esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model  
### Mathematical Model of Transportation with the Subcontracting Strategy  
The idea is to minimize the cost of distribution, subcontracting and delay over a finite time horizon [0, H]. In each period, there are three scenarios. The first one stipulates that the concerned UDC can assume all deliveries. For the second scenario, the UDC cannot satisfy the total demand and will call at least one UDC to complete what is missing. The final scenario is that the available capacity of the main UDC and the subcontractor UDC cannot satisfy the total demand, and a part of the demand will be delayed. In this case, the choice of UDC depends on the available capacity; the cost of subcontracting based on the distance between two UDCs; and the environmental impact. The purpose of the subcontracting strategy is to satisfy the demands of customers in time and to avoid penalties. If a part of the demand cannot be satisfied in period \(t\), it must be rescheduled for the next period and pay penalties. We suppose that penalties are more expensive than the subcontracting cost of each UDC (with \(C_{t}<C_{tr_{i}}<C_{p}\)). We assume that the distribution system has knowledge of the nominal capacity of delivery by \(C_{t}\). This capacity will be reduced according to maintenance operations (corrective maintenance and/or preventive maintenance).  
The expression of the total cost of transportation, the cost of subcontracting (transportation and environmental cost) and the cost delay is expressed as follows:  
\[C_{d}=\min\Biggl{\{}\sum_{t}C_{t}\times x_{t}+\sum_{t}\sum_{i} \left(C_{tr_{i}}+C_{tp_{i}}\right)\times y_{it}\times AF_{it}+\sum_{t}C_{p} \times Q_{t}\Biggr{\}} \tag{1}\]  
The following constraint defines that the quantity delivered by the concerned UDC to the allocated urban area in period \(t\) cannot exceed the nominal available capacity of delivery in each period \(t\), and it must be positive.  
\[0\leq x_{t}\leq C_{t}\ \forall t\in H \tag{2}\]  
The quantity of subcontracted quantity for UDC \(i\) in period \(t\) cannot exceed a certain availability rate given by UDC \(i\) in period \(t\).  
\[0\leq y_{it}\leq Y_{it}\ \forall t\in H\ i\in N \tag{3}\]  
The following equation defines the value of delayed quantity as the difference between the updated demand and the amount of products delivered by the main UDC and the subcontractor UDC.  
\[Q_{t}=\max[0,D_{t}-x_{t}-\sum_{i}y_{it}\times AF_{it}]\ \ \forall t\in H \tag{4}\]  
Figure 1: Problem description; UDC (urban distribution center).  
This equation gives the value of refreshed demand in period \(t+1\) as the sum between the demand in period \(t+1\) and the delayed product in the previous period.  
\[D_{t+1}=d_{t+1}+Q_{t}\ \ \forall t\in H \tag{5}\]  
The following constraint defines that the main UDC can subcontract their deliveries with many other UDC at most \(L\) UDC in each period \(t\).  
\[\sum_{i}AF_{it}\leq L\ \ \forall t\in H \tag{6}\]  
This equation defines binary variables.  
\[AF_{it}\in\{0,1\} \tag{7}\]  
This equation defines integer variables  
\[Q_{t},x_{t},y_{it}\ \text{integers} \tag{8}\]
# Effects of Growth Parameters on the Analysis of _Aspergillus flavus_ Volatile Metabolites
## 3 Results and Discussion
### Effect of the Concentration of Spore Suspension on MVOCs' Production  
Traditional fungal inoculation and subculture methods are quite simple. A small piece of fungal culture is removed with a sterile blade and then transferred to the surface of the growth medium. For _A. flavus_, it typically takes seven days for the fungi to cover the entire agar surface of the 100 mm diameter petri dish. In this study, differing spore suspensions were precisely prepared. Using these suspensions, a controlled spore dose could be added to the growth media in order to elucidate the relationship between dose and MVOC production.  
