esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Quantitative Findings  
A total of 44 GPPs participated in the study, representing all six counties of Northern Ireland. The sample was predominantly female, with the largest age group falling between 34 and 39 years. Most participants had 4-6 years of experience in the GPP role, and most of the participants were working full-time. Table 1 summarises the demographic characteristics of the participants.  
Participants were asked about the tasks they performed in their role as GPPs. All of them reported conducting medication reviews and reconciliations. The most commonly managed chronic conditions were cardiovascular diseases (68.2%), followed by respiratory conditions, including asthma and COPD (54.5%). Other common responsibilities included managing repeat prescriptions (97.7%) and answering medicine-related queries (95.5%). Figure 1 shows the distribution of tasks carried out by GPPs.  
Table 2 represents the relationship between demographic characteristics and confidence levels of the participants. Age positively influenced confidence, with GPPs aged over 45 years reporting the highest confidence levels (\(p=0.031\), \(\chi^{2}=7.91\)). Confidence increased with years of experience as a GPP, with those having 10 or more years of experience in  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Demographic Characteristic** & **Number (n = 44)** & **Percentage (\%)** \\ \hline Gender & & \\ Female & 32 & 72.7 \\ Male & 12 & 27.3 \\ \hline Age Group & & \\
28–33 years & 10 & 22.7 \\
34–39 years & 20 & 46.3 \\
40–45 years & 7 & 16.0 \\ \textgreater{}45 years & 7 & 16.0 \\ \hline Years of Experience as a Pharmacist Before GPP & & \\
1–2 years & 4 & 9.1 \\
3–5 years & 25 & 56.8 \\
6 years or more & 15 & 34.1 \\ \hline Years of Experience as a GPP & & \\
1–3 years & 4 & 9.1 \\
4–6 years & 25 & 56.8 \\
7–9 years & 10 & 22.7 \\
10 years or more & 5 & 11.4 \\ \hline Employment Type & & \\ Full-time & 26 & 59.1 \\ Part-time & 18 & 40.9 \\ \hline County & & \\ Derry/Londonderry & 2 & 4.3 \\ Antrim & 18 & 41.3 \\ Tyrone & 8 & 17.4 \\ Fermanagh & 3 & 6.5 \\ Down & 5 & 10.9 \\ Armagh & 9 & 19.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of participants.  
the role reporting the highest levels of confidence (\(p\) = 0.024, \(\chi^{2}\) = 13.78). Employment type was another significant factor, with full-time GPPs exhibiting higher confidence than part-time GPPs (\(p\) = 0.043, \(\chi^{2}\) = 9.34). Gender differences in confidence levels were not statistically significant.
## 2 Materials and Methods
### Data Gaps Filling and NEE Partitioning  
The filtered NEE and meteorological data gaps were filled with appropriate methods considering the co-variation of the fluxes with meteorological variables and the temporal auto-correlation of the fluxes [37], according to the online procedure ([[https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWebMethod](https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWebMethod)]([https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWebMethod](https://www.bgcjena.mpg.de/bgi/index.php/Services/REddyProcWebMethod))). Following this, the mean diurnal method (MDM) was applied to fill residual gaps found in the dataset.  
Since only the NEE can be measured directly by the eddy covariance method, it was partitioned using the same online procedure to gain gross primary production (GPP) and ecosystem respiration (Re), in accordance with Equation (1) [37]. First, an activation energy parameter (\(E_{0}\)) was estimated for the yearly dataset. Next, the reference temperature (\(R_{\text{ref}}\)) was estimated with a seven-day sliding window in steps of four days. If \(R_{\text{ref}}\) was not found in some sub-periods, its value was linearly interpolated. Third, the NEE was partitioned into GPP and Re using the estimated values of \(E_{0}\) and \(R_{\text{ref}}\). Only the nighttime original data after QA/QC were used in the estimation of \(E_{0}\), and the other datasets (i.e., the gap-filled data) were used in the partitioning of NEE:  
\[\text{Re}=R_{\text{ref}}e^{E_{0}(\frac{1}{T_{\text{ref}}-t_{0}}-\frac{1}{T_{ \text{a}}-t_{0}})} \tag{1}\]  
where the \(R_{\text{ref}}\) was the respiration rate at reference temperature (\(T_{\text{ref}}\), \(15\)\({}^{\circ}\)C), \(E_{0}\) was a free parameter, which essentially determined the temperature sensitivity and \(T_{0}\) was kept constant at \(-46.02\)\({}^{\circ}\)C [37]. To compare the magnitude of carbon dioxide flux components, the NEE was represented by net ecosystem production (NEP = \(-\)NEE), and together with GPP and Re were given as positive values at monthly and yearly time-scales, and which were also used to determine the relationship with meteorological factors at different time-scales.
