esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modelling and Methodology
### Two-Dimensional Analytical Wake Model
\[v_{x}=v_{0}\left(1-\frac{10.32a}{\sqrt{2\pi}}\left(\frac{r_{0}}{r_{0}+\alpha \cdot x}\right)^{2}\cdot e^{-\frac{r^{2}}{2}\cdot(\frac{2.58}{r_{x}})^{2}}\right) \tag{14}\]  
For single wake, the velocity deficit is determined according to the wake interaction patterns with a uniform representation formula:  
\[Deficit=\frac{10.32a}{\sqrt{2\pi}}\left(\frac{r_{0}}{r_{0}+\alpha\cdot x} \right)^{2}\cdot e^{-\frac{r^{2}}{2}\cdot(\frac{2.58}{r_{x}})^{2}}\sqrt{\frac {A_{overlap}}{A_{rotor}}} \tag{15}\]  
where \(A_{overlap}\) is the overlapping area between the wind turbine wake and the wind turbine rotor. For the partial wake pattern, it was calculated according to the relative wind turbine positions. For the full wake pattern, it was equal to the rotor area \(A_{rotor}\). Figure 3: Comparison of the normalized wind speed contours in the wake calculated by: (**a**) one-dimensional [PERSON] wake model and (**b**) two-dimensional Jensen-Gaussian wake model. For the \(i\)-th wind turbine influenced by multiple wakes of \(N\) wind turbines, its velocity deficit is represented by:  
\[Deficit^{i}=\sqrt{\sum_{i=1}^{N}\left(\frac{10.32a}{\sqrt{2\pi}}\bigg{(}\frac{r_{ 0}}{r_{0}+\alpha\cdot x}\bigg{)}^{2}\cdot e^{-\frac{r^{2}}{2}\cdot(\frac{2.58}{ \pi})^{2}}\sqrt{\frac{A_{overlap}^{i}}{A_{overlap}^{i}}}\right)^{2}},\ i=1,\ 2,\ \ldots,\ N \tag{16}\]  
Under simplified wind condition with uniform incoming wind speed \(v_{0}\), the final approaching wind speed \(v_{i}\) of the wake-affected wind turbine is calculated by:  
\[v_{i}=v_{0}\Big{(}1-Deficit^{i}\Big{)},\ i=1,\ 2,\ \ldots,\ N \tag{17}\]  
Under Weibull distribution wind condition with the wind speed magnitude continuously changing, it has been reported that only the scale parameter of Weibull distribution is affected by the wake effect for wind turbine power output calculation, which gives [29]:  
\[c_{i}=c\Big{(}1-Deficit^{i}\Big{)},\ i=1,\ 2,\ \ldots,\ N \tag{18}\]  
where \(c_{i}\) is the scale parameter of the Weibull distribution for the wake-affected wind turbine.
## 4 Material and Methods  
### Bacterial Strains, Media and Growth Conditions  
_S. ambfaciens_ ATCC 23877 was obtained from the American Type Culture Collection (ATCC). The genome sequence of this microorganism was determined [33]. Derivative strain _pirA._:pTYM-18 (here indicated as \(\Omega\)pirA) was previously described [35]. This strain was obtained by introducing the shuttle-plasmid pTYM-18, which integrates into _pirA_ gene, into _S. ambfaciens_ ATCC 23877 by conjugation _with E. coli_ GM2929/pUB307::Tn7, as described previously [34]. Derivative strain _pirA._:pTYM-rpoB(R) (here indicated as \(\Omega\)pirA rpoB(R)) was obtained by introducing the shuttle-plasmid _pirA._:pTYM-rpoB(R) already described [59] into _S. ambfaciens_ ATCC 23877 by conjugation as described above.  
The composition (per liter) of the media used in this study for _S. ambfaciens_ growth and manipulation is here reported. Yeast starch (YS) broth: 2 g yeast extract, 10 g soluble starch, (18 g agar in YS agar); SM-II: 15 g dextrose, 10 g soybean flour, 0.5 g MgSO4\(\cdot\)7H2O, 5 g CaCO3, 15 g agar; soya flour mannitol (SFM) agar: 20 g mannitol, 20 g soya flour, 20 g agar. The composition of R2 agar is reported [82]. For fermentation experiments, _S. ambfaciens_ strains were cultivated in shake flasks at 28 \({}^{\circ}\)C on a rotary shaker at 200 rpm as described [34; 83].  