Spores of _A. flavus_ were collected with a Tween 20 solution, diluted as needed with water and counted using a hemocytometer. Then, 10 \(\mu\)L of a specific concentration of the spore suspension were injected on the malt extract agar (MEA) media surface before the standard seven days of incubation at 30 \({}^{\circ}\)C. CAR/PDMS fiber was used to extract MVOCs for 5 h at 30 \({}^{\circ}\)C. Four concentrations of the spore suspension were used, including a high concentration (1.2 \(\times\) 10\({}^{7}\) spores/mL), medium concentration (2.4 \(\times\) 10\({}^{6}\) spores/mL) and low concentration (4.8 \(\times\) 10\({}^{5}\) spores/mL). A toothpick inoculation method was also used for comparison.  
The peak areas of 15 commonly-detected MVOCs emitted by _A. flavus_ 5-3B were selected to compare the different spore doses (Table 5). No consistent trends were observed that correlate spore count with specific MVOC peak ion counts.
## 3 Results and Discussion
### Galwanostatic and Potentiostatic Deposition  
In an initial series of experiments, the electrophoretic deposition and in situ co-crosslinking of GOD and BSA onto the electrode surface were investigated at a constant current, which was ensured using the galvanostatic technique. The chemical conditions were those already optimized above, while several constant current densities, in the range of 0.5 \(\div\) 3 mA/cm\({}^{2}\), were investigated for protein deposition. In this context, Figure S1 shows the relevant chronopotentiometric curves observed in these experiments, while Figure 2 shows the potential attained at the deposition electrode for each performed constant-current experiment.  
In all cases, notable dioxygen evolution at the deposition electrode was evident, while a foamy coating was observed only at the higher applied current densities; unfortunately, in the latter conditions, the degree of dioxygen bubbling was so strong that it quickly removed any newly formed coating. Indeed, as reported in Figure 2, for each applied current, thepotential at the deposition electrode quickly reached values high enough, even at the lowest current densities, to promote water oxidation and dioxygen bubbling, ruining any protein deposition. These findings strictly correspond with similar side effects observed in metal electroplating [21] and agree with those already pointed out elsewhere [17]; it is worth noting that the use of the galvanostatic deposition technique has already been attempted for EPD deposition [7; 8; 9; 10], but these side effects were unexpected and have never been reported.  
Similar results were observed even for potentiostatic (i.e., constant potential) deposition experiments (not shown here), so the constant current and potential deposition approaches were abandoned.
## 2 Materials and Methods
### Illumina MisSeq Sequencing and Processing of Sequencing Data  
Purified amplicons were pooled in equimolar and paired-end sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, CA, USA), according to the standard protocols by Majority Bio-Pharm Technology Co. Ltd. (Shanghai, China).  
The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by fastp version 0.20.0 [18] and merged by FLASH version 1.2.7 [19] with the following criteria: (a) Over the 50 bp sliding window, 300 bp readings were truncated at any site receiving an average quality score of <20. Truncated readings <50 bp and readings containing ambiguous characters were discarded; (b) Only overlapping sequences longer than 10 bp were assembled; (c) Primers were completely matched, and two nucleotides were allowed to be mismatched, and readings containing ambiguous bases were deleted. Operational taxonomic units (OTUs) with a 97% similarity cutoff [20; 21] were clustered using UPARSE version 7.0.1090 ([[http://drive5.com/uparse/](http://drive5.com/uparse/)]([http://drive5.com/uparse/](http://drive5.com/uparse/)), accessed on 14 April 2022) [20], and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed by RDP Classifier version 2.2 [22] against the 16S rRNA database using a confidence threshold of 70%.
## 3 Results
### Classical Virulence and Resistance Proteins  
We identified several virulence, adhesion, immune evasion, and resistance-associated factors; Table 1 illustrates a heatmap reflecting their abundances (LFQ intensities) and Table S4 shows the fold-changes for the same proteins between the indicated growth modes and time points. The highest LFQ intensities, indicating high protein abundances, ranged between 30 and 35 (log\({}_{2}\)) and were detected for the immunoglobulin binding protein SpA in PL8 h and SDBF_24 h cells and an NLPA-type ABC transporter (lipoprotein) specific for methionine in SDBF_24 h cells. A decreasing trend in abundances of known virulence factors or adhesins (e.g., coagulase, SasF, Emp, immunoglobulin binding proteins Sbi, SdrD, SdrE-truncated, SdrC, clumping factors Clfa/B, autolysins Sle1, and Atl) was observed over time for both the planktonic and biofilm cells. The greatest decrease was found for Sbi and the truncated form of SdrE. Proteins contributing to peptidoglycan synthesis/resistance such as FemA and FemB were increasingly produced with time, while an UDP-N-acetylmuramoyl-l-alanine-p-glutamate ligase (MurD) displayed a temporal increase only on planktonic cultures at the 26-h, 78-h, and PL96-h time points.  