## 4 Validation and Comparison
### WOA-Based Output Weight Optimization  
In path loss prediction, integrating multiple neural network models improves prediction accuracy, and further enhancement of overall performance can be achieved by optimizing the weight combinations of these models. The WOA used in this study configures the output weights of the models to effectively reduce prediction error. The optimized weights are applied to the integrated model and validated on the test set to further confirm its applicability in real-world scenarios. The algorithm parameters are shown in Table 4.  
Figure 11 illustrates the variation of MAPE across generations during the WOA optimization process. The results show a rapid decline in MAPE during the initial generations, followed by stabilization after the fourth generation.  
In the early iterations, WOA explores a broad solution space, quickly reducing MAPE as it searches for optimal weight combinations. As convergence progresses, the rate of improvement slows, and MAPE stabilizes, indicating that the algorithm has approached the optimal solution. This rapid convergence highlights WOA's efficiency in weight optimization, demonstrating its capability to effectively minimize errors in the early stages and achieve stable performance in later iterations.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Population size & 50 \\ Max Iterations & 30 \\ Initial value of A & 2 \\ Final value of A & 0 \\ Value of B & 1 \\ Range of C & \([0,2]\) \\ \hline \end{tabular}
\end{table}
Table 4: Parameters of the WOA algorithm.  
Figure 11: WOA iterative optimization curve.  
According to Table 5, the weight configurations of the four finally selected neural networks in the integrated model are as follows: the 1 st neural network weights 0.3484, indicating its significant role in the prediction results; the 2 nd neural network weights 0.0035, contributing the least, reflecting its limited impact in the integrated model; the 3 rd neural network weights 0.4153, contributing the most and significantly affecting the final prediction results; the 4 th neural network has a weight of 0.2326, showing a moderate level of contribution. This weight configuration fully reflects the relative importance of each neural network in the integrated model. The integrated model optimizes the combination of predictions from multiple networks through differentiated weight allocation, thereby improving overall performance.
# Thickness-Stretch Vibration of an Infinite Piezoelectric Plate with Flexoelectricity
###### Abstract  
In this paper, the thickness-stretch vibration of an infinite piezoelectric plate is studied, with consideration of the flexoelectric effect. The theoretical model developed herein is based on a one-dimensional formulation, with the assumption that the displacement and electric potential vary through the thickness. The Gibbs energy density function and variational principle are adopted to derive the constitutive equation with flexoelectricity, governing equations, and boundary conditions. For the effect of flexoelectricity, the coupling between the strain gradient through the thickness and the electric field is considered. Two electric boundary conditions are used in this work and the corresponding frequency shift due to the flexoelectricity is calculated. The present results demonstrate that the flexoelectric effect decreases the fundamental frequency of the thickness-stretch vibration and has a significant effect on the vibrational frequencies of the thickness-stretch mode of a thin piezoelectric plate. The results also show that the flexoelectric effect has a significant size dependency, and should be taken into consideration for the design and development of next-generation high-precision and high-frequency piezoelectric transducers and resonators in the future.  
flexoelectricity; piezoelectricity; thickness-stretch vibration; size effect; frequency shift +
Footnote †: journal: Applied
## 5 Results and Analysis
### Received Power
The gain of the antennas and the permittivity values of the environmental material are described in Table 2.  
