esa-sceva/satcom-chunk-collection huggingface dataset:
# Ribosomal Hiberation Factor Links Quorum-Sensing to Acid Resistance in EHEC
## 3 Results
### Ribosomal Hibenation Factor RMF Governs Cross-Stress Adaptation in EHEC  
Functional dissection established RMF as a master regulator of stress adaptation. The successful deletion of _rmf_ in EHEC was verified by agarose gel electrophoresis and sequencing (Figure S1). Targeted control of acid defense: Under pH 2.5 for 2 h, _\(\Delta\)rmf_ showed a significant downregulation of core acid resistance genes: _gadA_, _gadC_, _gadW_, _gadE_, _hdeA_, _hdeB_, and _adiaA_. All defects were fully rescued by genetic complementation (Figure 3A,B).  
accompanied by 4.8-fold accelerated death kinetics (\(-\)0.82 day\({}^{-1}\) vs. WT \(-\)0.17 day\({}^{-1}\)) (Figure 3E). In thermal stress, \(\Delta\)_rmf_ viability was reduced 7.0-fold after 3 h at 50 \({}^{\circ}\)C (Figure S2C). In oxidative stress, based on dose-response profiling (Figure S2A), 5 \(\upmu\)M H\({}_{2}\)O\({}_{2}\) was selected for subsequent oxidative stress assays, as it induced \(\approx\)50% reduction in stationary-phase biomass (OD\({}_{600}\) = 2.1 \(\pm\) 0.2 vs. control 4.0 \(\pm\) 0.3) without causing rapid lethality (<10% viability loss at 4 h). Under 5 mM H\({}_{2}\)O\({}_{2}\), \(\Delta\)_rmf_ biomass decreased by 50% at 9 h, partially rescued to 89% of WT levels (Figure S2B). In cryostress, \(\Delta\)_rmf_ survival dropped 6.8-fold after 5 days at \(-\)20 \({}^{\circ}\)C (Figure S2D).  
All stress phenotypes were fully or partially reversed by _rmf_ complementation. Crucially, the 13.7-fold viability deficit during starvation (exceeding acid/heat stress impacts) and 4.8-fold accelerated death kinetics position RMF as a primary guardian against energy depletion crises.
# Energy Efficient Communications for Reliable IoT Multicast 5G/Satellite Services
## 3 Network Coding
In addition to this, a comparative performance analysis is provided with respect to a classical negative acknowledged (NACKed) scheme, as depicted in Figure 1b, for a reliable multicast satellite communication scheme protocol relying on feedback channels (typically, the DVB-RCS/DVB-RCS2 systems), as well as with the classical NC scheme already investigated in [24], where no transmission rate optimization is performed. Recently, NC is combined with Multipath-TCP (MPTCP), which exploits multiple TCP connections using different paths, to protect TCP transmissions from packet losses and improve user's throughput, as explained in Figure 1d. Differently from analytical and simulative approaches, [PERSON] et al. addressed the integration of NC in a real protocol stack, taking as reference the DVB-S2/RCS2 architecture [26]. The specific implemented NC is positioned between network and the datalink layers and an emulator for the integration and validation of the NC applications in DVB-S2/RCS2 is also shown in the paper.
## 2 Materials and Methods
### Structure of the Microsurfacing Mix Design Method Proposed in This Work  
The present work proposes a new procedure for a microsurfacing mix design based on tests developed according to a set of European Standards for slurry surfacing. Figure 1 presents the flowchart of the mix design procedure proposed in this work.  
In the first step, two preliminary tests, developed by the RILEM TC 280-CBE, were carried out to determine the cement and added water content. Subsequently, the consistency test (EN 12274-3) was performed to confirm the amount of added water in the mixture. Finally, the cohesion test (EN 12274-4) was performed to identify the ideal relationship between the amount of added water, cement, and emulsion. In the end, the shaking abrasion test (EN 12274-7) was performed to determine the water sensitivity of the mixtures.  