Comparison of the EDBF_6 h and corresponding PL surfaceomes at the 8-h time point revealed that the immunoglobulin binding protein--Sbi, accumulation associated--ClfB, immunodominant antigen--IsaA, and the adhesins--SrdD/E had 30- to 60-fold higher abundances of EDBF when compared to PL cells (Table S4). The fully matured SDBF_24 h culture and the equivalent PL cells at the 79-h time point were proposed to contain the highest number of persister cells. Comparison of the SD-biofilms at the 6-h time point to the planktonic cells at a comparable time point (PL78 h) indicated \(>\)30-times higher abundance for the immuno evasion protein--IsaB and 7-18-fold higher abundances  
Figure 3: (**A**) Heatmap of row-wise standardized protein abundances (complete linkage, Lp norm; \(p\) = 1.5) with eleven clusters (distance threshold set at 50). Protein clusters with similar trends in specificity are circled. (**B**) 3D-PCA comparing protein abundance patterns with respect to their predicted secretion modes (predicted moonlighters are circled).  
for IsaA, HlgA, and SdrD/E adhesins. Greatest differences among the EDBF/SDBF_24 h- and PL 26 h/96 h-surfaces were detected between the SDBF_24 h and PL96 h cells; HlgA, ClifA/B, IsaB, YPEB-like peptidase, ABC transporter CntA displayed 6-13-fold higher abundances on SDBF cells than on PL cells. Thus, these abundance changes suggest that _S. aureus_ decreases cell wall modification enzymes over time during both growth modes, whereas in biofilm cells specific virulence, immune evasion, persistence, and/or adhesion-related proteins are produced more with increasing age.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 1 Introduction  
As a type of diffusion multiphase flow, particle-laden gas flows are a common phenomenon in nature. They also have been studied in many engineering contexts, where a gas is the continuous phase, and solid or liquid particles form the dispersed phase, such as in pneumatic transmission of coal powder along a fluidized bed [1]. Particle-laden flow is often confined to a dispersed phase having low concentrations, and so far, most of the experimental technology is designed for measuring particles at relatively low number density. Its corresponding numerical simulation involves a one-way coupling method, which is very effective for these flow types. However, as the fractional volume occupied by the dispersed phase increases, the influence of the dispersed phase on the carrier phase and interactions between particles should be considered. Furthermore, when the particle concentration is at extreme levels, fluid dynamics also cannot be neglected. Such two-phase flow is called granular flow and includes examples of aggregated particles flowing down a steep slope. This article focuses on flows suitable for the one-way coupling method, in which dispersed particles range from several nanometers to dozens of micrometers in size.
