esa-sceva/satcom-chunk-collection huggingface dataset:
# Leading Edge Blowing to Mimic and Enhance the Serration Effects for Aerofoil +
## 4 Use of Leading Edge Blowing to Enhance the Serration Effects (Hybrid Device)
### Aerodynamic Results
The distributions of \(\mathrm{\Delta C_{D}}\) in Figure 13b clearly show that improvement over the serration-only can be enhanced by increasing the level of \(Q\) ', especially at \(\mid\!\alpha\!\mid\!\geq 5^{\circ}\). A simple explanation of the lower \(\mathrm{C_{D}}\) as \(Q\)'increases, which is applied from the serrated leading edge against the inflow direction, is due to the counteracting force provided by the blowing jet in an anti-main flow direction. The implication of the results in Figure 13b is significant in that the use of leading edge blowing can help to reduce the extra parasite drag normally associated with a leading edge serration. Because of the different responses of \(\mathrm{C_{L}}\) and \(\mathrm{C_{D}}\) against the \(Q\) ', it would be of interest to examine the ratio between them. Defining \(\mathrm{\Delta C_{L}/C_{D}}\) (\(\alpha\)) = \(\mathrm{C_{L}/C_{D}}\) treated(\(\alpha\)) - \(\mathrm{C_{L}/C_{D}}\) baseline(\(\alpha\)). Figure 14a,b show the \(\mathrm{C_{L}/C_{D}}\) and \(\mathrm{\Delta C_{L}/C_{D}}\) against the angles of attack \(\alpha\) produced by the baseline, serration-only (\(\lambda 45A30\)) and hybrid (\(\lambda 45A30+Q\)'= 1.0-4.5 L/min) leading edge device at \(U\) = 25 ms\({}^{-1}\), respectively. The most significant results lie in the region of \(3^{\circ}\leq\alpha\)\(\leq 8^{\circ}\), where the serration-only case would have performed poorly. The hybrid device is no doubt a better choice than the serration-only case. More importantly, it can even outperform the baseline aerofoil as soon as the \(Q\)'is introduced. The improvement increases as the \(Q\) \begin{table}
\begin{tabular}{c c} \hline \hline \(\alpha\) **(o)** & **d(\(\mathrm{\Delta C_{L}}\))/d(Q?)** \\ \hline \(-15\) & \(-0.072\) \\ \(-5\) & \(-0.029\) \\ \(6\) & \(-0.045\) \\ \(12\) & \(-0.120\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the decay rates of \(\mathrm{\Delta C_{L}}\) against the \(Q\)'at four representative \(\alpha\). Figure 13: Comparison of the (**a**) \(\mathrm{C_{D}}\) as a function of \(\alpha\) produced by the baseline and hybrid (\(\lambda 45A30+\) blowing \(Q\) ) leading edge device at \(U\) = 25 ms\({}^{-1}\). The leading edge blowing range covers 0.0 (serration only) \(\leq Q\)'\(\leq\) 4.5 L/min, and (**b**) The corresponding \(\mathrm{\Delta C_{D}}\) as a function of \(\alpha\), where \(\mathrm{\Delta C_{D}}\) is the difference in \(\mathrm{C_{D}}\) between the hybrid (\(\lambda 45A30+Q\)'= 0.0–4.50 L/min) and the baseline. increases. At \(\alpha\geq 10^{\circ}\), although not as good as the baseline aerofoil, the hybrid device still outperforms the serration-only aerofoil when a finite range of optimal \(Q^{\prime}\) is utilised. The impact of adding leading edge blowing to the serration at negative angle of attack, however, is less beneficial.