Figure 8: A summarized scheme depicting the up or downregulated ncRNA involved in secondary metabolism, morphological development, primary metabolism, translation, and ribosomal structure.
## 3 Results
### Pixel-Scale Error Intercomparison  
To ensure proper error analysis at the grid cell scale, we firstly check the specified location or pixel for each rain gauge and then evaluate the spatial-temporally coincident satellite pixel with corresponding gauges over the TP. Note that the zero-event records are not considered as the rainfall events during the calculation.  
In general, the IMERG product showed a limited capability in estimating hourly rainfall in the warm season of 2014, with the averaged CC of 0.32 for all gauges across the TP (Table 1). Higher CC was observed in the northeast and southeast TP (Figure 3). Along the Himalayas (i.e., the southern part of the TP), CC was smaller with the mean value less than 0.40. The weaker correlation was attributed to the complex topography in the Himalayas, which might influence the retrieval of satellite precipitation [10]. In spite of higher elevation (i.e., more than 4000 m a.s.l.) in the central TP, the mean CC was more than 0.60.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **CC** & **RMSE (mm)** & **RB (\%)** \\ \hline
**Hourly** & IMERG & 0.32 & 2.94 & 30.6 \\
**3-Hourly** & IMERG & 0.45 & 3.26 & 28.1 \\  & 3B42V7 & 0.42 & 3.47 & 48.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean values of Correlation Coefficients (CC), Root Mean Square Error (RMSE) (mm) and Relative Bias (RB) (%) for IMERG and 3B42 Version 7 (3B42V7) rainfall estimates at hourly and 3-hourly timescales in the warm season of 2014 over the Tibetan Plateau (TP).  
Figure 2: Maps of the mean three-hourly (**a**) IMERG and (**b**) 3B42V7 rainfall estimates in the warm season of 2014 over the TP.  
At the three-hourly scale, IMERG and 3B42V7 showed similar RB, RMSE and CC patterns in space across the TP (Figure 3). Both of them showed overestimation in the south and west and underestimation in the northeast. However, several negative RB values of IMERG rainfall estimates were detected along the southern boundary of the TP, whereas 3B42V7 generally showed positive RB values. In addition, lower absolute RB values were mainly distributed in the eastern TP for both products at the three-hourly scale. Over the plateau, both IMERG and 3B42V7 showed overall overestimation of 28.1% and 48.6% at the three-hourly scale, respectively. As for RMSE, both products decreased gradually from the southeast to northwest across the TP, with averaged values of 3.26 mm (IMERG) and 3.47 mm (3B42V7), respectively (Table 1). 3B42V7 showed slightly worse agreement with gauge data than IMERG, with the CC value of 0.42 at the three-hourly scale. Spatially, the CC values for both products were generally higher in the east than those in the west.
## 1 Introduction  
Current production policies aim as much as possible to reduce chemical inputs in order to avoid soil, air and crop pollution in sustainable farm conditions [1]. Among the vegetable species worldwide, tomatoes are the most cultivated vegetables with areas exceeding 5.03 million ha and with average yields of 35.9 t-ha\({}^{-1}\). The expansion of tomato areas is increasing [2]. Worldwide, the average consumption of tomatoes is 21.17 kg/capita [3], and in Romania the average consumption grows to 41.40 kg-capita\({}^{-1}\)[4].  
Nowadays, there is a global interest in obtaining higher tomato production using different methods to improve the absorption of essential minerals in plants in order to increase production efficiently.  
Natural or artificial stimulants (physical, chemical) are factors that induce various changes in plant response to their defence by: the accumulation of primary or secondary metabolic compounds (polyphenols, lycopene, proteins, lipids, carbohydrates); physiological changes (chlorophyll compounds, photosynthetic activity, CO\({}_{2}\) exchange); and morphological changes (number of fruits, fruit mass, leaf area, production, etc.), in order to optimally restore [5; 6; 7].  
Electrostimulation of plants can induce plant movement, the activation of ion channels, ion transport, gene expression, enzymatic system activation, electrical signalling, plant-cell damage, enhanced wound healing, and can also influence plant growth [8].  