# A Model-Driven Approach for Conducting Simulation Experiments
## 3 Model-Driven Approach for Conducting Simulation Experiments
### Framework Overview  
Our approach for supporting the conducting of simulation experiments is based on the MDE principle. The central idea of MDE is combining meta models (i.e., a structured representation of concepts) with a means for code generation [36]. Typically, MDE is realized in an architecture with four levels of abstraction and translations between them [37]. Figure 2 illustrates our four-level MDE approach for simulation experiments. Experiment _meta models_ of the various domains and approaches of modeling and simulation are represented by the level \(M_{2}\). In addition to the definition of these so-called _base experiments_, meta models for a variety of _experiment types_ (e.g., sensitivity analysis (SA) or simulation-based optimization) exist. These two types of meta models can be composed flexibly to create meta models of complex simulation experiments. How meta models can be constructed is defined at the level of the _meta meta model_ (\(M_{3}\)), for example one could use formalisms such as UML class diagrams [38] or schema languages [39; 40] to express the meta models. However, experiment meta models are not only developed from scratch; therefore, we store them in a repository from which parts can be reused by related areas of simulation or by other experiment types. The newly developed meta models are again stored in the repository and thereby complement the knowledge collection about simulation experiments. A composed meta model (consisting of a base experiment and the experiment type) can be loaded via an interface (GUI or CLI). The loaded meta model dictates which inputs have to be provided to specify a valid simulation experiment. An experiment validator provides guidance for the modeler. If all inputs required by the meta model have been collected, a concrete tool-independent experiment specification (_model_\(M_{1}\)) is generated. This general representation of the simulation experiment can now be easily reused, and some tools might also be able to import this specification directly. To receive an executable experiment _instance_ (\(M_{0}\)), the \(M_{1}\)-specification is automatically transformed to the code of a target backend and subsequently executed using the respective tool binding. In the following, we will describe the distinct features of our framework further.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 4 Experiment  
### Dataset and Evaluation Metrics  
Experiments are performed on the WHAM! [24] dataset. WHAM! was originally designed for speech separation in noisy environments. It pairs each two-speaker mixture in the wsj0-2 mix [25] dataset with a real-world noise. The noise was recorded in urban environments, such as coffee shops, restaurants, bars, office buildings, parks, etc. [24]. Meanwhile, WHAM! also provides a version for speech enhancement, where only the speech of the first speaker is mixed with the noise at SNRs randomly sampled between \(-6\) and \(+3\) dB. The training set, development set, and test set contain 20,000, 5000, and 3000 utterances, respectively. The training and development sets share common speakers, but the test set speakers are different. In order to evaluate the generalizability of the proposed model, we further adopt the one-channel test set from CHiME-4 [26] for evaluation. Both the simulated and the real-world noisy utterances are used.  
Four widely used objective metrics are used to evaluate the enhanced speech, namely narrow-band Perceptual Evaluation of Speech Quality (PESQ-NB) [27], Short-Time Objective Intelligibility (STOI) [28], scale-invariant Source-to-Noise Ratio (SI-SNR) [29] and Signal-to-Distortion Ratio (SDR) [30]. All of these metrics are the larger the better. The Word Error Rate (WER) is used to indicate the performance of the enhanced speech on the ASR task.
## 7 Taxonomy of Methods for ES Development in ECG Interpretation
### Big Data-Based Methods  
Current studies are focusing on the direction of designing a cloud-based big data framework for automatic and real-time interpretation of aggregated heterogenous ECG signals from diverse sources. Big data analytics are employed to provide the processing capabilities to handle this large volume of data. [PERSON], [PERSON] [88] presented state-of-the-art big data tools for dealing with multimodal data sources in the Intelligent Medical Platform (IMP) including ECG data. The IMP support data reader was used to provide a continuous real-time response for data analysis and visualization of multimodal data extracted from the nonvolatile storage based on queries from Apache Hive. To evaluate the performance of the IMP data acquisition and persistence platform, stress testing based on ECG signals was carried out with 500 data sources, as the number of devices increased incrementally several times. The results showed that the implementation, even with an increased number of devices, has been able to scale successfully in dealing with health data without losing performance effectiveness as revealed by stable rate of query performance as the number of data sources and packets increase. Similarly, [PERSON], [PERSON] [90] proposed a cloud-based framework for fast real-time analysis of heterogenous ECG and electroencephalogram (EEG) data streamed from smart biomedical sensors and smartphones. The framework was designed by combining adaptive signal acquisition, segmentation and denoising to optimize the signal transmission rate. Feature extraction was carried out using AR Burg model. Assessment of ECG and EEG signals was achieved by utilizing various machine learning algorithms. With this framework, the total computational power gained per class for the considered ECG and EEG signals are twice and four times better, respectively, than the classical approaches.  
**Findings:** Recent developments in telemedicine and cloud computing have guided researchers' focus on the application of big data analytics to cloud-based ECG data analysis and interpretation. From our study, it is found that researchers are now creating tools and frameworks that would allow real-time processing of multimodal, heterogenous and dynamic cloud-based big ECG data gathered from smart IoT devices and systems for automatic remote interpretation. Although the field is still developing, evidence from available research indicated the scalability and robustness of the big data analytics method.
