esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Section C: Varroa Knowledge and Biosecurity Measures Adopted  
We also asked questions regarding the knowledge and management of honey bee diseases. Most of the respondents correctly identified the presented picture of varroa mites (98% in the Americas and 97.5% in Europe). For the question on the level of knowledge of varroa and its management, most of the respondents in the Americas and Europe said that they have observed varroa mites in their beehives, while a lower percentage in the Americas (9.5%) and in Europe (2.3%) never saw the mites at all. However, most of the beekeepers who saw varroa multiple times reported they were \"moderately knowledgeable\" on the disease (45.6% in the Americas and 37.3% in Europe) or \"very knowledgeable\" about it (23.1% in the Americas and 30.2% in Europe).  
Table 2 shows the mean usefulness scores for each prevention and control practice in the Americas and in Europe. \"Maintaining the number of varroa below the harmful threshold in each colony\" was the most commonly reported practice for both regions, with virtually no differences between the two. \"Having good knowledge of the signs of varroosis and virosis\" was considered highly useful in both regions but was considered a somewhat more effective prevention and control measure in Europe compared to the Americas (mean 1.81 versus 1.67, \(p<0.05\)). The respondents noted high values for \"treating swarms (no brood) just after harvest\" in Europe (mean = 1.68), with medium values in the Americas (1.32, \(p<0.001\)). The practice of \"adopting diagnostic tools for measuring varroa infestation levels after treatments and during the year\" was considered highly useful in both regions, with significantly higher mean values in the Americas (1.72) compared to Europe (mean = 1.55, \(p<0.01\)). \"Adopting/providing hives with screened bottom boards\" was considered a moderately useful strategy in both regions with the practice considered somewhat more useful in Europe than in the Americas (\(p<0.001\)). With the exception of the practices noted above, there were few differences between the Americas and Europe when it comes to the perceptions of the usefulness of varroa preventions and control measures.
## 4 Pitch Angle Control Strategy for Reducing Load and Torque Pulsation
By using the proposed pitch angle control strategy, the fluctuating component of hydrodynamic frequency in aerodynamic torque is reduced, as well as the 1P aerodynamic load fluctuation and 3P aerodynamic torque fluctuation of the wind turbine. The block diagram of variable pitch control is shown in Figure 3.  
The transfer function of the low-pass filter in Figure 3 can be written as  
\[G(s)= \frac{\lambda_{0}s^{2}+\lambda_{1}s+\lambda_{2}}{\mu_{0}s^{2}+\mu_{1}s+ \mu_{2}} \tag{20}\]  
where \(s\) is the Laplace operator, and \(\lambda_{0}\), \(\lambda_{1}\), \(\lambda_{2}\), \(\mu_{0}\), \(\mu_{1}\), \(\mu_{2}\) are transfer function parameters. The proportional and integral coefficients of the PI controller can be expressed as \(K_{p}\) and \(K_{i}\), so \(\Delta\beta\) can be expressed as  
\[\Delta\beta=K_{p}P_{3P}(t)+K_{i}\int_{0}^{t}P_{3P}d\tau(\tau) \tag{21}\]  
where \(P_{3P}(t)\) is the 3\(P\) fluctuating component of the output power filtered by the low-pass filter. Wind shear and tower shadow make the wind speed captured by each blade in the process of rotation closely related to the azimuth angle signal \(\theta_{i}\) of the blade [38]. If the pitch angle of each blade changes periodically with the blade azimuth angle, the load fluctuation caused by the non-uniform wind speed will be effectively suppressed. The pitch angle of each blade can be written as  
\[\Delta\beta_{i}=cos\theta_{i}\Delta\beta \tag{22}\]  
\[\beta_{i}=\beta^{\prime}_{ref}+\Delta\beta_{i} \tag{23}\]
## 5 Performance Evaluation Using OPNET Testbed
### Large Network
#### 5.2.2 Mean End-to-End Delay  
Figure 23 shows the mean end-to-end delay performance for the highway scenario in the large network for varying buffer sizes and service rates. Observations are similar to those of Figure 18 and hence the same discussion applies, except the mean end-to-end delay is higher in this case due to the increase in network size.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Data Accessibility  