Over the years, various researchers have looked at the ability of plants to generate higher yields through the use of electrical stimuli [6].  
Numerous studies that use the effects of electric current on plants starting from the seeds [9], roots [10], cells [11], conservation capacity [12], the uses of chemical nutrients monitored by electric signal [13] and last but not least the production capacity [14] were conducted capturing the attention of researchers.  
In the case of the tomato, the positive effects of direct electric current (DC) with an intensity between 15 and 3 \(\mu\) A on growth and ion accumulation in a tomato plant was studied by [PERSON] et al. [15]. In another study, the use of 4-12 kV electricity for 30-40 s led to an increase in seed germination [16]. [PERSON] et al. by applying a low-voltage electric current 160 mT, 60 Hz, for 1 min on tomato seeds, observed an improvement in the process of germination, a more abundant sprouting, a development of the root system and implicitly an increase in the leaf surface [17].  
The purpose of this study was to evaluate the characteristics that tomato fruits have acquired by applying a direct electric current, following the evolution of some biometrical, physiological and quality indicators.
## 4 Analysis of Test Results
### Parameter Determination Based on the Duncan-Chang Model
Figure 10:2 Relationship between \((\sigma_{1}-\sigma_{3})_{\text{ult}}\) and the fiber length of the fiber-reinforced soil. Figure 10:3 Relationship between \(E_{i}\) and the confining pressure of the fiber-reinforced soil. Considering the effects of fiber content, fiber length, and confining pressure on \(E_{i}\) and \((\sigma_{1}-\sigma_{3})_{\text{ult}}\), the fiber-reinforced soil model parameters, the predictive models for \(E_{i}\) and \((\sigma_{1}-\sigma_{3})_{\text{ult}}\) are proposed as follows:  
Figure 10:4  
Figure 10:5  
To verify the accuracy of Equations (7) and (8) for \(E_{i}\) and \((\sigma_{1}-\sigma_{3})_{\text{ult}}\), we compared the measured values of \(E_{i}\) and \((\sigma_{1}-\sigma_{3})_{\text{ult}}\) with the predicted values (Figures 17 and 18). The values were basically consistent, with correlation coefficients \(R^{2}\) over 0.97. The stress-strain relationship of fiber-reinforced soil can be described by the Duncan-Chang model, and the proposed method for determining \(E_{i}\) and \((\sigma_{1}-\sigma_{3})_{\text{ult}}\) is feasible. Equations (7) and (8) can accurately predict the parameters of the Duncan-Chang model, and the model can accurately determine the mathematical expressions of the mechanical properties of the fiber-reinforced soils, meaning that these findings can provide a reference for the solution of similar problems.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 3 Simulations
### System Performance
It is shown that the performance of the dual-channel system before optimization with layout A is improved in comparison with the original single channel system, with noise attenuation over 10 dB at rotation angles less than 60\({}^{\circ}\), but the improvement decreases as the rotation angle increases, with a minimum reduction of 5.5 dB. After optimization, the noise reduction exceeds 14.5 dB at every head rotation angle, with less of a difference in noise reduction at most angles, except for \(\pm 75^{\circ}\) where noise reduction is significantly higher. It performs better than the optimized single channel system because the noise reduction is only slightly less at \(\pm 30^{\circ}\) and \(\pm 45^{\circ}\), but the improvement in noise reduction exceeds 6.0 dB at all other angles. However, for the dual-channel system with layout B, the performance improvement is insignificant compared with the single channel system. The difference in noise reduction at each rotation angle before optimization is less than 2.4 dB, while after optimization the corresponding performance curves for both systems are shown to have similar trends and values, with the difference in noise reduction between the two being less than 4.2 dB at  
Figure 7: Noise attenuation of single channel system with two optimization methods when head rotates. each rotation angle. It can be explained that the transfer responses for virtual plants of both systems remain relatively close to each other as the human head rotates, as shown in Figure 6, suggesting that the second source of the dual-channel system with layout B does not offer more degrees of freedom. Results at other frequencies, namely 125 Hz and 500 Hz, are then presented. Figure 9 depicts the control performance of the original system at three frequencies with head rotation. It is shown that as the head rotates, the noise attenuation decreases faster at higher frequencies because of the shrunken quiet zone. The minimum noise reduction is only 1.1 dB at the maximum rotation angle at 500 Hz. Figure 8: Noise attenuation with transfer response model optimization when head rotates. Single channel stands for single channel system, Dual-channel A for dual-channel system with layout A and Dual-channel B for dual-channel system with layout B.  