# Purification and Characterization of Authentic 30S Ribosomal Precursors Induced by Heat Shock
## 2 Results  
### Introduction of His and Strep Affinity Tags onto 30S Ribosomal Proteins  
Because of the reduced amount of DnaK chaperones devoted to ribosome assembly under heat shock conditions, there is a transient accumulation of authentic precursors, not degraded or dead-end particles. These 21S particles were previously characterized after radioactive labelling, since very few are normally present in bacterial cells [22]. The isolation of these small ribosomal subunits therefore depends on having a very efficient method for eliminating mature particles, and affinity chromatography is an attractive solution for the purification of 21S. We noted that the 30S precursors described in the literature contain bS20 but not uS2 [9; 10; 11; 12; 13; 17]. We therefore decided to introduce two different tags, using strep tags on bS20, and 6 xHis tags on the late-assembly uS2 proteins [23]. We chose bS20 for the early proteins because its N- and C-terminal extremities are accessible enough for the tag to be used to pull out the particles, yet its core is deeply buried, which prevents dropping during the process. We used the Datsenko and Wanner method [24] to insert these tags at the _rpsB_ (encoding the late protein uS2) and _rpsT_ (encoding the early protein bS20) carboxyl termini in E6006 bacteria. Because the disruption of ribosomal biogenesis or maturation can cause slower growth or cold sensitivity, we first checked whether the tag insertions affected bacterial growth. To do this, we compared the growth characteristics of E6006 to those of MG1655 at 20, 30, 37 and 42 \({}^{\circ}\)C in LB and M63 minimal medium. In all conditions, the growth was identical in both strains. Similarly, we found that the presence of a tag did not affect the minimum inhibitory concentrations (MICs) for gentamycin, kanamycin, chloramphenicol, or in vitro translation assays (results not shown). These data suggest that tag presence does not affect ribosome activity. By incorporating these tags, we were able to purify ribosomes very efficiently using nickel-charged affinity resin (Ni-NTA) or streptactin. Finally, we used the E6006 strain to purify 70S from crude extracts, as well as for obtaining 30S and 50S ribosomes from ribosome pellets obtained from a single ultracentrifugation (Figure 1).
## 7 Discussion
### Accuracy
The choice of forecasting method depends on the learning difficulty and data availability, while the performance of the forecasting model depends on the size of the dataset. The STLF using ICM includes methods such as SNN and SVMs. The dataset size affects the performance of ANNs and SVMs, with larger datasets improving generalization. The fuzzy logic adapts to uncertainty, while genetic algorithms optimize parameters; their effectiveness depends on the diversity of the dataset. Ensemble methods benefit from larger datasets for model diversity. Deep learning models such as RNNs and LSTMs require substantial data for effective training. Hybrid models combine techniques, and the dataset size also influences their performance. The analysis showed that certain hybrid models achieve MAPE values below the 3% threshold, indicating a high level of accuracy in load prediction. This accuracy has a direct impact on the grid's ability to effectively balance supply and demand, thereby reducing the frequency of load shedding events and the reliance on peaking power plants. Careful pre-processing and validation are critical for a reliable STLF model in power systems.
# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods  
The research methodology incorporates experimental validation through real-time simulation of jamming scenarios, leveraging metrics such as retransmission rates, energy consumption, and routing resilience to quantify the algorithm's performance. Comparative analysis with traditional static mitigation strategies will highlight improvements in latency, energy efficiency, and network reliability.
## 6 Experiment  
In this section, four sets of simulation experiments were conducted to evaluate the performance of the MuL-ACO scheme. In the first set of experiments, the algorithm proposed in this paper was compared with other intelligent algorithms, which are the well-known GA and the novel sparrow search algorithm (SSA) [33]. In the second set of experiments, the algorithm proposed in this paper was compared with the traditional ACO and other improved ACO, which are MH-ACO and MF-ACO. Two groups of experiments were carried out on maps with different sizes and different numbers of obstacles. The third set of experiments was set with different starting points and ending points on a map, and compared with the proposed algorithm with ACO, MH-ACO, and MF-ACO. To further verify the effectiveness of the improved algorithm, the fourth set of experiments was simulated in a dynamic environment. Furthermore, the height threshold of uneven environments was set to (\(-1\) m, \(1\) m) to constrain the robot's trajectory search. The initial trajectory is given on each map to show the process of trajectory generation and optimization.  