Data accessibility and management are some of the most frequently occurring issues in the WHNCS because of the limited storage availability and may even lead to end-to-end packet delay [81]. Sensors installed in particular industrial environments can send identical data, which often leads to unnecessary data aggregation and the need to process a massive amount of metadata. To avoid this problem, motes can be designed so as to filter the non-critical data using predetermined conditions or distributed source coding (DSC) to compress the raw data before sending them to the sink [125]. These methods can significantly improve data scalability, device versatility, and battery life [126]. Alternative techniques, such as low-energy adaptive clustering hierarchical (LEACH), power efficient gathering in sensor information systems (PEGASIS), and multi-hop routing protocol with unequal clustering (MRPUC), can be combined into clusters in order to overcome the problem of data transmission for a large group of nodes [127]. However, the research trends show that an increase in the network size increases the computational and communication overhead of the WHNCS.
## 1 Introduction
The number of objects in each VisDrone2019 sample is uniformly distributed between 10 and 300, while the number of objects in each COCO sample is mostly less than 20. The percentage of small objects (with a ratio of 0.05 to the entire background) in VisDrone is up to 72.45%. The above study established a meaningful foreground by setting a fixed mask ratio, which reduces the number of parameters and the number of floating-point operations. However, a fixed mask ratio ignores the fact that the size of the foreground region varies with the flight altitude and viewing angle, reducing the flexibility of the network. An SSD is leveraged in this paper, and a novel model, namely a multi-scale feature fusion and context-enhanced spatial sparse convolution SSD (MFCESSC-SSD), is proposed to facilitate effective object detection in UAV images. First, a multi-scale feature fusion (MFF) scheme is defined to project and concatenate features obtained from different scales followed by a batch normalization (BN). Self-attention units (SAUs) are used to capture the internal correlation between features and suppress the effect of background noise while obtaining global contextual information. Then, down-sampling and horizontal connection are applied to generate a new feature pyramid, which is then fed to the detection heads. Second, context-enhanced spatial sparse convolution (CESSC) is developed to optimize the detection heads and enhance meaningful areas within the foreground, which comprises context-enhanced group normalization (CE-GN) and an optimal mask estimation mechanism based on ground-truth labeling. MFF merges the local detailed and global semantic features to confirm the detection of small objects in UAV images, significantly improving detection accuracy. Meanwhile, CESSC aims to optimize the detection heads in different layers and integrate focal and context-enhanced information via limiting computations by only performing convolution on sparse regions. Therefore, the MFCESSC-SSD can reach a successful balance between accuracy and efficiency. The MFCESSC-SSD is evaluated on VisDrone2019 datasets. The result demonstrates the effectiveness of the proposed approach. This method is further extended to practical detection tasks, such as identifying rodents in grassland based on UAV images, demonstrating the superiority of the proposed approach in dealing with small objects in vision tasks. The contributions of the current study are listed as follows:  
1. A multi-scale feature fusion scheme is designed based on the feature maps of an SSD (i.e., MFF) to add global semantic features to the shallow feature maps, while context mining and self-attentive learning are integrated into an SAU to obtain global contextual information. 2. Context-enhanced spatial sparse convolution (i.e., CESSC) is performed to reinforce global contextual information and enhance focal features, while a dynamic mask ratio mechanism is proposed that can be automatically updated based on the information of the feature layer.
