esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Following the lipid film hydration method applied in this study, we can conclude that liposomes with azelaic acid of different concentrations (AALipo15%, AALipo20%, AALipo25%) were successfully formulated, highlighting the characteristic spherical shape and encapsulation efficiency between 77.3-85.73%, framed with a size of up to 500 nm and good stability, as demonstrated by FTIR spectroscopy, DLS, and zeta-potential measurements.  
The antimicrobial activity of AA was tested in comparison with the corresponding liposomal formulation in the same concentrations, against Gram-positive and Gram-negative bacteria, demonstrating that the liposomal formulation confers a superior antibacterial effect regardless of concentration, compared to free AA.  
The non-toxic effect of the new liposomal formulation was demonstrated by an MTS assay, while the in vitro wound healing effect was assessed by the \"scratch assay\" performed on human dermal fibroblasts. Moreover, by using a comet assay, the protective effect of the new liposomal formulation was demonstrated against H\({}_{2}\)O\({}_{2}\)- induced cellular DNA damage. After corroborating the results, we can state that the optimal liposomal formulation in terms of antimicrobial effect, wound, and protective effect is AALipo20%.  
Along with their safety considerations, these findings represent a good premise in order to develop further topical formulations as a dermato-cosmetic product with superior therapeutic potential, designed for the advanced treatment of complex skin disorders.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122413039/s1](https://www.mdpi.com/article/10.3390/app122413039/s1)]([https://www.mdpi.com/article/10.3390/app122413039/s1](https://www.mdpi.com/article/10.3390/app122413039/s1)), Figure S1: Particle size distribution diagram: (a) blank liposomes; (b) liposomes loaded with 15% AA; (c) liposomes loaded with 20% AA; (d) liposomes loaded with 25% AA.  
Conceptualization, P.M.P., S.C. and S.I.V.; methodology, F.M., L.F., F.B., V.L., D.C.Z. and A.A.; software, L.F. and F.B.; investigation, F.M., D.C.Z. and A.A.; writing--original draft preparation, S.C., L.F., F.M. and P.M.P.; writing--review and editing, S.C. and S.I.V.; supervision, S.C.; project administration, S.C. and P.M.P.; funding acquisition, S.C. and F.M.. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The research was supported by the University of Oradea, within the Grants Competition \"Scientific Research of Excellence Related to Priority Areas with Capitalization through Technology Transfer INO-TRANSFER-UO\", Project No. 246/08.11.2022.  
The authors declare no conflict of interest.
## 2 System Model and Problem Formulation
### Network Model
This constraint is essential for safety in multi-UAV systems [35]. UAV Flight Altitude Constraint: Each UAV \(u\) should maintain its altitude within the specified range:  
\[H_{min}\leq Z_{u}(t)\leq H_{max},\quad\forall u\in\mathcal{U},\forall t\in \mathcal{T}, \tag{2}\]  
These altitude limits are practical considerations for UAV deployment [36]. As stated in [24; 37], for a UAV with speed \(V\) at time slot \(t\), the propulsion energy consumption within that time slot can be calculated as follows:  
Figure 1: Application scenarios for UAV-assisted telematics. \[P(V)= P_{0}\bigg{(}1+\frac{3V^{2}}{U_{r}^{2}}\bigg{)}+P_{1}\left(\left(1+\frac{V^{4} }{4v_{r}^{4}}\right)^{\frac{1}{2}}-\frac{V^{2}}{2v_{r}^{2}}\right)\right)^{\frac {1}{2}}+\frac{1}{2}d_{0}\rho sAV^{3}, \tag{3}\]  
where \(P_{0}\) represents the rotor blade profile power of a UAV in a hovering state, \(P_{1}\) is the rotor blade induced power in hovering, \(U_{r}\) denotes the tip speed of the blades, \(v_{r}\) signifies the average rotor induced velocity during hover, \(d_{0}\) is the drag ratio experienced by the UAV, \(\rho\) represents air density, \(s\) denotes the rotor disc solidity, and \(A\) represents the blade disc area. The power consumption for the hovering state is \(P_{h}=P(V=0)=P_{0}+P_{1}\). Therefore, the UAV's propulsion consumption at the time slot \(t\) is obtained by  
\[E_{u}^{\rm fly}(t)=P(V)\cdot\frac{\|\mathbf{l_{u}}(t)-\mathbf{l_{u}}(t-1)\|}{V }+P_{h}\cdot\max\bigg{(}0,\tau-\frac{\|\mathbf{l_{u}}(t)-\mathbf{l_{u}}(t-1)\| }{V}\bigg{)}. \tag{4}\]  
This energy model is commonly used in UAV trajectory optimization studies [38].
