esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Discussions
### Effect of the Number of S2s  
In the proposed method, the aortic component can be extracted by the average, because the average operation can smooth the pulmonary component and noise. However, the performance of average operation depends on the number of S2s involved in. It is reasonable that the greater the number of S2s involved in the average operation are, the greater possibility the assumption becomes true (i.e., the Equations (3) and (5) become true). So, the proposed method runs in block by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subject Index** & **Indicator** & **HVD Method [7] (ms)** & **STFT Method [4] (ms)** & **Proposed Method (ms)** \\ \hline \multirow{2}{*}{\(1\)} & mean & \((-1.40,1.40)\) & \((-0.64,0.64)\) & \((-0.61,0.61)\) \\  & std & \((12.30,14.29)\) & \((5.60,6.50)\) & \((5.36,6.23)\) \\ \hline \multirow{2}{*}{\(2\)} & mean & \((-1.80,1.80)\) & \((-2.40,2.40)\) & \((-0.52,0.52)\) \\  & std & \((15.48,18.04)\) & \((20.66,24.08)\) & \((4.50,5.25)\) \\ \hline \  
block. A large collection of S2s is needed before it can run. The embedded ensemble information of respiration modulation are taken advantage to yield the excellent performance. The proposed method will absolutely degrade if the number of S2s is too small. The experiment experience suggests that a very good performance can be obtained if the number of S2s in a recording is greater than 200. That is, the recording time should be greater than 3 min for a normal subject. However, the HVD method and the STFT method do not run in a manner of block. They can work with a single S2 input. There is no limit on the number of cardiac cycles for the HVD method and the STFT method.
# The Impact of Grafted Larvae and Collection Day on Royal Jelly's Production and Quality
## 3 Results and Discussion
### Effect of Larval Sex on Royal Jelly Production and Composition
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Group A3 (Worker)** & **Group B3 (Drone)** \\  & **(_n = 12_)** & **(_n = 12_)** \\ \hline Total yield per grafting & 5.6 \(\pm\) 0.8 a  & 4.1 \(\pm\) 1.9 a  \\ (g) & (4.7–6.8) & (1.2–6.3) \\ \hline Moisture & 69.3 \(\pm\) 1.10 a  & 68.3 \(\pm\) 1.63 a  \\ (\%) & (67.3–69.7) & (66.8–71.8) \\ \hline Protein Content & 14.54 \(\pm\) 0.92 a  & 14.12 \(\pm\) 1.06 a  \\ (\%) & (12.3–15.9) & (11.3–16.8) \\ \hline Fructose & 3.16 \(\pm\) 0.20 a  & 2.84 \(\pm\) 0.68 a  \\ (\%) & (2.35–4.29) & (2.40–3.43) \\ \hline Glucose & 4.66 \(\pm\) 0.31 a  & 4.00 \(\pm\) 0.89 a  \\ (\%) & (3.12–5.47) & (3.14–5.34) \\ \hline Sucrose & 1.94 \(\pm\) 1.00 a  & 1.53 \(\pm\) 0.91 a  \\ (\%) & (1.21–4.10) & (1.29–4.18) \\ \hline
10-HDA & 3.51 \(\pm\) 0.20 a  & 3.42 \(\pm\) 0.18 a  \\ (\%) & (2.81–4.23) & (3.01–4.17) \\ \hline \hline \end{tabular} Where different latin letters are used, statistically significant differences were found (a = 0.05). The values in parentheses refer to the range of values for each parameter. Where _n_: the number of samples in each group. A3 Group: worker larvae; B3 Group: drone larvae.  
\end{table} 
Table 3: Average yield per cell and physicochemical parameters of RJ samples from queen cells with different larval sex. variability remaining at similar levels. The protein content of RJ across all samples ranged from 11.6% to 16.8%, confirming that larval sex does not influence the protein composition of the jelly. Furthermore, the RJ produced in both cases exhibited comparable concentrations of major sugars and 10-hydroxy-2-decenoic acid (10-HDA). These results support the conclusion that honeybees do not discriminate between male and female larvae when provisioning queen cells, resulting in RJ of similar composition in both cases. No statistically significant differences were observed in the physicochemical properties or yield of RJ produced from drone larvae compared to worker larvae (all \(p>0.05\)). Therefore, the null hypothesis (H\({}_{3}\)) that the sex of larvae does not influence RJ characteristics could not be rejected. The negligible impact of larval sex on RJ production and composition is consistent with studies demonstrating that nurse bees provide queen cells with RJ similarly regardless of larval sex. The drone larva graft acceptance aligns with observations by [PERSON] et al. [36], who attributed the acceptance of queen cells grafted with drones to colonies with anarchy syndrome. The uniformity in moisture, protein, and 10-HDA levels between sexes further supports the notion that RJ composition is governed more by secretion physiology than larval genotype [38].