The present work produced two mixtures, one with Emulsion 1 and one with Emulsion 2, for each test. Thus, the microsurfacing mixtures were always produced using the same type of aggregate and cement, varying the amounts of the asphalt emulsion, added water,and cement. For mixture preparation, the dry aggregate amount was considered a reference for the amount of asphalt emulsion (typically between 10% and 15%), added water (typically around 10%), and cement (typically between 2% and 3%), i.e., the percentage of these materials in each mixture was always defined considering the mass of aggregate as 100%.
## 3 Results
### Physicochemical Parameters
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Paryameter**} & \multicolumn{3}{c}{**Type of Liqueur**} \\ \cline{2-4}  & **LX** & **LS** & **LCS** \\ \hline pH [-] & 3.71 \(\pm\) 0.02 * & 3.72 \(\pm\) 0.02 * & 3.72 \(\pm\) 0.02 * \\ \hline Total acidity & 2.46 \(\pm\) 0.07 * & 2.76 \(\pm\) 0.06 ** & 2.43 \(\pm\) 0.02 * \\ \hline Density [\({}^{\circ}\)Blg] & 6.17 \(\pm\) 0.03 * & \textless{5 **} & 8.6 \(\pm\) 0.00 *** \\ \hline TSS [\({}^{\circ}\)Bx] & 21.97 \(\pm\) 0.06 * & 13.67 \(\pm\) 0.15 ** & 23.03 \(\pm\) 0.25 *** \\ \hline Ethanol [\%] & 36.45 \(\pm\) 3.54 * & 38.79 \(\pm\) 2.81 * & 40.83 \(\pm\) 2.81 * \\ \hline DPPH [\(\mu\)mol Tx/100 mL] & 367.88 \(\pm\) 5.17 * & 360.26 \(\pm\) 15.09 * & 348.8 \(\pm\) 10.93 * \\ \hline ABTS [\(\mu\)mol Tx/100 mL] & 1081.88 \(\pm\) 196.62 * & 1188.13 \(\pm\) 186.23 * & 1238.13 \(\pm\) 181.57 * \\ \hline FRAP & 653.20 \(\pm\) 42.75 * & 594.20 \(\pm\) 38.31 * & 598.20 \(\pm\) 51.00 * \\ \hline
\begin{tabular}{c} Polyphenols \\ [mg gallic acid/100 mL] \\ \end{tabular} & 95.36 \(\pm\) 1.99 * & 92.93 \(\pm\) 2.10 * & 109.16 \(\pm\) 1.96 ** \\ \hline \multirow{4}{*}{Color} & L* & 23.82 \(\pm\) 0.12 * & 23.41 \(\pm\) 0.01 ** & 24.00 \(\pm\) 0.03 *** \\ \cline{2-4}  & a* & 7.86 \(\pm\) 0.08 * & 7.09 \(\pm\) 0.3 ** & 7.18 \(\pm\) 0.12 ** \\ \cline{2-4}  & b* & \(-\)9.24 \(\pm\) 0.02 * & \(-\)9.44 \(\pm\) 0.02 ** & \(-\)9.20 \(\pm\) 0.01 *** \\ \cline{2-4}  & C* & 12.13 \(\pm\) 0.06 * & 11.80 \(\pm\) 0.03 ** & 11.67 \(\pm\) 0.07 ** \\ \cline{2-4}  & h\({}^{\circ}\) & 310.33 \(\pm\) 0.31 * & 306.80 \(\pm\) 0.10 ** & 307.93 \(\pm\) 0.50 *** \\ \hline \end{tabular}  
* * ** ***–differences between results denoted with different symbols in the same row are statistically significant (\(p\leq\) 0.05).  
\end{table} 
Table 1: Physicochemical parameters of liqueurs made of bilberry and highbush blueberry with xylitol (LX), stevia (LS), and cane sugar (LCS) as sweeteners. xylitol, and the difference observed compared to the two other linequers was statistically significant. The contribution of blue color was the highest in the liqueur produced with stevia. It was similar in the two remaining liqueur variants; however, the differences noted between individual variants were statistically significant (Table 1).