## 4 Comparisons of Results for DFE, MLD Algorithms
### Equalization by Decision Feedback Equalizer  
The DFE equalization is performed on the distorted data obtained at the OFDM receiver. Two (2) feedback taps are used for QPSK and 16-QAM distorted constellations,  
Figure 3: Constellation diagrams and EVM measurements for the QPSK, 16-QAM, 64-QAM, and 128-QAM modulation using MLD equalization, (**a–d**) respective constellation diagrams before equalization and (**e–h**) after equalization.  
while four (4) and six (6) feedback taps are used for 64-QAM and 128-QAM distorted constellations for equalization.  
Figure 4 shows the constellation diagrams of QPSK, 16-QAM, 64-QAM, and 128-QAM obtained before and after the equalization at the maximum fiber length that can be achieved before (a, b, c, and d) and after equalizing (e, f, g, and h) for the respective modulation formats. After equalization, the EVM of the equalized constellations is computed.  
The proposed DFE performance is evaluated over various fiber lengths. For QPSK modulation in OFDM subcarriers, with a maximum fiber length of 120 km, an EVM of 16.60% is achieved, while for 16-QAM modulation, at a maximum fiber length of 100 km, the EVM is 12.32% after equalization. In the cases of 64-QAM and 128-QAM modulations, EVMs of 7.19% and 4.73% are achieved for fiber lengths of 50 km and 40 km, respectively. Notably, all the equalized EVM values are below the 3 GPP threshold.  
Table 4 presents a comparison of MLD and DFE algorithms in terms of EVM for different modulation formats. DFE demonstrates marginally superior performance compared to MLD regarding EVM. The primary reason for this is DFE's capacity to utilize input from previously identified symbols, which can statistically reduce the variance of the error to mitigate interference, leading to a cleaner signal and reduced EVM. However, the enhancement in performance may be minimal, as MLD remains an ideal detection method.  
Statistically, MLD identifies the greatest likelihood sequence that corresponds to the received signal, based on the channel model. In this regard, MLD can rectify errors utilizing all available information simultaneously; however, it lacks the advantages of feedback. In a noisy channel with ISI, the MLD is ideal as it reduces the probability of bit errors by evaluating all potential symbol sequences, but its statistical robustness can be affected by errors in detecting symbols that introduce incorrect decisions. It doesn't use prior decisions to correct subsequent symbol detection errors. Since DFE leverages feedback, it  
Figure 4: Constellation diagrams and EVM measurements for the QPSK, 16-QAM, 64-QAM, and 128-QAM modulation using DFE equalization: (**a–d**) respective constellation diagrams before equalization, and (**e–h**) after equalization.  
can statistically reduce the impact of previous symbol errors on future decisions, hence slightly better EVM than that of MLD.
## 3 Results
### Ops-Da  
The statistics were then supervised by an OPLS-DA analysis (Figure 3B,C). OPLS-DA results showed that the two classes of honeycomb were well-separated, with Q2 = 0.896, R2Y = 0.926, and R2X = 0.251, and with ACC and AMC samples falling in the 95% confidence ellipse regions (Figure 3B). A total of 21 compounds with a VIP value greater than 1 were selected as potential markers (\(p<0.05\)).  
In summary, honeycomb from different entomological origins were discriminated using a chemometric method based on GC-MS data. Eight VOCs (2-phenylethanol, dihydrooxophorone, ethyl phenylacetate, ethyl palmitate, betula, phenethyl acetate, isophorone, and 4-Oxoisophorone) were selected as potential markers for ACC, whereas thirteen VOCs (linalool, illac aldehyde A, ocime, \(\alpha\)-terpineol, limonene, decanal, benzaldehyde, furfural, ethyl octoate, octanal, dehydro-ar-ionene, acetylfuran, and \(\alpha\)-cedrene) were selected as potential markers for AMC (Figure 3C).