## 2 Materials and Methods
### Material Selection
#### 2.2.1 Multi-Objective Decision-Making (MODM)  
Multi-objective decision-making (MODM) optimizes various conflicting objectives that are part of a design problem by creating figures of merit (FOM) or performance indices for the relative evaluation and ranking of possible material candidates for a specific application [14]. The figure of merit (FOM) or performance index for a specific engineering application is a parameter of continuous value that is generated by using a known or supervised functional relationship between the various desired attributes of the material. For instance, the FOM for piezoelectric sensors is given by Equation (1) as:  
\[\frac{K^{2}_{33}}{d_{33}(1-K^{2}_{33})}, \tag{1}\]  
where: \(K_{33}\) is the coupling coefficient and \(d_{33}\) is the piezoelectric strain coefficient.  
The FOM for piezoelectric actuators is given by the piezoelectric strain coefficient \(e_{33}\) or piezoelectric strain coefficient \(d_{33}\), to evaluate the performance of materials in the longitudinal mode [3].  
In the case of piezoelectric energy harvesters, there are two FOMs given by Equations (2) and (3) to assess the material's performance in the longitudinal mode in resonance and off-resonance conditions respectively:  
\[\frac{K^{2}_{33}\times Q_{m}}{S_{11}}, \tag{2}\]  
\[\frac{d_{33}\times g_{33}}{tan\delta}, \tag{3}\]  
where: \(K_{33}\) is the coupling coefficient, \(d_{33}\) is the piezoelectric strain coefficient, \(S_{33}\) is the elastic compliance, \(Q_{m}\) represents the mechanical quality factor, and \(tan\delta\) represents dielectric losses.  
Therefore, in this method, the candidate material having the maximum FOM is considered as the optimum material for the given application. This method of material selection is considered as one of the most effective and efficient methods of material selection and is usually involved in design-related engineering activities [14].
## 6 Conclusions  
This paper, through dynamic triaxial testing of 12 groups of coarse-grained soil samples at varying relative densities, investigates the impact of relative density on the dynamic elastic modulus and the damping ratio (\(\lambda\)) of coarse-grained soils. The conclusions are as follows:  
1. The dynamic elastic modulus of coarse-grained soil progressively diminishes with increasing dynamic strain. The attenuation rate of dynamic elastic modulus escalates with increasing relative density; the rate of decay of the coarse-grained soil's normalized dynamic shear modulus with increasing dynamic shear strain also rises with an increase in relative density, with the attenuation trend becoming more distinct at higher relative density values. Additionally, the rate of increase in the damping ratio-dynamic shear strain relationship curve gradually decreases as relative density increases.
2. The maximum dynamic shear modulus of coarse-grained soil increases with an increase in relative density. An empirical formula has been established to describe the relationship between the maximum dynamic shear modulus and relative density of coarse-grained soil.
3. The maximum damping ratio of coarse-grained soil decreases progressively with increasing relative density. An empirical formula has been developed to delineate the relationship between the maximum damping ratio and relative density of coarse-grained soil.  
The conclusions drawn in this article are helpful for the construction of coarse-grained soil engineering such as dams and retaining walls, and the formulas obtained can provide reference for further research on the dynamic performance of coarse-grained soil.  
