Using LDA
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Volatile** & **Wilks’** & & & & & & \\
**Compounds (\(\mu\)g/L)** & **Lambda** & **F** & **df1** & **df2** & \(p\) & **Function 1** & **Function 2** \\ \hline
2-Butenal & 0.231 & 54.909 & 2 & 33 & \(<\)0.001 & 0.171 ** & 0.165 \\
2,4-Decadien & 0.517 & 15.401 & 2 & 33 & \(<\)0.001 & 0.091 ** & 0.088 \\ Decanal & 0.538 & 14.158 & 2 & 33 & \(<\)0.001 & \(-\)0.064 & 0.220 ** \\ Decene & 0.507 & 16.070 & 2 & 33 & \(<\)0.001 & \(-\)0.069 & 0.234 * \\
1-Dodecene & 0.548 & 13.604 & 2 & 33 & \(<\)0.001 & \(-\)0.063 & 0.215 * \\ Heptanal & 0.699 & 7.120 & 2 & 33 & 0.003 & 0.060 & \(-\)0.079 ** \\
2-Heptenal & 0.434 & 21.488 & 2 & 33 & \(<\)0.001 & 0.107 ** & \(-\)0.107 \\
2,4-Hexadien & 0.488 & 17.326 & 2 & 33 & \(<\)0.001 & 0.096 ** & 0.093 \\
2,4-Hexadiene & 0.620 & 10.094 & 2 & 33 & \(<\)0.001 & 0.073 ** & 0.071 \\
3,5-Hexadien-1-ol & 0.778 & 4.703 & 2 & 33 & 0.016 & 0.007 & 0.179 ** \\ Hexanal & 0.588 & 11.554 & 2 & 33 & \(<\)0.001 & 0.081 ** & \(-\)0.024 \\
1-Hexanol & 0.753 & 5.410 & 2 & 33 & 0.009 & \(-\)0.003 & 0.194 ** \\
2-Hexenal & 0.566 & 12.657 & 2 & 33 & \(<\)0.001 & 0.077 & 0.128 ** \\
2,6-Nonadienal & 0.337 & 32.509 & 2 & 33 & \(<\)0.001 & 0.132 ** & 0.131 \\
2,6-Nonadien-1-ol & 0.558 & 13.067 & 2 & 33 & \(<\)0.001 & 0.085 ** & 0.057 \\ Nonanal & 0.429 & 21.969 & 2 & 33 & \(<\)0.001 & 0.105 & 0.139 ** \\
2-Nonen-1-ol & 0.493 & 16.955 & 2 & 33 & \(<\)0.001 & 0.086 & 0.170 ** \\
1-Octanol & 0.494 & 16.903 & 2 & 33 & \(<\)0.001 & 0.095 ** & 0.092 \\
2-Octanol & 0.207 & 63.348 & 2 & 33 & \(<\)0.001 & 0.190 ** & \(-\)0.056 \\ Pentanal & 0.549 & 13.541 & 2 & 33 & \(<\)0.001 & 0.085 ** & 0.082 \\ Pentanol & 0.645 & 9.079 & 2 & 33 & 0.001 & 0.071 ** & 0.044 \\ \hline \end{tabular}
\begin{tabular}{l} F: function value, df: degrees of freedom, \(p\): level of significance. Pooled within-groups correlations between discriminating variables and standardized canonical discriminant functions. Variables ordered by absolute size of correlation within function. ** Largest absolute correlation between each variable and any discriminant function. \\ \end{tabular}
\end{table} 
Table 4: Contribution of volatile compounds to the canonical discriminant function structure matrix.
## 7 Conclusions  
The optimization of the L-shaped bracket structure in the APTS (Acquisition, Pointing, and Tracking System) has demonstrated the effectiveness of topology optimization in achieving a lightweight and displacement-stable design. By applying the Solid Isotropic Material with Penalization (SIMP) method, the study successfully minimized structural compliance while adhering to volume, displacement, and stress constraints. The results indicate that topology optimization is a viable solution for improving mechanical support structures in high-performance applications, such as satellite communication and remote sensing systems. The key conclusions from the optimization process are as follows:  
**1. Volume Minimization with Displacement Constraints:**  
The optimized structure aimed at minimizing volume under displacement constraints achieved a weight reduction of 0.648 kg, with a maximum displacement of 0.04420 mm and a maximum von Mises stress of 11.81 MPa. This design met all functional requirements while significantly reducing material usage.  