To build the environment map of the mobile robot, 2D-H grid maps with different sizes, obstacles, and terrains were modeled by the MATLAB simulation platform, where the blue areas are low terrain areas, and the green areas are high terrain areas. Obstacles were randomly placed on the map and start and end points were set. All experiments were performed on the same PC to obtain an unbiased comparison of CPU time. Parameters of MuL-ACO were set as the following: \(k=50,\;I_{\max}=50,\;\alpha=3,\;\beta=6\), \(C=10,\;Q=100,\;u=5\,,w=8,\;h_{c}=1,\;\tau(0)=20\,,\mu=33,\;\lambda=\gamma= \psi=1\).  
Figure 7: Flow chart of the MuL-ACO.
## 2 Materials and Methods
### Health Risk Assessment  
In this study, the health risk assessment model issued by the United States Environmental Protection Agency (USEPA) [37; 38] was employed to evaluate the health risks caused by dust exposure to urban residents in Yangxin County. The calculation model formulas of the pollutant exposures ADD\({}_{\text{ing}}\), ADD\({}_{\text{inh}}\), and ADD\({}_{\text{derm}}\), through the hand-oral contact intake, respiratory inhalation, and skin contact, are shown as Equations (2)-(4). The calculation model of the lifelong average exposure dose of carcinogenic heavy metal inhalation is shown as Equation (5).  
\[\text{ADD}_{\text{ing}}=\text{C}\times\frac{\text{EF}\times\text{ED}\times \text{R}_{\text{ing}}}{\text{AT}\times\text{BW}}\times 10^{-6} \tag{2}\]  
\[\text{ADD}_{\text{inh}}=\text{C}\times\frac{\text{EF}\times\text{ED}\times \text{R}_{\text{inh}}}{\text{AT}\times\text{BW}\times\text{PEF}} \tag{3}\]  
\[\text{ADD}_{\text{derm}}=\text{C}\times\frac{\text{EF}\times\text{ED}\times \text{AF}\times\text{SA}\times\text{ABS}}{\text{AT}\times\text{BW}}\times 10^{-6} \tag{4}\]  
\[\text{LADD}_{\text{inh}}=\frac{\text{C}\times\text{EF}}{\text{PEF}\times\text {AT}}\times(\frac{\text{R}_{\text{inh}}^{\text{child}}\times\text{ED}_{\text{ child}}}{\text{BW}_{\text{child}}}+\frac{\text{R}_{\text{inh}}^{\text{adult}}\times\text{ED}_{\text{adult}}}{\text{BW}_{ \text{adult}}}) \tag{5}\]  
where ADD\({}_{\text{ing}}\), ADD\({}_{\text{inh}}\), and ADD\({}_{\text{derm}}\) are the non-carcinogenic exposures of pollutants in the three routes of hand-oral contact, inhalation, and skin contact, mg/(kg\(\cdot\)d); LAD\({}_{\text{inh}}\) is the average daily exposure amount of carcinogenic heavy metals inhalation, mg/(kg\(\cdot\)d); C is the heavy metal content of road dust, mg/kg. The meanings and sources of other parameters were shown in literature [36].  
The total non-carcinogenic and carcinogenic risks of various types of heavy metal pollution in dust to the human body can be calculated by Equations (6) and (7).  
\[\text{HI}=\sum\text{H}\text{Q}_{\text{i}}=\sum\frac{\text{ADD}_{\text{ij}}}{ \text{R}\text{D}_{\text{ij}}} \tag{6}\]  
\[\text{R}_{\text{T}}=\sum\text{R}_{\text{i}}=\sum\text{LADD}_{\text{ij}}\times \text{SF}_{\text{ij}} \tag{7}\]  
where HI is the total non-carcinogenic risk; RfD is the reference dose; R\({}_{\text{T}}\) is the total carcinogenic risk; SF is the carcinogenic slope factor, standing for the probability of human exposure to a certain pollutant causing cancer. When HI \(<\) 1, the risk is small or negligible. When HI \(>\) 1, it indicates that there is a non-carcinogenic health risk. According to the USEPA standard, R\({}_{\text{i}}\) \(<\) 10\({}^{-6}\) is considered to have no carcinogenic risk, and R\({}_{\text{i}}\)\(>\) 10\({}^{-4}\) is considered to have a significant carcinogenic risk [39]. Table 1 showed the reference doses and carcinogenic slopes of various heavy metals in different exposure routes.