Figure 9: Noise attenuation with conventional control at different frequencies. Figure 10 presents the noise control performance before and after optimization at 125 Hz and 500 Hz, showing the similar significant improvement in noise control performance for larger rotation angles. Compared with the noise attenuation before optimization, the minimum value increases from 7.8 dB to 16.6 dB at 125 Hz and from 1.1 dB to 7.9 dB at 500 Hz. In summary, the method improves the robustness to the human head rotation for the frequencies of interest and makes the difference between the noise reduction for different rotation angles smaller.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 9 Threat Analysis
### Secure Aggregation  
The common aggregation function used for FT is FedAvg, as described in Algorithm 1. For the sake of clarity, we summarize its operation here. Each client trains the global model (\(\theta_{t}\)) in round \(t\) using its data, producing a local update of the weights (\(\Delta\theta_{k}\)). The updates are aggregated with a standard weighted mean:  
\[\theta_{t+1}=\theta_{t}+\eta\sum_{k=1}^{K}\frac{n_{k}}{n}\Delta\theta_{k} \tag{1}\]  
where \(K\) is the number of clients, \(n_{k}\) is the number of data samples on client \(k\), \(n\) is the total number of samples across all clients, and \(\eta\) is the learning rate. It can be easily noted  
Figure 17: FL threats.  
how there is no active control over the client updates (\(\Delta\theta_{k}\)). Thus, a malicious client can poison its data and produce poisoned updates that will deviate and spoil the global model (\(\theta_{t}\)). The selected TFF tool provides different methods that can be used to both spot and reduce the effects of poisoning [75]. The secure aggregator implementation provided by TFF leverages the following two mathematical functions.  
* **Zeroing:** The updates that exceed a zeroing threshold (\(Z\)) are filtered out. \(Z\) can be fixed or computed at runtime using the \(i\)-th percentile of all the clients' updates. \[\Delta\theta_{k}^{\prime}=\begin{cases}\Delta\theta_{k}&\text{ if }\|\Delta \theta_{k}\|_{2}\leq Z\\ 0&\text{ otherwise}\end{cases}\] (2)
* **Clipping:** The updates are normalized in such a way that their norm never exceeds the clipping threshold (\(C\)). \(C\) can be fixed or computed at runtime using the \(i\)-th percentile of all the clients' updates. \[\Delta\theta_{k}^{\prime}=\frac{\Delta\theta_{k}}{\max\left(1,\frac{\| \Delta\theta_{k}\|_{2}}{C}\right)}\] (3)  
The main difference between these two functions is that zeroing is designed to identify and eliminate malicious updates, whereas clipping is intended to mitigate the impact of such updates. In this context, zeroing can be a valuable tool for detecting poisoning and conducting further analysis on malicious clients. Conversely, clipping can be an effective approach for retaining the beneficial elements of malicious updates while avoiding the complete discarding of update information. The combination of clipping and zeroing is an effective method for identifying malicious updates while retaining the beneficial information contained within them. The logic is to clip all the updates and discard the clipped updates that exceed the threshold (\(Z\)). TFF employs an adaptive quantile estimation process for both zeroing and clipping, thereby enabling the dynamic adjustment of thresholds for the handling of outliers [76]. The zeroing operation, by default, sets \(Z\) to the 98 th percentile, while the clipping mechanism sets \(C\) to the 80 th percentile. The \(C\) setting ensures that model updates with disproportionately large values are scaled down to a more manageable level, preventing them from dominating the aggregated update and skewing the learning process.