# Ensemble Modelling for Predicting Fish Mortality +
## 4 Materials and Methods
### Artificial Neural Networks  
Let us firstly discuss the architecture and parameters of the developed ANNs. All the ANNs considered have identical structures, so we shall focus on a single ANN. Accordingly, the alluded ANN is a conventional Multilayer Perceptron (MLP), which consists of four layers, namely, one input layer, two fully connected (dense) hidden layers, and one output layer (cf. Figure 3). The input layer of the network is a passive layer with no learnable parameters, which merely receives the data of each sample of the dataset. Each fully connected hidden layer has 130 neurons (units) and employs Rectified Linear Unit (ReLU) activation functions. The output layer contains a single linear neuron, which produces a real number that represents the estimation of fish deaths due to a particular infectious disease. The ANN is compiled using a Mean Squared Error (MSE) loss function and the well-known Adam optimization algorithm, which is an extension of the stochastic gradient descent [25] (It is noted that the ANNs developed in [11] are also compiled using a Mean Squared Error loss function, rather than using a Mean Absolute Error, as stated in the text of [11], due to a typographic error). For tackling the overfitting problem, the dropout regularization technique was used where necessary. By implementing dropout, the selected neurons of the ANN are randomly ignored (\"dropped out\") during the training phase, and, as a consequence, no weight updates are applied to those ignored neurons [26].
## 4 Discussion
Nevertheless, because elderly patients are unfamiliar with this type of control, there were cases where the charging port was not properly inserted when charging was necessary, causing the use of the SAR to stop. Additionally, after accidentally lowering the volume to the lowest level, they complained that 'the care robot doesn't say anything'. Therefore, it is necessary to design the SAR in a user-friendly manner to lower psychological barriers and increase user acceptance. Our findings corroborate those of [PERSON] et al. (2020), who implemented a program where nurses made four home visits to COPD patients, offering education on fine dust and particulate matter (PM) exposure, along with self-care guidance and instruction on using a mobile application to regulate window opening times. Consequently, patients became more aware of PM risks and endeavored to minimize their exposure [58]. Studies have demonstrated the efficacy of providing self-management education to COPD patients, leading to improvements in their quality of life [59], decreased hospitalizations for all causes, and reduced medication usage [27]. Notably, the integration of SAR-sensor technology in our study partly supplanted human intervention. SARs offer certain advantages, such as continuous monitoring, prompt responses in emergencies, and the ability to deliver persistent verbal instructions. They can potentially facilitate various caregiving services by establishing connections among SARs, sensors, caregivers, hospitals, emergency services, and more. However, further research is warranted to explore the role of SARs in nursing and service delivery. SARs can be categorized into therapeutic, companion, entertainment, and telepresence robots based on their functions [60]. Previous research has indicated that certain individuals find intimacy through physical interaction with SARs, such as petting and cuddling [61], and are willing to use them to fulfill specific social needs [62]. Our study revealed that the effectiveness of information delivery may hinge on participants' acceptance of SARs. Hence, researchers must account for individual preferences, needs, and technical proficiency when designing SAR services. This pilot study has several limitations as a preliminary investigation of SARs integrating an environmental sensor. First, the small sample size limits the generalizability of our findings. In this study, we were unable to ascertain how older COPD patients changed their behavior after receiving alarms from the SAR. Consequently, when interpreting the results of this study, we cannot definitively conclude that SAR alarms directly cause improvements in air quality. Therefore, further investigation is warranted to examine how patients respond immediately to the SAR's air quality alarms. Future studies should implement a robust design that includes multi-site data collection, a longer study duration, and the incorporation of control variables to account for external factors.