## 3 Results  
### Growth Parameters and Productivity  
The initial biomass was 6.80 \(\pm\) 0.04 g per plant and significant differences in growth parameters were detected after 10 weeks (Table 2). At the individual level, F110 produced significantly (\(p<0.05\)) higher aboveground biomass than L220 and higher stem growth than F220 and L220. At the unit level (pooled individual weights), on the other hand, both F220 and L220 produced significantly higher (\(p<0.05\)) aboveground and belowground biomasses, number of leaves and stem growth than did F110 and L110. The total final biomass in F220 was higher (\(p<0.05\)) than in both L110 and F110; meanwhile, L220 only differed significantly (\(p<0.05\)) from L110. The same trend was observed in total productivity, with F220 expressing the highest productivity rate, at 56.6 \(\pm\) 14.0 g m\({}^{-2}\) day\({}^{-1}\). Regarding relative growth rate, the treatments showed similar values even though the low-density treatments had a slightly higher rate on average. Cumulative biomass gains over time (Figure 2) showed a large variance in individual weight across treatments, and, as a result, no statistical main effects of either plant density nor artificial lighting were detected. Nonetheless, a within-subject effect of the variable week was detected [F\({}_{(76.4,122.7)}\)= 120.58, \(p=2.88\times 10^{-9}\), generalized \(\eta^{2}=0.76\)].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Unit** & **F110** & **F220** & **L110** & **L220** \\ \hline _Growth per plant_ & & & & & \\ Initial biomass & g FW & 6.8 \(\pm\) 0.3 & 6.8 \(\pm\) 0.2 & 6.8 \(\pm\) 0.1 & 6.8 \(\pm\) 0.1 \\ Final biomass & g FW & 30.2 \(\pm\) 5.0 & 24.7 \(\pm\) 4.5 & 28.4 \(\pm\) 4.9 & 23.8 \(\pm\) 3.6 \\ Final aboveground biomass & g FW & 25.7 \(\pm\) 4.4 a  & 21.1 \(\pm\) 4.0 ab  & 24.4 \(\pm\) 4.5 ab  & 20.3 \(\pm\) 3.2 b  \\ Final belowground biomass & g FW & 4.5 \(\pm\) 0.7 & 3.6 \(\pm\) 0.5 & 4.1 \(\pm\) 0.4 & 3.5 \(\pm\) 0.5 \\ Leaves & n & 243 \(\pm\) 36 & 205 \(\pm\) 34 & 261 \(\pm\) 33 & 218 \(\pm\) 28 \\ Stems & mm & 55.2 \(\pm\) 7.0 a  & 41.2 \(\pm\) 5.5 b  & 50.3 \(\pm\) 8.3 ab  & 40.9 \(\pm\) 3.8 b  \\ \hline \end{tabular}
\end{table}
Table 2: Growth parameters (mean \(\pm\) s.d.) of \(H\). _portulacoides_ hydroponic units (\(n=5\)). FW—fresh weight. Different letters (a,b,c) indicate significant differences between treatments (\(p<0.05\)).
# Maintenance Method for Rail Corrugation Considering Wheel-Rail Interaction Force
## 2 Materials and Methods
### Rail Surface Roughness  
The rail grinding car was composed of two vehicles with two units on each vehicle, as shown in Figure 3. Each unit had four grinding stones. Therefore, a total of 16 stones were used. The grinding car and a rail grinding stone are shown in Figure 3b and Figure 3c, respectively. Details on the grinding car and grinding stones are presented in Table 2. Stones A and B had diameters of 180 and 260 mm, respectively, and were capable of grinding angles of (\(-\))20-(\(+\))15\({}^{\circ}\) and (\(-\))70-(\(-\))5\({}^{\circ}\), respectively. The grinding speed was 2-8 km/h, and ordinary speed was approximately 60 km/h.  