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 3 Camouflage Effect of an RB RAIM Detector in Multi-Outlier Mode
### Camouflage Effect of Residuals
\tag{14}\]  
At this time, it is not possible to identify outliers by the relationship of the elements of matrix \(\mathbf{S}\). The following two factors should be considered. 1. The combination of outliers. The diagonal elements of \(\mathbf{S}\) are differentiated depending on the geometry. 2. The magnitude and direction of outliers. The ratio of two biases is \(\beta=b_{g}/b_{k}\), taking two outliers as an example. Therefore, due to the overlapping of \(r_{i,bias}\), the residual of outliers may not be obvious. We define this situation as the camouflage effect of residuals in a multiple outlier mode, which creates difficulties for the performance of outlier identification in the RB RAIM detector. The camouflage effect causes two types of mistakes in the FDE of the RB RAIM detector: masking and swamping. Masking means the misidentification of outliers so the final state estimation will still be contaminated by outliers. Swamping means the identification of normal observations as outliers so that the accuracy of the final state estimation will be compromised.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 5 Experimental Results
### Evaluation of Used Areas in Room  
To evaluate the frequently used areas, they are labeled as shown in Figure 16. After statistical analysis, the average numbers of predicted people per day in different areas are demonstrated as shown in Figure 17. Areas A, B, and C, the main areas of the reading room, have relatively more people than areas D and E. Owing to the smaller sizes and locations (near doors or windows) of tables and chairs in Areas D and E, they have only 81 and 21 people, respectively, compared with 136 people in Area A.  
Figure 16: Labeling areas in the reading room.  
Figure 15: Predicted number of people in reading room following the timeline.
## 3 Flotation Recovery Models  
Flotation performance has generally been modelled based on first kinetics with suitable modifications to account for unfloatable minerals, for example [21]. In conventional batch cells, all particles spend the same length of time and, hence, their performance may be modelled similar to plug-flow reactors.  
Plug flow model: for a first-order rate reaction with a rate constant \(k_{c}\), exhibiting plug flow transport and a retention time \(t\), recovery \(R_{c}\) is given by  
\[R_{c}=1-exp\left(-k_{c}\,t_{p}\right), \tag{14}\]  
where \(t_{p}\) is the mean residence time of particles.  
Perfect mixing model: Continuously operated flotation cells are considered to behave like fully mixed reactors, and the residence time distribution (RTD) of particles is shown to approximate a negative exponential distribution. For a system exhibiting such perfect mixing behaviour with a mean residence time \(\tau\):  
\[R_{c}=1-\left(1+k_{c}\,\tau_{p}\right)^{-1}, \tag{15}\]  
The performance of counter-current flotation columns are considered to be in between those of plug flow and fully mixed reactors due to the axial mixing that occurs within the column, as described earlier. Axial mixing and, in turn, the RTD of the particles is affected by the superficial velocity of the slurry, gas holdup in the column, slip velocity of the particles, and the column height.  
For a counter-current column operation, the particle residence time \(\tau_{p}\) can be estimated by [PERSON] and [PERSON] [10]:  
\[\tau_{p}=\tau_{L}\,\left(\,\frac{J_{sl}/\left(1-\varepsilon_{g}\right)}{J_{sl} /\left(1-\varepsilon_{g}\right)+U_{sp}}\,\,\right), \tag{16}\]  
and the liquid residence time \(\tau_{L}\) is given as:  
\[\tau_{L}=\frac{H_{c}\,\left(1-\varepsilon_{g}\right)}{J_{sl}}, \tag{17}\]  
The recovery of solids in such a column with axial mixing as characterized by the vessel dispersion number, \(N_{d}\), can be given by [PERSON] [34]:  
\[R_{c}=1-\frac{4\,\,a\,\,exp\left(\frac{1}{2N_{d}}\right)}{(1+a)^{2}exp\left( \frac{a}{2N_{d}}\right)-(1-a)^{2}exp\left(\frac{-a}{2N_{d}}\right)} \tag{18}\]where  
\[a=\left(1+4\ k_{c}\ \tau_{p}\ N_{d}\right)^{1/2} \tag{19}\]  
In the extreme case of axial mixing approaching plug flow, \(N_{d}\)\(\rightarrow\)\(\infty\), Equation (18) reduces to Equation (15), representing perfect mixing operation. Similarly, in the absence of axial mixing, \(N_{d}\)\(\rightarrow\) 0, i.e., negligible dispersion, leads to Equation (14) which is applicable for plug flow transport.