## 3 Results and Analysis
### Mutual Influence between Adjacent Roadways
#### 3.3.1 Solution of Stress Function for a Plane Containing Two Openings
The process of determining point \(\gamma_{1}\) corresponding to point \(\sigma_{2}\) in the expression is as follows: firstly, according to the mapping function, point \(t_{2}\) on the boundary of Opening 2 in the \(x_{2}O_{2}y_{2}\) coordinate system is determined, namely \(t_{2}=w_{2}(\sigma_{2})\); then, the corresponding point \(\gamma_{1}\) of point \(t_{2}\) in the \(x_{1}O_{1}y_{1}\) coordinate system is calculated by \(\eta_{1}=t_{2}+c\); finally, mapped point \(\gamma_{1}\) corresponding to point \(\eta_{1}\) is obtained using the inverse mapping function \(\gamma_{1}=w_{1}^{-1}(\eta_{1})\). As seen from the above, by transforming the coordinate points on different planes and in different coordinate systems, the desired surplus force can be calculated, thereby eliminating the need for further transformation of the corresponding stress analytical functions. However, solving the inverse mapping function can only yield an approximate solution, and for a plane containing one opening of an irregular and complex shape, its accuracy might be relatively low, thereby significantly reducing the accuracy of the stress function solution. In light of this, in this study, the technique of low-dimensional variable optimization, which was introduced by [PERSON] et al. [19], was utilized to replace the inverse mapping function for solving the coordinates of point \(\gamma_{1}\) corresponding to \(\sigma_{2}\) and the coordinates of point \(\gamma_{2}\) corresponding to \(\sigma_{1}\), eventually obtaining the expression of the corresponding surplus force. After determining the expression for \(f^{1}_{12}(\sigma_{2})\), in the first iteration calculation, the stress boundary condition for the unit circle corresponding to Opening 2 can be represented as follows:  
\[\varphi_{2}^{1}(\sigma_{2})+\frac{\omega_{2}(\sigma_{2})}{\overline{\omega_{2 }^{\prime}}(\sigma_{2})}\overline{\varphi_{2}^{1\prime}}(\sigma_{2})+ \overline{\psi_{2}^{1}}(\sigma_{2})=-f^{1}_{12}(\sigma_{2}) \tag{18}\]  
After the first iteration, the boundary of Opening 2 already satisfies the no-surface-force-stress boundary condition, but at this point, a new surplus surface force \(f^{1}_{12}(\sigma_{2})\) is be generated on the boundary of Opening 1. Therefore, it is necessary to continue to apply new counter forces on the boundary of Opening 1 for balance, the calculation process is consistent with the previous one. In the following iteration process, it is only required to update the corresponding stress analytical functions based on the solution result from the previous step.
## 2 Technology Forecasting Methodology
### Technology Forecasting Using TFDEA  
A fundamental concept of TFDEA is estimating technological frontier using DEA and measuring a change of frontiers as new DMUs appear even though there is a slight variation according to an objective of forecasting. Rate of Change (ROC) is measured by observing a DMU that existed in the frontier when it appeared but moved inside as new DMUs are presented and forecasting a future technology or product using ROC. In TFDEA, the frontier is state-of-the-art (SOA) which means the most efficient status that can be achieved at that time, and DMUs that exist on a frontier are regarded as SOA. Thus, ROC is measured with a change of efficiency estimates of DMUs which were SOA when they first appeared but became non-SOA as time goes by. This method has excellence in measuring technological ROC because it uses only SOA, which means widespread and cutting-edge technology compared with all other observations. The process of TFDEA is composed of two phases. In phase 1, it estimates the technological frontier using DEA and measures an ROC of the frontier. Additionally, it then forecasts a technology or product in the future using ROC in phase 2.
## 4 Optimization Problem Raising and Solving  
Based on (6), (14), (30), and (36) mentioned above, it can be seen that once the distance \(d\) is fixed, the capacity lower bound of UWOC is a unary function of the tilting angle \(\theta\). Changing \(\theta\) might obtain the maximum value of the lower bound of UWOC at some given distance, so the above question turns into one mathematical optimization problem. For this purpose, the optimization problem of capacity lower bound is first raised in this section, then the optimization issue is further proved to be a simple convex optimization one, and finally the theoretical solution to the optimal tilting angle is deduced.