Figure 1: Rail corrugation. (**a**) Section A; (**b**) Section B; (**c**) rail before grinding; (**d**) rail after grinding.  
Figure 2: Measurement: (**a**) RAILPROF instrument; (**b**) rail surface roughness measuring work.  
To examine the impact of rail corrugation on the track, measurements were taken before and after grinding the rail corrugation section. The tests were conducted on an 8-car train. The measurement was performed 10 times consecutively at the same time using the measurement in Figure 4 for an operating train. The measured items were dynamic wheel load, vertical rail acceleration, and vertical displacement of rails and sleepers. Vertical rail displacement and sleeper displacement were measured using displacement transducers such as linear variable differential transformers (LVDTs) mounted on a jig anchored on the side bridge inspection passage without the influence of train load. LVDTs (CDP-10M) have a sensitivity of 500 \(\times\) 10\({}^{-6}\)/mm, a rated power of 6.25 mV/V \(\pm\) 0.3%, and a frequency response of 500 Hz. Displacements are measured and recorded automatically by the computer-controlled data acquisition system [19]. The wheel loads were measured using  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Details** \\ \hline Size (length \(\times\) width \(\times\) height) & 27.1 m \(\times\) 2.4 m \(\times\) 3.7 m \\ Weight & 76 tons \\ Engine spec. & 205 kw, 1800 RPM \(\times\) 2 each \\ Number of grinding stones & 16 \\ Diameter of grinding stones & Stone A: 180 mm \(\times\) 12 each \\  & Stone B: 260 mm \(\times\) 4 each \\ Grinding motor type & Rotary wheel type, electric type \\ Grinding motor rotation speed & 3600 RPM \\ Grinding motor power & 11 kw \(\times\) 16 each \\ Grinding angle & Stone A: (\(-\))20\(-\)(+)15\({}^{\circ}\) \\  & Stone B: (\(-\))70\(-\)(\(-\))5\({}^{\circ}\) \\ Grinding head control & 2 stones 1 set, independent control \\ Normal running speed & 60 km/h \\ Grinding speed & 2–8 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the rail grinding car.  
Figure 3: Grinding car. (**a**) Illustration of the unit module; (**b**) photo of grinding work; (**c**) photo of grinding stones.  
strain gauges. The vertical rail acceleration has a frequency response range of DC-130 Hz, a natural frequency of 240 Hz, and a gravimeter with an acceleration of 5.1 g.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.2 Drone Used for Image Acquisition  
In this study, a DJI Mavic Pro drone (Figure 3) was used. It is a multirotor system with four motors (quadcopter) powered by a LiPo (lithium polymer) smart battery with a capacity of 3830 mAh and an autonomy of 27 min. It features a wingspan of 20 cm, a weight of 736 g, and a maximum speed of 65 km/h. The DJI Mavic Pro is equipped with a 12-megapixel 4 K camera (stabilized by a pod). This camera acquires images in true colors with 8-bit radiometric resolution. This acquisition is carried out automatically using a cadence previously defined during the flight preparation. The system uses autonomous ultrasonic sensor flight technology to reduce the risk of accidents and is equipped with a GPS-GLONASS location system. The system includes a ground control radio station (connected to a smartphone) with a range of 7.3 km under normal conditions (no obstacle to the transmission of the radio signal) and a battery life of 1.5 h.  