The spatial variations of the channel are determined by moving the transmitting antenna on a rectangular grid of 7 \(\times\) 5 by considering a static channel environment and fixed receiver. The grid spacing is considered to be 7 cm for the operating frequency of 2.5 GHz and 5 GHz to obtain the independent samples. The effect of the mobile receiver is captured as well, between link distances of 10 to 500 m for fixed transmitters. The transfer function of the channel and received signal power is measured using a Vector Network Analyzer (VNA HP8753 ES) and Matlab, as shown in Figure 4a. Each transfer function is obtained for the frequency bands of 2.4 GHz to 5 GHz by taking 1601 frequency gap points with the spectral resolution of 1.87 MHz. The measurements taken in frequency response are easier for calibration under the conditions of a static environment. The low-loss coaxial cables are used with the transmitting and receiving antenna. The calibration for the attenuation of connectors and loss due to coaxial cable is considered while computing the received signal power. The transmitting antennas are located at positions (2, 3, 3), (0.5, 2.5, 5) and (2.5, 2, 7) and the receiving antennas are placed at (2, 3, 10), (0.5, 2.5, 50), and (2.5, 2, 100). The measurements are carried out for the range of link distance from 10 to 500 m for LOS and NLOS cases by splitting the area of the gallery due to the non-uniform structure of the mine. A set of three measurements are taken for each link distance by varying the transmitter positions, and after taking these values, the receiving antenna is switched to another location for the consideration of the next three values. The antenna polarizations at corresponding locations are taken carefully against the propagation loss and the link distance is varied up to 500 m for the collection of the measured data. The channel of the propagated signals is considered stationary during the measurement, and the received signal power is collected from multiple locations including the propagation loss and SNR. Figure 7 shows the comparison analysis of theoretical and experimental received power at 2.4 GHz and 5 GHz. It can be seen that the behavior of the theoretical received power is nearly accurate even for the different operating frequencies. A minor deviation of approximately 12 to 16% can be noticed which validates the predicted received power values by the suggested approach.
## 4 Descriptions of Different Topologies of WSNs
### Cluster-Based Topology
#### 4.2.2 Cluster Types  
There exist many different ways to classify the clusters. Two of the most common classifications are homogeneous or heterogeneous clusters and static or dynamic clusters. The former classification is based on the characteristics and functionality of the sensors in the cluster, whereas the later is based on the method used to form the cluster.  
In homogeneous networks, all nodes have the same characteristics, hardware and processing capabilities. The cluster head role is periodically rotated among the nodes to balance the load, ensure that sensors consume energy more uniformly, and try to avoid the black hole problem.  
In heterogeneous sensor networks, there are generally two types of sensors:  
(a) Sensors with higher processing capabilities and complex hardware, used generally to create some sort of backbone inside the WSN. They are designated as the cluster head nodes, and therefore have to serve as data collectors and processing centres for data gathered by other sensor nodes.  
(b) Participating sensors, with lower capabilities than the previous ones, used to actually sense the desired attributes in the field.  
Static clusters are usually created when the network is formed of heterogeneous nodes and the network designers want to create the clusters around the more powerful nodes. In this case, the clusters are formed at the time of network deployment. The attributes of each cluster, such as the size of a cluster, the CH, the number of participating sensors and the area it covers, are static. Static clusters are easy to deploy, but their use is only appropriate for limited scenarios where the sensor field is predetermined, the targets to monitor are not in motion and it is easy to perform maintenance tasks (_i.e._, sensors replacements) in the network. On the other hand, Dynamic cluster architectures make a better use of the sensors. Sensors do not statically belong to a cluster and may support different clusters at different times.
# Lateral Loading of a Rock-Socketed Pile Using the Strain Wedge Model Based on Hoek-Brown Criterion
## 4 Analysis the Lateral Bearing Capacity of Rock-Socketed Piles Based on Strain Wedge Model
### The Relationship between \(p\) and (\(\Delta\sigma_{h}\), \(\tau\))  
The horizontal stress change (\(\Delta\sigma_{h}\)) is constant across the width of the trapezoid BB'C'C (of face with \(\overline{BC}\) of the passive wedge) is shown in Figure 3. Moreover, the rock resistance per unit pile length is obtained from the balance condition of the force [21]. The ultimate load per unit length at \(x\) depth is:  
\[\overline{BC}=D+2(h-x)\tan\beta_{m}\tan\rho_{m} \tag{50}\]  
\[p=\Delta\sigma_{h}\overline{BC}S_{1}+2\tau DS_{2} \tag{51}\] \[=\Delta\sigma_{h}[D+2(h-x)\tan\beta_{m}\tan\rho_{m}]S_{1}+\frac {y}{2.5D/E_{m}+y/0.8\sigma_{ci}(RF)^{0.45}}DS_{2}\]  
where \(S_{1}\), \(S_{2}\) is factor of pile type, and \(S_{1}=0.75\), \(S_{2}=0.5\) for circular pile cross section; and \(S_{1}=S_{2}=1.0\) for a square pile.  