# LSH Models in Federated Recommendation
## 4 Comparative Analysis of Experiment
### Evaluation Metrics  
For the traditional Matrix Factorization (MF) model, the Root Mean Square Error (_RMSE_) is used as the evaluation index, and the smaller the _RMSE_, the more accurate the prediction result. The _RMSE_ is as follows:  
\[RMSE=\frac{\sqrt{\sum\limits_{R}(r_{ui}-\widetilde{r}_{ui})^{2}}}{|R|} \tag{8}\]  
where \(|R|\) denotes the number of valid ratings in the dataset.  
For the NCF model, the prediction output is a list of recommended rankings. This paper employs Leave-One-Out Cross-Validation (LOOCV) as the evaluation metric. The rankings are determined based on the ratings, and these rankings are then used to calculate the Hit Rate (HR) for positive samples. For instance, with a Top-K value of 10, the top ten recommendations are examined to identify the presence of positive samples. If a positive sample is found, it is considered a hit; otherwise, it is classified as a miss. A higher HR value indicates a more accurate recommendation performance. Additionally, based on the ranking outcomes, the Normalized Discounted Cumulative Gain (_NDCG_) is computed to assess the predictive effectiveness. A higher _NDCG_ value signifies an enhanced recommendation performance. The _NDCG_ is as follows:  
\[NDCG(k)=Z_{k}\sum\limits_{j=1}^{k}\frac{2^{r(j)}-1}{\log_{2}(j+1)} \tag{9}\]  
where \(k\) represents the number of top \(k\); Z\({}_{k}\) is the normalization parameter.  
The \(F_{1}\) score is the harmonic average of _Precision_ and _Recall_, and it is particularly useful in binary classification problems. When the positive and negative sample categories in the dataset are not balanced, the \(F_{1}\) score can evaluate the model performance more comprehensively, because it considers the effects of both false positive (False Positives, _FP_) and false negative (False Negatives, _FN_). The \(F_{1}\) score is calculated as follows:  
\[F_{1}=\frac{2\cdot Precision\cdot Recall}{Precision+Recall} \tag{10}\]  
where _Precision_ represents the proportion of all samples predicted to be positive, using the formula \(Precision=\frac{TP}{TP+FP}\). _Recall_ represents the proportion of all samples that are actually positive, according to the formula \(Recall=\frac{TP}{TP+FN}\). _TP_ is the number of true cases, the number the model correctly predicts as a positive class.  
\(F_{1}\) scores range from 0 to 1, with higher values indicating better performance of the model. When the \(F_{1}\) score approaches 1, the representation model achieves a good balance between _Precision_ and _Recall_.
## 2 Principles and Methods
### Hierarchical Frequency Fusion
#### 2.2.1 Theoretical Analysis
For station pairings that are far apart, the correlation coefficient curve's first monotonically falling interval is narrow and has an excessive number of oscillatory attenuation bands. When the frequency is higher than 6 Hz, the reliability falls. The correlation coefficient curve of the small spacing station pair has a more pronounced downward tendency overall in the high-frequency band. Furthermore, its effective frequency band range is broader. However, there is minimal change in its correlation coefficient in the low-frequency range of 1 to 4 Hz. The shape of the coherence coefficient in this band is different from the Bessel function curve, and it is less reliable. A great deal of studies and practical applications have shown that the distance between stations affects the depth of the microtremor survey [26; 27]. As surface waves propagate at shallow ground surfaces, dispersion and attenuation features cause the energy of high-frequency signals to decay more quickly, penetrate to small depths, and have high resolution of shallow velocity patterns. Low-frequency signals decay slowly, travel long distances, penetrate to enormous depths, and contain more information about the deeper medium. Pairs with a small station spacing record signals with a predominance of high-frequency components during station array observations, whereas pairings with large station spacing can capture more long-wavelength signals. The S-wave velocity structure of subsurface media at different depths can be inferred from surface waves of different frequencies [28]. The  
Figure 3: Correlation coefficient curves and zero-order first-class Bessel function curve. (**a**) Correlation coefficient curves; (**b**) \(J_{0}\): Zero-order first-class Bessel function curve. dispersion curves of various station spacing can be fused in bands according to the spacing size by combining the impact of station pair spacing on the form of the correlation coefficient curve. The frequency travels from high to low, the level goes from shallow to deep, and the sub-bands are fused to achieve a sharper dispersion curve and geological structure.
# Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin
###### Abstract  
In this work, numerical simulations are conducted to reveal the hydrodynamic mechanism of caudal fin propulsion. In the modeling of a bionic caudal fin, a universal kinematics model with three degrees of freedom is adopted and the flexible deformation in the spanwise direction is considered. Navier-Stokes equations are used to solve the unsteady fluid flow and dynamic mesh method is applied to track the locomotion. The force coefficients, torque coefficient, and flow field characteristics are extracted and analyzed. Then the thrust efficiency is calculated. In order to verify validity and feasibility of the algorithm, hydrodynamic performance of flapping foil is analyzed. The present results of flapping foil compare well with those in experimental researches. After that, the influences of amplitude of angle of attack, amplitude of heave motion, Strouhal number, and spanwise flexibility are analyzed. The results show that, the performance can be improved by adjusting the motion and flexibility parameters. The spanwise flexibility of caudal fin can increase thrust force with high propulsive efficiency.  
biological fluid dynamics; numerical simulation; caudal fin propulsion; spanwise flexibility Article
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 4 Linearization of the Scanning Function
### The Positioning of the Two Mirrors of the Device  
One can observe from Figure 9 that the system does not use the entire lengths of the two mirrors \(\mathrm{M}_{1}\) and \(\mathrm{M}_{2}\).  
To determine their useful lengths and the way they must be positioned, the construction in Figure 9b is used, to refer to the situation when all possible reflections on the two mirrors are included on the utilized portions.  
Let the position of the most distant incidence point \(\mathrm{I}_{1}\) (with regard to the point C) on \(\mathrm{M}_{1}\) be \(\mathrm{CI}_{1}=p(\mathrm{I}_{1})=p\). The minimum distance from C to an incidence point on \(\mathrm{M}_{1}\) is for the incidence point \(\mathrm{I}_{k+1}\), and it is equal to \(p_{\mathrm{min}}=\mathrm{CI}_{k+1}=\mathrm{CI}_{k+1}\). From the sine theorem in triangle \(\mathrm{I}_{1}\mathrm{CI}_{k+1}\) is  
\[p_{min}=p\frac{\cos\xi}{\cos(2\psi k-\xi)}=p(I_{k+1}), \tag{40}\]  
where \(k\) is provided by Equation (39). While this is, in all situations, the minimum distance from C to an incidence point, the minimum distance from C to an incidence point on the other mirror is  
\[\min\{p(I_{k}),\ p(I_{k+2})\}=\min\left\{p\frac{\cos\xi}{\cos(2\psi(k-1)-\xi)},p\frac{\cos\xi}{\cos(2\psi(k+1)-\xi)}\right\}, \tag{41}\]  
On the other hand, the maximum distance from C to an incidence point is not necessarily \(p(\mathrm{I}_{1})=p\), but \[p_{\max}=\max\{p\big{(}I_{j}\big{)},\ p(I_{N})\}=\max\bigg{\{}p\frac{\cos\xi}{\cos(2 \psi(j-1)-\xi)},p\frac{\cos\xi}{\cos(2\psi(N-1)-\xi)}\bigg{\}},\ \text{where}\ j=1\ \text{or}\ 2. \tag{42}\]  
In conclusion, the two mirrors M\({}_{1}\) and M\({}_{2}\) must have portions corresponding to the above distances/polar coordinates from their intersection point C. In the example in Figure 9b, I\({}_{1}\) is on M\({}_{1}\), I\({}_{k+1}\) is on M\({}_{2}\), and I\({}_{N}\) is on M\({}_{2}\), therefore the useful portion of the mirror M\({}_{1}\) is between \(\min\{p(I_{k}),\ p(I_{k+2})\}\), Equation (41), and \(p=p(\text{I}_{1})\), while the useful portion of the mirror M\({}_{2}\) is between \(p_{\min}=p_{k+1}\), Equation (40), and \(p_{\max}\), Equation (42).