Conceptualization, [PERSON] and M.J.; methodology, [PERSON]; software, S.C.; formal analysis, [PERSON] and J.G.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, M.J.; writing--review and editing, M.J.; project administration, M.J. and J.G.; funding acquisition, M.J. and J.G. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support from a project from Guangxi Natural Science Foundation (No. 2021 GXNSFBA196091), a project from Guangxi Science and Technology (No. AA23023016), a project from Inner Mongolia Transportation Department Construction Technology (No. NJ-2021-10).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen Number**} & \multicolumn{3}{c}{**Maximum Damping Ratio**} \\ \cline{2-4}  & \(m\) & \(n\) & \(R^{2}\) \\ \hline S1 & 0.0072 & \(-0.554\) & 0.9533 \\ S2 & 0.074 & \(-0.58\) & 0.978 \\ S3 & 0.085 & \(-0.563\) & 0.957 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting results of Equation (5).  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 3 Results and Discussion
### Adsorption
The pH of the solution was equilibrated to 6.3, in which Zn was predominated in the form of Zn\({}^{2+}\) and ZnOH\({}^{+}\)[47]. Thus, zinc adsorption on P-200 appeared to occur via exchange of Zn\({}^{2+}\) and ZnOH\({}^{+}\) with surface-bond Na\({}^{+}\) and H\({}^{+}\) (Equations (15) and (16)). A similar observation was reported in Zn\({}^{2+}\) adsorption on groundwater treatment sludge, which had the similar \(\equiv\)SO\({}^{-}\) sites [23]. Of the three obtained adsorbents, P-200 had the optimal surface sites concentration (\(H\)s), and thus exhibited the highest adsorption capacity. \[2\equiv\text{SO}^{-}\text{Na}^{+}+\text{Zn}^{2+}\leftrightarrow \equiv\text{SO}^{-}\text{2}\text{Zn}^{2+}+2\text{Na}^{+} \tag{13}\] \[2\equiv\text{SO}^{-}\text{H}^{+}+\text{Zn}^{2+}\leftrightarrow \equiv\text{SO}^{-}\text{2}\text{Zn}^{2+}+2\text{H}^{+}\] (14) \[\equiv\text{SO}^{-}\text{H}^{+}+\text{Zn}^{2+}+\text{H}_{2}\text{O} \leftrightarrow \equiv\text{SO}^{-}\left(\text{ZnOH}\right)^{+}+2\text{H}^{+}\] (15) \[\equiv\text{SO}^{-}\text{Na}^{+}+\text{Zn}^{2+}+\text{H}_{2}\text{O} \leftrightarrow \equiv\text{SO}^{-}\left(\text{ZnOH}\right)^{+}+\text{Na}^{+}+\text{OH}^{-} \tag{16}\]  
The operation cost of the adsorbent P-200 for Zn\({}^{2+}\) adsorption was important in wastewater treatment. Synthesis of one ton of magnetic adsorbent needed 0.01 ton of ascorbic acid, 0.27 ton of caustic soda, and 45 kW h power, and the total production cost was US$ 158.8. Disposal of red mud accounted for about 5% of alumina production [48], and the alumina price was US$ 439 per ton in China [49]. The disposal cost of red mud was nearly US$ 22 per ton, which could be deducted from the production cost of P-200. Thus, the net cost of P-200 production was about US$ 136.8 per ton. For treating Zn\({}^{2+}\)-containing wastewater, the operation cost of P-200 was approximately US$ 1.45 per ton, lower than the widely used adsorbents, such as granular active carbon [50] and zero-valent nanometer iron [51], indicating that the application of P-200 for heavy metal removal was economically feasible.
## 3 Results and Discussion
### Analysis of Deformed Surfaces
#### 3.4.2 Deformed Surfaces of Samples Tested at 200 \({}^{\circ}\)C  
Figures 14-16 show the deformation features of the three different forms of LPBF Ti6 Al4V(ELI) samples tested at a temperature of 200 \({}^{\circ}\)C and at strain rates of 0.001 s\({}^{-1}\), 0.005 s \({}^{-1}\), and 0.1 s\({}^{-1}\). The micrographs in Figure 14 show that the failure surfaces of samples C were dominated by the growth and coalescence of microscopic voids and crack propagation. This is an important failure mechanism in ductile materials. Adiabatic shear bands were not apparent in samples C, as shown in this figure, in contrast to what was observed at room temperature. This suggests that the applied strain rates at this strain of about 30% imposed on this material at this temperature were not sufficient to cause the localization of plastic flow.  
The presence of ASBs and cracks propagating along them was evident in samples D and E at all testing conditions, as shown in Figures 15 and 16. This is similar to the observation made for the two forms of LPBF Ti6 Al4V (ELI) tested at room temperature, as shown in the micrographs in Figures 12 and 13. This suggests that the applied strain rates at this strain of about 30% were sufficient to cause the localization of plastic flow in samples D and E at a temperature of 200 \({}^{\circ}\)C. The difference in observations made for samples C and those of samples D and E can be attributed to the material's hardness.  