# Can Livestock Farming Benefit from Industry 4.0 Technology? Evidence from Recent Study
## 4 Results
### Livestock Farming vs. Industrial Production
#### 4.1.4 Living Individuals: Animals and Humans on the Farm  
Living animals present the most obvious and relevant difference between industrial production and livestock farming. They are vertebrates with eyes, voice, senses, and perception, with brains and emotions. Every human working in livestock farming feels an emotional relation to the animals, and the animals are protected by law and culture from mistreatment and negligence. Not only the working people on the farm but also the public society and the consumer expects and requires that animals are treated with respect to their biological requirements and with a certain level of animal welfare.  
Domestic animals not only interact with the humans in their environment but also with the technical infrastructure and machinery [16]. The total system is characterized by a complex interaction of humans, animals, technical facilities, and the environment. Biological rhythms and processes are the driver and impulse generator for part of the activities and tasks. Each individual grows differently, has its own need of feed, water, climate, movement, sexual behavior, and so on. Each individual may be different in susceptibility to stress and diseases. Autonomous machinery must be able to react to the needs and behavior of the animal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **EU-28** & **Germany** \\ \hline Total number of farm holdings & 10.3 million (a) & 263.500 (b) \\ Number of holdings keeping livestock & 5.7 million (a) & 167.900 (b) \\ Utilized agricultural area (UAA) & 156.7 million ha (a) & 16.6 million ha (b) \\ Average farm size in hectares & 15.2 ha & 63.0 ha \\ Poultry population in livestock units & 20.8 million LSU (a) & 2.2 million LSU (a) \\ Pig population in livestock units & 33.0 million LSU (a) & 6.5 million LSU (a) \\ Cattle population in livestock units & 64.3 million LSU (a) & 9.0 million LSU (a) \\ Livestock density index & 0.8 LSU/ha (a) & 1.1 LSU/ha (a) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key figures of agricultural structure and livestock farming in the EU and in Germany. (LSU: Livestock units).  
Industry 4.0 benefits are driven by industrial use cases that allow the production of individual products and services as requested by each client. In biology, the term \"individual\" has two different meanings: \"(1) in general: the individual living being in its specificity; above all, the individual being in its uniqueness in contrast to the mass (mass society). Individuality means the differentiation of the individual from the set of individuals.... (2) a spatially and temporally limited structure of certain shape, organization and possibilities for change.\" [35]. When applying the benefits \"Digital individualization\" and \"Demand orientation\" to livestock farming, these benefits must regard not only the client demand but also the challenge to cope with the specific identity and demand of each individual animal, or at least each group or herd of animals.
## 5 Conclusions  
In this research, externally prestressed CFRP tendons were utilized to strengthen six simply supported beams of 3.5 m length and two two-span continuous beams of 7 m length, aiming to evaluate the efficacy of prestressed carbon fiber reinforcements in augmenting the bending and shear resistance capabilities of concrete simply supported and continuous beams. The formula for calculating the beam's bending load-carrying capacity was refined based on model test outcomes. Conclusions drawn from the experimental results and discussions are as follows:  
1. Through the reinforcement of externally prestressed CFRP tendons, the bending load-bearing capacity of the test beams was significantly enhanced, the bending rigidity demonstrates an increase positively related to the external prestressing level. When compared with the contrast beam, the simply supported beams reinforced with CFRP tendons using bend and linear layouts saw increases in bending load-bearing capacity of approximately 160% and 170%, respectively; whereas, for the continuous beams strengthened with curved layout reinforcement, the bending load-bearing capacity was increased by approximately 113%. Under identical reinforcement conditions, the increase in bending and shear capacities of the simply supported beams with CFRP tendon reinforcement was greater than that of the continuous beams. 2. By comparing the strain of stirrups in the reinforced beams, it is observed that increasing the prestressing level of the CFRP tendons effectively enhances the shear capacity of the concrete beams. Notably, the contribution of bend-arranged reinforcement to the shear performance of the beam exceeds that of linear-arranged reinforcement. Under identical reinforcement parameters, the increase in shear capacity observed in simply supported beams outperforms that in continuous beams. 