# A Wideband Non-Stationary 3D GBSM for HAP-MIMO Communication Systems at Millimeter-Wave Bands
###### Abstract  
High-altitude platforms (HAPs) are considered to be the most important equipment for next-generation wireless communication technologies. In this paper, we investigate the channel characteristics under the configurations of massive multiple-input multiple-output (MIMO) space and large bandwidth at millimeter-wave (mmWave) bands, along with the moving essence of the HAP and ground terminals. A non-stationary three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for a HAP communication system. We use a cylinder-based geometric modeling method to construct the channel and derive the channel impulse response (CIR). Additionally, the birth-death process of the scatterers is enclosed using the Markov process. Large-scale parameters such as free space loss and rainfall attenuation are also taken into consideration. Due to the relative motion between HAP and ground terminals, the massive MIMO space, and the wide bandwidth in the mmWave band, the channel characteristics of HAP exhibit non-stationarities in time, space, and frequency domains. By deriving the temporal auto-correlation function (ACF), we explore the non-stationarity in the time domain and the impact of various parameters on the correlations across the HAP-MIMO channels. The spatial cross-correlation function (CCF) for massive MIMO scenarios, and the frequency correlation function (FCF) in the mmWave bands are also considered. Moreover, we conduct simulation research using MATLAB. Simulation results show that the theoretical results align well with the simulation results, and this highlights the fact that the constructed 3D GBSM can characterize the non-stationary characteristics of HAP-MIMO channels across the time, space, and frequency domains.  
keywords: geometry-based stochastic models (GBSM); high-altitude platforms (HAP); Markov process; massive multiple-input multiple-output (MIMO); millimeter wave (mmWave); non-stationary +
Footnote †: journal: Electronics Letters
## 6 Conclusions  
The contribution of this work was to characterize the air-ground channel used by a drone in three scenarios. Both small-scale and large-scale fading were estimated. Regarding large-scale fading, path loss and shadowing were characterized. At lower heights, the path loss presented a negative exponent, while a positive exponent was observed for higher heights. Shadowing parameters (mean and standard deviation) were also presented. We observed higher standard deviation for lower height flights. Thus, results show more severe long-scale fading for air-to-ground communication at lower altitudes, even over a lake surface.  
We also present a small-scale fading study focused on comparing the theoretical and empirical CDFs of the collected samples. The results showed a higher variation in small-scale fading in the flight with the lower speed and lower height. We speculate that this result is related to the fact that the aircraft has less control of speed correction when it is affected by gusts of wind. The KS test shows that the Weibull distribution best describes the channel at a significance level of 95%.  
Finally, LCRs were used to estimate the Doppler frequency and drone speed. Even if the aircraft has its average speed strongly affected by wind, the expected results of the Doppler frequency were observed within the theoretical range. Flights over the lake at 3 km/h and a height of 8 m show better discrimination regarding speed and Doppler frequency estimations. This specific flight experiences a higher speed, because there was a barrier of vegetation on one of the lake's edges.  
It is worth noting that flights at low altitudes close to water presented a high path loss exponent, which can hinder the applications in this scenario. Such an effect should be better analyzed in order to comprehend the influence of the water surface on UAV communication channels at low altitude, as there is a lack of works in this subject area. Thus, we intend to investigate this in a future work.  
Conceptualization, D.L.L., V.A.d.S.J. and A.A.M.d.M.; Data curation, D.L.L.; Formal Analysis, D.L.L., V.A.d.S.J. and A.A.M.d.M.; Supervision, D.L.L., P.J.A. and A.A.M.d.M.; Visualization, D.L.L.; Writing--original draft, D.L.L.; Writing--reviewing & editing, D.L.L., P.J.A., M.M.d.M.C., V.A.d.S.J. and A.A.M.d.M. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The proof of concept simulations provided by this paper was supported by the High Performance Computing Center at UFRN (NPAD/UFRN).  
The authors declare that they have no competing interests.