Figure 13: Typical microstructure of samples E deformed at ambient temperature and a strain rate of (**a**) 0.001 s\({}^{-1}\), (**b**) 0.005 s \({}^{-1}\), and (**c**) 0.1 s\({}^{-1}\).  
Figure 14: Typical microstructure of samples C deformed at a temperature of 200 \({}^{\circ}\)C and strain rates of (**a**) 0.001 s\({}^{-1}\), (**b**) 0.005 s \({}^{-1}\), and (**c**) 0.1 s\({}^{-1}\).  
Figure 15: Typical microstructure of samples D deformed at a temperature of 200 \({}^{\circ}\)C and strain rates of (**a**) 0.001 s\({}^{-1}\), (**b**) 0.005 s \({}^{-1}\), and (**c**) 0.1 s\({}^{-1}\).  
The difference in the observations made for samples C, and samples D and E can be attributed to the hardness of the different forms of the alloy. Generally, one of the critical factors that influences the presence and thickness of ASBs is the hardness of materials [45], and it is known that the thickness of ASBs diminishes with the increasing hardness of materials. The localization of deformation is enhanced when the resistance to deformation decreases with strain. It is further enhanced by strain rates, weakening regions where stress concentration is high [45; 46; 47]. Therefore, flow stress localization, which appears in the form of ASBs, may fail to occur in harder materials due to their high resistance to softening and the attendant formation of localized shear bands [47].
# Stimulation Montage Achieves Balanced Focality and Intensity
## 3 Results  
### Study with Single Target  
The advantages of using multiple electrodes instead of the conventional system with two large pad electrodes can be demonstrated in the results of the electric field distribution with a single target in Figure 1A,B. Figure 1C shows the electrode configurations calculated by different algorithms. Note, the configuration only shows the electrodes whose current is larger than 1 uA. The conventional montage produces a maximum electric field of 0.4385 V/m with a focality of 63.5043 mm. The figure clearly indicates this spreadout distribution not only activates the target in the motor cortex, but also the entire left hemisphere. A target error of 28.3738 mm shows that the conventional montage does not create an accurate stimulation either. The performance of the MI algorithm is similar to the conventional montage with even higher electric field intensity and worse focality and accuracy, which is as expected. The MI method maximizes the intensity at the target reaching 0.7449 V/mm with only four electrodes. Although its intensity is more than 1.5 times higher than conventional system, it affects larger brain regions, especially the frontal lobe. The focality and target error are 81.9083 mm and 29.1206 mm, respectively. The CLS method has the opposite results of the MI algorithm and conventional montage. CLS produces an extremely precise and accurate stimulation pattern among all methods with a focality of 10.8616 mm and target error less than 1 cm. However, the outcome intensity of CLS is around 17 times lower than the desired value. Such a low intensity may not be clinically efficacious. Additionally, this montage requires 80 electrodes, which introduces additional drawbacks compared to the other methods. As for the LCMV and the proposed SBFI method, Figure 1 shows that the performance of those two are comparable. Both successfully find a balance between MI and CLS algorithms with around 20 electrodes. The desired intensity 0.3 V/m at the target region is achieved, while the focality and target error are within control. The focality of LCMV is 25.2921 mm, and its target error is 9.6710 mm. The proposed SBFI method with \(\lambda=0.0316\) is slightly better at the focality of 22.1130 mm and target error of 7.3776 mm. Compared to the conventional montage and MI algorithm, these dramatic improvements in focality and target error indicate that more energy is focused in a smaller region surrounding the target point, which promises more effective and safe stimulation.
## 6 Discussion and Summary
### SVM SHM Highlights  
* To ensure a good level of generality in structural damage identification capabilities, sufficient examples of damage scenarios should be provided in the model training step. The data generated by the FEM could be useful in scenarios where the damage state of the building is not available. Classification errors may occur when unlearned pattern scenarios are fed to the SVM model.
* It is essential to keep the number of SVM features to a computationally feasible number due to the computational processing cost. To reduce this cost, dimensionality reduction techniques such as PCA, random projection and boosting techniques are encouraged.