## 3 Materials and Methods
### Node  
In this subsection, the employed node and its configuration are detailed. To collect the data from all sensors, we have selected a compatible Arduino Mega 2560 module. This microprocessor board has 54 digital input/output pins, 16 analog inputs, 4 UARTs (hardware serial ports), 16 MHz crystal oscillator, USB connection, power jack, In-Circuit Serial Programming (ICSP) connector and reset button. In addition, this compatible Arduino Mega module can be supplied from our PC by means of a USB cable or by an external power supply (9 up to 12 VDC). However, one of the main drawbacks of this board is that it does not have any type of wireless interface to integrate it in a WSN and the storage capacity of data from the sensors is limited. To solve this problem, we use a Wi-Fi module ESP8266 ESP-01 [50] and a microSD card reader which will be connected to our microprocessor module as Figure 9 shows.  
Another important issue to deal when implementing the module is how to collect the signals from the different LDRs in the node of BN1. In this node the 27 used LDRs must be connected. Moreover, the sensors of humidity, water level, workers presence and illumination are connected too. This issue does not appear in the other nodes. To this end, we select an analog 16 \(\times\) 1 multiplexer (MUX). In this case, the Multiplexer 74 HC4067 [51] will be chosen. Because we need to collect data from 27 LDRs, it is needed to use two 16 \(\times\) 1 MUX. Finally, to join the signals from both, we will use a new 2 \(\times\) 1 MUX [52]. Whit this, we will be able to gather data from 30 analog devices.  
The resulting operation of this combination of multiplexers works as follows. On the one hand, we have a series of input signals, in our case 32 analog inputs (from AI0 to AI32), which are controlled by the control signals (from C0 to C5). As a function of the control signals, we will select an input signal to drive it to the output. Finally, the STROBE signal enables or disables the multiplexing.
# Arabic Automatic Speech Recognition: A Systematic Literature Review
## 3 Method
### Research Questions (RQ)  
The first step of this SLR consists of defining the research questions and Secondary Research Questions (SRQ). As presented earlier, the purpose is to review the Arabic ASR studies conducted between 2011 and 2021. A total of seven RQs and three SRQs were defined to carry out a detailed review of the field. The RQs and SRQs related to the purposes are as follows:  
RQ 1. What is the bibliographic information of the existing studies?  
* SRQ 1.1. What are the most active countries?
* SRQ 1.2. How has the number of studies evolved across the years?
* SRQ 1.3. What are the types of venues (i.e., journals, conferences, or workshops) used by the authors of studies?  
RQ 2. What is the considered variant of Arabic in speech recognition studies?  
RQ 3. What are the toolkits most often used in the Arabic speech recognition field?  
RQ 4. Which datasets were most often used, and types of Arabic speech recognition were identified in these datasets?  
RQ 5. What are the used feature extraction and classification techniques for Arabic speech recognition studies?  
RQ 6. What are the current gaps and future research in the Arabic ASR field?  
Figure 2: PRISMA flow chart of the study selection.  
RQ 7. What is the performance of Arabic recognition systems in terms of accuracy rate or WER?