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 2 On-Line Solid-Phase Extraction (On-Line SPE)
### SPE Formats  
SPE has been employed in different formats (that may vary in size) the most popular ones are: (1) Cartridges (usually in 1 mL syringes to 6 mL syringes), (2) disks (47 mm in diameter), (3) SPE pipette tips, and (4) 96-well SPE microliter plates (using 1 mL disks) [15]. The cartridge is the most commonly used design; it consists of a syringe barrel containing a sorbent phase fitted between two 20-\(\upmu\)m frits. These frits are porous and usually made of polyethylene (PE), polytetrafluoroethylene (PTFE) or stainless steel.  
Another format is the extraction disk; it contains particles usually between 8-12 \(\upmu\)m placed in an inert matrix. The main advantage of the disks is that the samples are passed through it at higher flow rates, which is very important in the analysis of traces of organic compounds in water, allowing shorter extraction times. Even so, standard SPE cartridges continue maintaining their advantage concerning the other formats. In SPE, the search continues for new formats that use less materials, presents higher performance capabilities and are easier to be employed for the development of future process extraction [15].  
One of the main SPE drawbacks is the ease of blockage of the sorbent pores by small particulate matter present in the different (activation, washing, and elution) solvents or by endogenous matrix compounds. Besides, the large number of stages needed to carry out this technique may facilitate contamination of the original sample during the process. This explains why SPE continued to be improved towards a more environment and analyst friendly technique, originating the nowadays-termed miniaturized techniques. This group of techniques has been highlighted against conventional ones by their possibility of minimizing both the consumption of toxic organic solvents and the volume of sample used. In addition, the extraction phases can be reused (in opposition to the dischargeable conventional SPE cartridges), a better pre-concentration of the analytes is obtained and it is possible to directly couple them with chromatographic systems, such as in the case of solid-phase microextraction (SPME) [4], solid-phase dispersive extraction (dSPE) [16], stir-bar sorbent extraction (SBSE) [17] and microextraction in packed sorbent (MEPS) [18]. These techniques allow extracting and concentrating the solute in the same stage, which reduces the potential loss of solutes and analysis time. In addition, when directly coupled to chromatographic techniques, the smaller number of steps to be employed reduces human experimental errors. As a result of these characteristics, these micro techniques make it possible to determine compounds in various complex matrices such as food and beverages, environmental samples and biological fluids at trace levels. Although, in this review not all techniques are going to be addressed, it is important to bear in mind that they have been essential in the advancement of sample preparation techniques.
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 4 The PSO Method for DP of Ship
### Application of PSO Algorithm for DP of Ship  
In this work, the PSO was used to tune the modified passivity-based controller for improving the performance of a dynamic positioning system in a surface ship. The design space is the controller gains which are the diagonal elements in ex. \(K_{q}\) matrix:  
\[X_{Design}=(k_{q_{1}},k_{q_{2}},k_{q_{3}}). \tag{21}\]  
Figure 3: The flow-chart of the particle swarm optimization (PSO) algorithm.  
The objective that used in this optimization is the performance index integral square error (ISE) which can be defined for the three outputs (x-position, y-position and yaw angle (\(\psi\))):  
\[ISE=\int_{t_{0}}^{t_{f}}e_{x}(t)^{2}+e_{y}(t)^{2}+e_{\psi}(t)^{2}, \tag{22}\]  
\(e_{x}(t)\) is the error in the x-position, \(e_{y}(t)\) is the error in the y-position and \(e_{\psi}(t)\) is the error in the yaw angle. We used this method (the summation of the three errors) in preparing the performance index as in [36].
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 3 Design of an Adaptive Fault Tolerance Mechanism  
We can see in the literature a lack of research and development related to low-level hardware-implemented AFT mechanisms devoted to detect and tolerate intermittent faults. With this work, we intend to fill this gap and propose a fault-tolerance mechanism able to detect intermittent faults in the RAM and adapt its operation to increase the protection of the faulty bit without increasing the redundancy.  
In addition, we have inserted this AFT mechanism into the VHDL model of the Plasma microprocessor [24]. Plasma has a 32-bit MIPS architecture with a four-stage pipeline. The VHDL model of Plasma is described at register transfer (RT) and Logic abstraction levels.