* The performance of the final SVM depends critically on the damage-sensitive features selected in the model input. Popular selected damage features include ARM features, wavelet-based energy features, and statistical features. If denoising techniques are not applied, the final performance may also be affected [45].
* There is a high dependence between the selected hyperparameters of the SVM and its performance, so the use of optimization techniques for hyperparameter tuning, such as PSO, PSH, CSA and GA, is recommended. More recent optimization techniques include sunflower optimization [144].
* The model training time using SVM approaches might be inadequate for model update stages in dynamic SHM building models and real-time building monitoring systems.
* SVM approaches show high capability for small structural damage identification [104] in combination with feature selection techniques.
## 4 Architecture and Data Workflow of UGVs  
UGVs for agricultural monitoring come in various designs, differing in size, structure, and operational concepts. Most have typical dimensions in the range of 0.5-1 m to facilitate access to crops with narrow interrow spacing, weigh 100 kg or less, and are powered by electric motors using internal batteries (this is not always the case for UGVs designed for crop treatments, which require more power to operate hydraulic pumps and other mechanical tools, often relying on internal combustion engines). Figure 2 shows an effective robot development platform for monitoring, as described in detail in the Introduction. While this example features a skid-steering rover with rubber tracks, other designs using wheels with different drive systems are also possible. Each option has its advantages and drawbacks (e.g., wheel-based systems generally offer a higher operational speed but may struggle in uneven or soft soils, particularly in muddy conditions). Since this review focuses primarily on ICT aspects, we do not delve into vehicle motion models or other mechatronic issues. For those topics, interested readers can refer to [2; 4; 10; 21; 22; 23].  
The functional diagram in Figure 3 illustrates the main components of an exemplary UGV designed for precision agriculture tasks, including optical and positioning sensors, computational modules, and potentially actuators. It also outlines the data flows between these components, demonstrating how sensory inputs are processed and transformed into actionable insights [25]. For simplicity, the diagram groups activities such as harvesting, pruning, and spraying under the term 'field operations' and omits ancillary components, such as the power supply system that controls the orientation of photovoltaic (PV) panels [26]. The'remote server' box accounts for the option to perform data exchange using cloud protocols either during field monitoring (via wireless communication network, WCN) or afterward, through WCN or physical links/devices (e.g., cables, memory device transfers). This step is well-suited for non-real-time operations, such as the generation of prescription maps or information for digital twins.  
Figure 2: On the left: a typical robot development platform for agricultural monitoring. On the right: a simplified diagram of the main monitoring and navigation components (dimensions are in mm). Sensors are typically mounted on an external frame atop the UGV, offering a ‘human-like’ perspective and easy access to the devices. In many cases, two or more cameras are installed, facing the left and right crops. The actual number and placement of devices on the UGV may vary; the diagram is for conceptual purposes only. The image and diagram (modified by the authors) were taken from [24] with preliminary authorization.
## 3 Related Work
### Why Is Energy Efficient Channel Coding Important for NB-IoT  
One of the most important issues in the design of NB-IoT systems is error correction. If well designed, the channel coding technique for NB-IoT can help save a considerable amount of energy by significantly reducing the number of required retransmissions. This justifies the fact that several research studies have proposed channel coding techniques to achieve energy efficiency. In its current form, the NB-IoT uplink baseband processing can be divided into channel coding and modulation. In the case of the NB-IoT uplink, the channel coding includes Cyclic Redundancy Check (CRC) generation and attachment, turbo or convolutional coding, and rate matching. Reliability is a key performance criterion in any form of wireless communication including the NB-IoT. It is therefore crucial that any NB-IoT design ensures that end-to-end communication occurs reliably between terminals. As in most engineering designs, the effort to enhance one performance often comes with an associated cost in terms of the other. Channel coding is often used in most communication systems to ensure resilience to channel impairments and ensure reliable link transmissions. Therefore, previous research work has proposed various channel coding approaches to ensure reliability. The main identified approaches have been the traditional repetition approach and the Narrowband Link Adaptation (NBLA) approach [3]. Despite their attempt to enhance communication reliability, the approaches have been shown to suffer significant energy costs. This issue is what the present research work addresses. It strives to find a balance between ensuring reliable communication on NB-IoT uplinks while maintaining energy efficiency.