3. The prestress level of CFRP tendons and the pre-existing cracks have minimal influence on the ultimate bending capacity of concrete beams, but they noticeably influence the cracking moment and yielding moment of the test beams. The yielding load is positively correlated with the external prestress level, whereas pre-existing cracks lead to a decrease in carrying capacity in the reinforced beams. 4. Based on existing theories, modifications have been made to the bending load-bearing capacity formula. The correction formula takes into account the influence of cracking conditions and the form of prestressing tendon arrangement. The discrepancy between theoretical calculations and measured values is within 10%, proving its validity for beams externally prestressed using CFRP tendons. The above conclusions will be confirmed by additional tests and finite element simulations to improve the reliability of the test results and to obtain design values with adequate safety factors that take into account a wider range of variability. However, further experimental work and theoretical studies are necessary to develop more rational and accurate
## 4 Experimental Results
### UAV Application in Transportation Analysis  
To demonstrate the practical usability of the model in real-world UAV applications, experiments are conducted using the Traffic-Net dataset. By applying the model to Traffic-Net, its effectiveness in traffic condition monitoring is validated, which is crucial for UAV-based traffic management systems. To align with the traffic condition classification tasks, the softmax loss function from the model is retained to ensure consistency in the optimization process, and the model is retrained. As shown in Figure 12, the proposed model effectively recognized and classified different traffic conditions with an average latency of 12.53 ms, including dense traffic, sparse traffic, fire incidents, and accidents. The high prediction accuracy across these varied scenarios underscores the model's robustness and its potential for deployment in UAV systems tasked with real-time traffic monitoring. These results demonstrate that the proposed model is not only capable of operating efficiently in diverse environments but also adept at handling the real-world challenges that UAVs encounter  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Inference Time** & **mAP(\%)** & **EPF (J)** & **CPFPS(RMB)** & **CPI** \\ \hline yolov8s & 140.72 ms & 17.3\% & 0.4925 & 42.06 & 6.20 \\ yolo-fastestv2 & 19.86 ms & 10.1\% & 0.0695 & 5.94 & 21.49 \\ FastestDet & 20.47 ms & 10.3\% & 0.0716 & 6.12 & 20.95 \\
**Proposed model (320 \(\times\) 320)** & **12.13 ms** & **10.8\%** & **0.0425** & **3.63** & **34.02** \\
**Proposed model (352 \(\times\) 352)** & **15.27 ms** & **11.5\%** & **0.0534** & **4.57** & **27.61** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comprehensive performance indicators (CPI) for different modelswhen managing traffic conditions. This experiment confirms that the proposed model can effectively support UAVs in performing crucial tasks such as traffic surveillance, incident detection, and emergency response, making it a valuable tool for enhancing urban mobility and safety.
## 3 Results  
### Model Validation  
We compared the modeled results with observations at the study site over seven years (2006 to 2012); these observations included GPP, NEE, and soil temperature at a depth of 15 cm (Figure 2). The predicted variables through numerical estimations satisfactorily captured their seasonal variations. The corresponding root mean square errors (RMSE) were 1.6 g C m\({}^{-2}\) d\({}^{-1}\), 1.4 g C m\({}^{-2}\) d\({}^{-1}\), and 3.0 \({}^{\circ}\)C for GPP, NEE and soil temperature, respectively. In particular, our results showed excellent agreement between modeled and observed seasonal trends in soil temperature (Figure 2e,f). However, there were some discrepancies between modeled and observed GPP and NPP, especially during high flux events. These differences were in part caused by weather forcing data that were not obtained from the study, as explained above. However, the seasonal variations and magnitudes of GPP, NEE, and soil temperature were comparable with the observations, providing confidence in modeled results for exploring the growth of _Pinus densiflora_ under climate change.  
Figure 2: Model validation: (**a**) predicted (gray lines) and observed (circles) GPP (RMSE = 1.6 g C m\({}^{-2}\) d\({}^{-1}\)), (**b**) NEE (RMSE = 1.4 g C m\({}^{-2}\) d\({}^{-1}\)), and (**c**) soil temperature to a depth of 0.15 m (RMSE = 3\({}^{\circ}\)C) during the model validation period from 2006 to 2012. The corresponding results were presented in (**d–f**), respectively. The gray dashed line in (**d–f**) represents the 1:1 relationship.