Alternatively, one can write the above equation as follows:  
Figure 10: Schematic diagram of bore wall roughness of rock-socketed pile.  
\[A=\frac{p/D}{\Delta\sigma_{h}}=\frac{\overline{BCS}_{1}}{D}+\frac{2\tau S_{2}}{ \Delta\sigma_{h}}=\left(1+\frac{2(h-x)\tan\beta_{m}\tan\rho_{m}}{D}\right)S_{1}+ \frac{y}{\frac{25D/E_{m}+y/0.8q_{h}(RF)^{0.45}}{\Delta\sigma_{h}}}S_{2} \tag{52}\]  
where \(A\) is ratio between the equivalent pile face stress and the lateral stress change.  
Therefore:  
\[p=AD\Delta\sigma_{h} \tag{53}\]  
Referring to the normalized pile deflection shape shown in Figure 4 and Equation (34):  
\[\delta=\frac{(1+v)\varepsilon}{2}\sin 2\Theta_{m} \tag{54}\]  
It can be obtained that the relationship between the linear deflection angle and the lateral deformation of the rock:  
\[\Psi=\frac{\varepsilon}{\delta}=\frac{2}{(1+v)\sin 2\Theta_{m}} \tag{55}\]  
Based on the above analysis, the final expression of the subgrade reaction modulus can be obtained:  
\[E_{s}(x)=\frac{p}{y}=\frac{AD\Delta\sigma_{h}}{\delta(h-x)}=\frac{ADE_{i} \varepsilon}{\delta(h-x)}=\frac{A\Psi}{(h-x)}DE_{i} \tag{56}\]  
The maximum depth \(h\) of the strain wedge corresponds to the global force balance depth of the pile-rock system.  
Equation (28) can be solved by numerical methods by substituting Equation (56) into Equation (28).
## 2 Materials and Methods
### Estimating Anchor Boxes from Training Data  
The YOLOv2 detector uses an idea called an anchor box to detect multiple objects with different shapes within the same image. Anchor boxes are a set of predefined bounding boxes of a certain height and width to capture the scale and aspect ratio of specific object classes to be detected and are typically chosen based on object sizes in the training dataset. For the efficient and precise performance of the detector, estimating the appropriate number of anchor boxes, including their shape and scale, is necessary. The k-means clustering algorithm, with a mean Intersection over Union (_IoU_) distance metric, was used to estimate the number of anchor boxes. The k-means clustering algorithm groups the similar ground truth boxes from the labeled training dataset. _IoU_ measures the similarity between anchor boxes and ground truth boxes, and guarantees that it overlaps each other. The mean _IoU_ distance metric \(d\) was calculated using the following formula:  
\[d=1\ -\ IoU \tag{4}\]  
where _IoU_ is the ratio between the intersection between anchor boxes and ground truth boxes to the area of union between anchor boxes and ground truth boxes.  
Based on the clustering results analysis, the number of anchors was set to 20, corresponding to a mean _IoU_ = 0.8424 as a trade-off between the number of anchors and the mean _IoU_ (Figure 3). The clustered anchor boxes for transverse cracks are characterized in that the width is noticeably smaller than the height. The height then corresponds to the different lengths of the transverse crack.  
Figure 2: The architecture of the used detector based on YOLOv2 with ResNet-50 layers.