The configuration of the drone and the flight planning were conducted with a DJI Go 4 (version 4.3.28) and DroneDeploy (version 4.0.0) software, respectively.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 3 Materials and Methods
### Jigsaw Transform and Cellular Automata  
Image encryption is of great importance currently to ensure the protection of sensitive information by preventing unauthorized access to it. Cryptography techniques used for image encryption are required to have features such as hiding the visual information of the image, having a large key space to resist brute force attacks, and having high key sensitivity to prevent differential attacks [41]. A recent example of an algorithm that meets these requirements is that proposed by [PERSON] et al. [42], where they developed a three-dimensional chaotic system with line equilibrium, which was used along with Josephus traversal to implement an image encryption scheme based on pixel and bit scrambling and pixel value diffusion, resulting in an encryption method secure against brute force attacks and differential attacks. In addition, the authors implemented the proposed chaotic system into an electronic circuit. In contrast, in the present work, we used the Jigsaw transform and a cellular automaton to encrypt the watermarked image, improving the key space of the Jigsaw-transform-alone implementation.
# A Reconfigurable Mesh-Ring Topology for Bluetooth Sensor Networks
## Proposed Method
### Routing Algorithm  
In general, the packet routing of the Mesh-Ring network can be classified into two cases, namely the intra-piconet routing and the inter-piconet routing cases. In the intra-piconet routing case, a source node and a destination node are located at the same piconet under a master. First, the packet is forwarded to the master, and then the master forwards to the destination in the corresponding slots of its piconet. In the inter-piconet routing case, the packets will be forwarded to the master of the source node in the piconet. Then the source master \(R_{i}\) checks and delivers the packets to the immediate downstream master along the interconnecting mesh or the ring subnet, as well as passing the packets to the master of the destination. Finally, the destination master \(R_{j}\) forwards packets to reach the final destination.  
To reduce the hop length of a larger ring subnet and efficiently deliver packets, a criterion is defined in Equation (2) to minimize the hop distance between master \(i\) as \(R_{i}\) and master \(j\) as \(R_{j}\). As long as a master in the ring subnet receives a new packet either from the piconet or the ring subnet, the master checks the mesh link first and the hop distance from the source ID to the destination ID will be computed by Equation (2). If Equation (2) is satisfied, the packet will be forwarded in the counterclockwise direction in the ring subnet. Otherwise, the packet will be forwarded in the clockwise direction.  
\[R_{j}-R_{i}\leq c/2;j>i \tag{2}\]  
To create a full RMR network, the computation complexity is O (\(n^{2}\)) and the formation message complexity is O (\(n\)). During scatternet formation, the connection messages do not affect packet transmission. On the other hand, the computational complexity of the optimal mesh connectivity is equal to O (\(p\)\(\times\)\(ml\)) during the maintenance phase, where \(p\) is the number of piconets and \(ml\) is the number of available mesh links. In addition, the message complexity of the mesh connection is equal to O (\(ml\)), since only \(ml\) links need to be established. As a result, the impact on the connection message over the sending packets is quite small, since the connection message is triggered only as the topology changes.
## 2 Materials and Methods
### Calibration and Experimental Protocol
#### 2.3.3 Experiment 2: Linking Model Inputs to Yield Estimation Accuracy  
The core goal of this experiment is to test whether crop N status and yield variation across the experimental treatments in both years can be retrieved using only time-series observations of LAI from each plot and the growing season meteorology. Because crop N status data were not collected across all experimental sites, the model testing was against plot yields, which are strongly determined by N fertilization. The key information for LNAretrieval and yield estimation was a combination of LAI and climate time-series, along with the hypotheses represented in the DALEC-Crop model, following the approach in Experiment 1.  
In this experiment we also test the relative information content of LAI and climate data for providing robust LNA and yield retrievals. We explored the yield estimation accuracy when interchanging the annual meteorological data and LAI observations between the two seasons used to drive and constrain DALEC-Crop during the calibration of the slope and intercept coefficients of the LNA fit. Using the sampled cross-treatment parameter calibration as prior distributions, two sub-experiments were carried out involving inter-changing the meteorological driving data (Experiment 2\({}_{\text{MET}}\)) and the LAI observations (Experiment 2\({}_{\text{OBS}}\)) between the two seasons. For both seasons, the accuracy of the yield estimates generated from these two experiments was evaluated based on the validation plots.