**2. Volume Minimization with Displacement and Stress Constraints:**  
By introducing both displacement and stress constraints, the optimized structure further reduced weight to 0.635 kg, with a maximum displacement of 0.04718 mm and a maximum stress of 9.027 MPa. This solution achieved the best balance between structural stability and material efficiency, offering the lowest weight and lowest stress.  
3. Compliance Minimization with Volume Fraction Constraints:  
The compliance minimization approach, constrained by volume fraction, resulted in a structure with a weight of 0.655 kg and a maximum displacement of 0.04830 mm. Although the stress exceeded the optimization constraint at 12.98 MPa, the result is still relatively close, indicating that this approach could be further refined for stress control.  
Overall, the proposed methodology offers a robust and practical solution for optimizing mechanical support structures in APTS, enhancing performance while reducing material usage, making it highly suitable for engineering applications.  
**Author Contributions:** Investigation, Resources, Writing--original draft, B.G.; Conceptualization, Methodology, Investigation, Resources, Writing--original draft; H.Y.; supervision, W.C.; Project administration, H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by \"Light of the West\" Talent Training Plan (No. XAB2021 YN14), provided by the Chinese Academy of Sciences.  
**Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 2 Related Work
They stated that direct learning from the sound waveform matches the log-mel filter-bank performance. [PERSON] et al. (2017) [49] developed a method named between-class learning that mixture of two audio samples then trains neural networks to anticipate the mixing ratio of these samples. That method has improved efficiency for different architectures used for sound recognition tasks according to their experiments. Besides, they developed an end-to-endmethod that outperforms traditional learning techniques on various datasets using the between-class learning approach and 1D CNN. End-to-end applications using 1D CNNs are becoming common in signal processing because of their ability to benefit from its structure that can learn from waveforms directly [34; 50]. An end-to-end speech recognition approach based on waveforms and multi-scale convolutions that directly learn input data representation from signal was proposed by [PERSON] et al. (2017) [66]. They used several 1D convolution layers with different kernels to extract data features, and then to ensure consistent sampling frequency, they used a pooling layer to concatenate features. Their experiments reported an error rate of 23.28%. [PERSON] and [PERSON] developed another end-to-end technique for speech identification (2018) [44]. They tried to learn the (low and high) cut-off data frequencies of filters. Their model reduced the proposed model parameters by learning practical filters from the first layer. They achieved 0.85% as a sentence error rate based on the TIMIT dataset. In the same year, [PERSON] et al. [67] developed an end-to-end architecture using a 1D CNN with filter-banks without using mel-filter for speech recognition. Furthermore, [PERSON] et al. (2019) [47] proposed a sound classification method based on the 1D CNN. They used a gamma-tone filter bank for features generation. Their method was tested on the UrbanSound8k dataset with a classification accuracy of 89%. It was suggested that the hybrid model integrating two or more models could achieve high prediction accuracy [17]. A robust prediction can be achieved by combining several CNN networks that learn from waveform raw audio as one or two representations of the signal. [PERSON] et al. (2018) [23] combined two different networks; one learns the audio waveform directly, and the second using log-mel features to learn high-level representations from the waveform. Both models are independently trained, and the two model predictions are combined with the Dempster-Shafer method. An accuracy of 92.2% was achieved using this ensemble method on UrbanSound8k datasets with a 2% improvement in recognition accuracy compared to the current end-to-end model.
# Rheological Characteristics Evaluation of Bitumen Composites Containing Rock Asphalt and Diatomite
## 3 Experimental Methods
### Dynamic Shear Rheometer (DSR) Test  
The temperature and frequency sweep tests were conducted to evaluate the linear rheological characteristics of composite-modified asphalt with different RA and diatomite. The commonly obtained rheological indices from the DSR test were the dynamic shear modulus (G*), phase angle (\(\delta\)), and loss tangent (tan\(\delta\)). These indices were used to evaluate the viscosity and elasticity of the composite modified asphalt. The rutting factor (G*/sin\(\delta\)) was used to evaluate the anti-rutting ability of composite-modified asphalt as a high-temperature performance index, and the fatigue factor (G*-sin\(\delta\)) was used to evaluate the anti-fatigue performance. The asphalt samples were 25 mm in diameter and 1 mm in thinkness.