# Development of Building CFD Model Design Process Based on BIM
## 3 Design Method of Optimized CFD Model Using BIM
### Grid Optimization to Optimize CFD Model  
The optimal grid design is required to ensure the accuracy and economic feasibility of CFD analysis. Grid quality and the number of grids should be considered for optimal grid design. The optimal grid conditions are determined through grid independence tests. Grid independence test is conducted in most CFD studies, however there are no standardized methods or procedures for these tests [31]. In this respect, [PERSON] et al. [1] proffered and validated an improved grid independence test method by applying the grid resolution used in fire simulations.  
This study developed this method into an algorithm to implement a grid optimization function for the subject of analysis. The grid optimization process is as follows. The characteristic length of the building to be analyzed is calculated using the extracted IFC geometric information. Then, an appropriate grid resolution is selected for the calculated characteristic length. The minimum grid size is calculated about the appropriate grid resolution and is then entered into the OpenFOAM grid creation script. cfMesh was used as a tool to design hexahedral grids, and the simplified geometry model described in Section 3.2 was used as the target to create the grids.
## 5 Conclusions  
In this study, the mechanical properties of rubber fiber-reinforced expansive clay under freeze-thaw cycles were investigated and compared with those of unreinforced expansive clay. The main conclusions are as follows.  
1. The mixing of rubber fibers reduced the effect of freeze-thaw cycles on the shear strength and elastic modulus of expansive clay. The shear strength and elastic modulus of rubber fiber-reinforced expansive clay decreased slightly with an increase in the number of freeze-thaw cycles. However, the shear strength and elastic modulus of the unreinforced expansive clay decreased rapidly with an increase in the number of freeze-thaw cycles.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & _c/kPa_ & **tan\(\varphi\)** & **\(M\)** & **\(\sigma_{3}\)/kPa** & **\(N\)** \\ \hline This study & 7 & 0.275 & 0.0210 & 100, 200, 300 & 0, 1, 3, 6, 10, 15 \\ [PERSON] et al. [49] & 6 & 0.575 & 0.0166 & 100, 200, 300 & 0, 1, 3, 5, 9 \\ [PERSON] et al. [50] & 30 & 0.395 & 0.0080 & 50, 100, 200, 300 & 0, 1, 4, 6, 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters.  
Figure 11: Test results and calculation results with different test conditions. (**a**) \(N=10\) in this study; (**b**) \(\sigma_{3}=300\) kPa in [PERSON] et al. [49]; (**c**) \(\sigma_{3}=200\) kPa in [PERSON] et al. [50].  
2. The relationship between the number of freeze-thaw cycles and the ultimate strength was established, which can describe the change in ultimate strength under freeze-thaw cycles. The ultimate strength of rubber fiber-reinforced expansive clay does not change markedly as the number of freeze-thaw cycles increases, and the deterioration parameter \(\lambda\) is 0. In contrast, the ultimate strength of the unreinforced expansive clay significantly decreased after freeze-thaw cycles, with the deterioration parameters as 0.336 and 0.275.
3. A calculation model of the deviatoric stress-axial strain curves after freeze-thaw cycles was established, which can describe the deviatoric stress-axial strain behavior of rubber fiber-reinforced expansive clay and unreinforced expansive clay under different confining pressures and numbers of freeze-thaw cycles.  
This paper investigated the static response of rubber fiber-reinforced expansive soil under freeze-thaw cycles. The subgrade soils are subjected to dynamic loads, and future research on the dynamic response of rubber fiber-reinforced expansive soil under freeze-thaw cycles can be carried out.  
Conceptualization, Z.Y.; data curation, writing--original draft, Z.S.; methodology, X.L.; supervision, G.C.; project administration, R.W. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42177153, and the National Major Scientific Research Instrument Development Project, grant number 41627801.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.