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 3 Results and Discussion
### Chemical Hydrolysis of Undisrupted Cells
They also noted that alkaline pretreatment (5 M NaOH, 90 \({}^{\circ}\)C for 30 min) of the biomass had a sugar release yield of 14 mg g\({}^{-1}\) DW, which was lower than with acidic hydrolysis. Furthermore, [PERSON] et al. [6] demonstrated that acidic hydrolysis improved the efficacy of subsequent enzymatic hydrolysis with cellulases significantly, with an increase of sugar release from 15 mg g\({}^{-1}\) DW to 129 mg g\({}^{-1}\) DW. In the current study, even though this effect was confirmed, the influence of acidic hydrolysis  
Figure 7: Chemical hydrolysis of undisrupted _M. salina_ cells. Enzyme dosing is given as a mass ratio of CDW and adjusted to 1% protease (P), 5.9% cellulase (C1), and 0.12% mannanase (M) as indicated in the figure. Since changes in reaction volume due to pH adjustments throughout the process were not negligible, sugar concentrations normalized to the initial reaction volume are presented in addition to the measured values. (**a**) Actual and normalized final concentrations of glucose (\(\blacksquare\),\(\blacksquare\)) and mannose (\(\blacksquare\),\(\blacksquare\)); (**b**) saccharification efficiency (\(\eta_{\text{Sac}}\)). Error bars indicate the standard deviation of triplicate experiments. prior to enzymatic hydrolysis was less remarkable with an increase of sugar release from 12-17 mg g\({}^{-1}\) DW to merely 57-68 mg g\({}^{-1}\) DW. This difference might have originated from using _M. salina_ cells grown in a nutrient-replete medium since these have a different macromolecular and cell wall composition [12], which could have resulted in the increased structural stability of the cell wall and made it harder to disrupt or hydrolyze. Although acidic hydrolysis of _M. salina_ biomass improved the saccharification yield, it should be noted that it also resulted in a substantial dilution of the biomass due to the addition of acid and base to adjust the pH of the reaction medium. More precisely, dilution of the biomass by a factor of 1.3-1.6 resulted in a decrease of final glucose concentration from 6.8-8.1 g L\({}^{-1}\) to 4.1-5.1 g L\({}^{-1}\). Moreover, the excessive addition of acid and base resulted in an increase in the salt content of the hydrolysate, which may have negatively affected microbial growth when it was used as a feedstock for fermentation. Hence, the acidic hydrolysis approach was avoided in further hydrolysis experiments in this work.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 3 High-Speed Train Noise Research Methods  
At present, the noise of high-speed trains in motion is mainly studied by line tests, wind tunnel tests, and numerical simulations.
## 5 Conclusions  
In this paper, a two-stage structural damage detection method based on 1D-CNN and SVM is proposed. It is still challenging to detect the damage of a structure accurately based on the vibration response of the structure. To solve the problem that traditional machine learning methods need to design feature extraction methods by manually combining expert knowledge, this paper uses 1D-CNN to automatically extract rich features from vibration responses. To solve the problem that the number of samples with different severity of damage at the same damage location is small and 1D-CNN cannot distinguish these samples well, this paper uses SVM combined with wavelet packet decomposition to achieve the accurate identification of these samples. Experiments were conducted on an eight-layer steel frame, and damage responses were collected for ten damage cases. After preprocessing operations such as offset elimination, normalization, and slicing, the damage locations corresponding to the samples were first determined by 1D-CNN, and then the damage severity corresponding to the samples was determined by SVM. In the comparison experiments with other methods, the method proposed in this paper achieved the best results, taking into account the operation speed and recognition effect.  
However, the method in this paper still has some limitations, as it can only determine predefined damage cases and cannot determine continuous damage locations or damage severity. In future research, we intend to use deep learning-based regression methods to predict continuous damage and combine expert knowledge to improve detection performance. In addition, the data are provided by other labs, and we do not have permission to share the data. The code is released at [[https://github.com/jch12138/two-stage-structure-damage-detection](https://github.com/jch12138/two-stage-structure-damage-detection)]([https://github.com/jch12138/two-stage-structure-damage-detection](https://github.com/jch12138/two-stage-structure-damage-detection)) (accessed on 30 August 2022).  
Data curation, J.L. and X.W.; methodology, C.J. and Q.Z.; software, C.J.; supervision, Q.Z. and J.L.; visualization, X.W.; writing--original draft, C.J.; writing--review & editing, Q.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by China Natural Science Foundation under grant No. 62171391. [PERSON] and [PERSON] from Information and Network Center of Xiamen University are acknowledged for the help with the GPU computing.