# A Simple Method to Estimate Weed Control Threshold by Using RGB Images from Drones
## 1 Introduction
Moreover, the availability of UAVs able to carry increasing payloads allowed users to broaden the range of possible sensors (thermal, visible, multispectral and hyperspectral cameras) and to select the best solution to investigate all possible stressful conditions due to biotic (diseases, pests and weeds) and abiotic (drought, nutritional deficiencies and extreme temperatures) causes [27]. Coupled with UAV technology, sophisticated image data analysis tools have been developed. There is a lot of software available that has proved to be reliable in estimating green canopy cover from aerial images of fields [28], however, their use has been mainly devoted to monitoring crop behaviour (health, growth and productivity) [29], rather than to estimate weed development. The reason for this is that these techniques have not yet reached a proper level to make their use viable in usual farming practices because of the difficulties in separating weed from crop cover and even more in discriminating between different weed species [30]. For this purpose, object-based image analysis (OBIA) techniques, based on the integration of radiometric (reflectance), visual (texture, contrast, and shape) and spatial (position and height in the field) information, have been proposed to improve the discriminatory power [31; 32]. Unavoidably, these techniques are more expensive [33], with an estimated additional cost of $28 ha\({}^{-1}\), and demanding in terms of know-how required. Costs and complexity can often discourage farmers who are forced to choose more traditional and error-prone scouting techniques to determine their economic threshold of intervention. This work aims to propose a simple method to estimate weed economic threshold which can overcome these constraints and facilitate farmers' access to these technologies. For this, we used high-resolution RGB images captured by a low-cost drone, a free downloadable app for image processing and a common spreadsheet software to perform the model parametrization. Moreover, a real application of the method is provided to evaluate the limits and benefits of the entire procedure and to point out the most promising implications for future developments in this research field.
# Fires Drive Long-Term Environmental Degradation in the Amazon Basin
## 2 Materials and Methods
### Burned Areas  
Fire detection was performed using a contextual algorithm [70] that exploits the strong emission of mid-infrared radiation from fires [71; 72]. The algorithm examines each pixel of the MODIS swath and ultimately categorizes each pixel into the following classes: missing data, cloud, water, non-fire, fire, or unknown.  
The Terra and Aqua combined MCD64A1 Version 6 Burned Area data product is a monthly, globally gridded 500 m product containing per-pixel burned-area data and quality information. The MCD64A1 burned-area mapping approach employs 500 m MODIS Surface Reflectance imagery coupled with 1 km MODIS active fire observations. The algorithm uses a burn-sensitive vegetation index (VI) to create dynamic thresholds that can be applied to the composite data. This VI is derived from MODIS shortwave infrared atmospherically corrected surface-reflectance bands 5 and 7 with a measure of temporal texture (i.e., \(05-\rho 7/\rho 5+\rho 7\)). The algorithm identifies the date of burn for the 500 m grid cells within each individual MODIS _tile_. The date is encoded in a single data layer as the ordinal day of the calendar year during which the burn occurred, with values assigned to unburned land pixels and additional special values reserved for missing data and water grid cells [73; 74].  
Mapping was used to relate the recording date of the satellite's passage to the nearest record for individual grid cells. The date was then encoded in a single layer of output data in terms of Julian days on which burning occurred (interval 1-366); 0 was assigned to \"no burn\" pixels and additional special values were assigned to missing data and water grid cells. We sought to calculate the burned areas for the Amazon Basin (1-365 Julian days) from 2001 to 2019 (for 2019, we used data until 1 September 2019). Subsequently, the data were vectorized and the areas were quantified in km\({}^{2}\) (extracted by country, ILs, and CUs located in the Amazon Basin). We then aggregated the data from Julian days to quarters, called Q1, Q2, Q3, and Q4, which correspond to the first, second, third, and fourth quarters, respectively.
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 3 Code-Frequency Two-Stage Compression Acquisition
### Complexity Analysis  
The complexity of the proposed algorithm consists of three parts: frequency domain compression preprocessing, code domain compression, and signal reconstruction, of which the first and third are the main parts, and the second part can be ignored as the elements of the measurement matrix are \(-1\) or \(1\). In this paper, we chose the orthogonal matching pursuit (OMP) algorithm for signal reconstruction, whose complexity is \(O(N\cdot(\log_{2}(N))^{2})\)[30; 31]. Table 1 gives the complexities of the serial acquisition algorithm, the PMF-FFT acquisition algorithm, the code-compression (CC) acquisition algorithm [26] and the proposed code-frequency compression (CFC) acquisition algorithm.