esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Samples Study
#### 2.3.3 Wettability and Surface Free Energy  
The contact angle measurements were made using the DigiDDrop Contact Angle Meter (GBX, Romans-sur-Isere, France) equipped with a closed and thermostated chamber. The Milli-Q ultrapure water (\(\gamma_{1}\cong\) 72.8 mN/m at 20 \({}^{\circ}\)C) was used as the probe liquid. The sessile droplet technique was used as follows: a 6 \(\mu\)L droplet was settled on the examined surface and the advancing contact angle was measured, then 3 \(\mu\)L of liquid was sucked and the receding contact angle was measured. The contact angle values were calculated by the WinDrop++ software using the polynomial algorithm based on the droplet shape using the contour mode. Ten water droplets were measured and averaged along each surface in order to increase the measurement accuracy.  
The surface free energy (SFE) of each sample was estimated using the contact angle hysteresis (CAH) approach proposed by [PERSON] [48]:  
\[\gamma_{\text{s}}=\gamma_{1}\frac{\left(1+\cos\theta_{\text{a}}\right)^{2}}{ \left(1+\cos\theta_{\text{r}}\right)^{2}-\left(1+\cos\theta_{\text{a}}\right) ^{2}} \tag{1}\]  
where: \(\gamma_{\text{s}}\)--the surface free energy, \(\gamma_{1}\)--the surface tension of the probe liquid, \(\theta_{\text{a}}\)--the advancing contact angle, \(\theta_{\text{r}}\)--the receding contact angle. This allows us to determine the SFE as the function of three directly measurable parameters: liquid surface tension and both advancing and receding contact angles.
## 4 SRPC Control Algorithm
### DC-Bus Voltage Balancing and Tracking of the Reference Signals  
Hereafter, the symbol \(\sigma\) refers to both catenary sections of \(x\) and \(y\). The symbols \(l\), \(u\) denote the lower and the upper MMC arms, respectively. Two PI controllers are responsible to achieve the voltage balancing control for the MMC main DC-link voltage as shown in the control strategy of Figure 6a. The first PI controller is used to compare the actual value of the DC-link voltage with its reference value \(V^{*}_{dc}\). The output of this PI controller is multiplied by two synchronizing signals to obtain the dc-link voltage regulation signals of section \(x\) and section \(y\) converters. The second PI controller balances the voltages between the two DC-link capacitors. The output signal of this controller is added to the compensation current references, \(i_{\pi}\)\({}^{*}\), and \(i_{\eta}\)\({}^{*}\). Equations (23)-(26) are implemented in the digital controller to implement the deadbeat predictive current control methodology for circulating currents reduction in the MMC (the source code to implement the Equations (23)-(26) in the controller is shown in the Appendix A). This leads to generate the upper and the lower voltage reference signals for each MMC arm.  
Figure 5: Diagram for establishing the references of compensation currents. E-PLL: definition.  
On the other hand, and since one of the contributions of this paper is to establish a comparison between the deadbeat predictive current control methodology and the conventional PI controller, Figure 6b shows the DC-link voltage control and the calculation of the voltage reference signals when using the conventional PI controller. It is noteworthy to mention that conventional stationary frame PI controllers are not practically favorable to serve this application, since they cannot reach the zero steady-state error, but they are good enough for the demonstration.
## 3 Results and Discussion
### Optimization of Piercing Strategy  
Based on the parameter dependencies observed during the monitoring of the piercing process, it was possible to optimize the strategy used in soft piercings. For mild steel 20 mm thick, firstly, a piercing programmed in three individual steps was used (Piercing A), each one with its own values of time, focal position, standoff, duties, and ramp time. The total piercing duration was 11-11.5 s. However, thanks to the information released from the monitoring, it was possible to select a single piercing (Piercing B) with continuous movement between two standoffs and with two combinations of duties and ramp time. This optimization was carried out through a design of experiments in which each process parameter was fixed and its monitoring signal analyzed in order to adjust each parameter for a better response in relation to time. Piercing A was based on the modification of the focal position maintaining the standoff. However, the know-how obtained in this study revealed that the piercing was affected by pressure, duties, and ramp time modifications. Thus, Piercing B was proposed taking into account these results. Pressure stabilization in laser cutting is not an immediate process, so this parameter was not directly modified. Piercing B duration showed a decrease when the focal position was fixed and the standoff was varied. This movement implied the approach of the cutting head to the sheet, improving the arrival of the gas to the piercing zone, and contributing to evacuate the material. Regarding duties and ramp time, the maximum values of duty minimum and duty maximum were selected without provoking explosion, and the ramp time was also minimized. Figure 6 shows that the final piercing duration was decreased to 8-8.6 s, obtaining a reduction in time of 25%. Table 3 summarizes piercing parameters values for each strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Thickness** & **Gas** & \begin{tabular}{c} **Type of** \\ **Piercing** \\ \end{tabular} & \begin{tabular}{c} **Spectrum** \\ **Monitored** \\ \end{tabular} & \multicolumn{1}{c}{**Process Parameters**} \\ \hline Mild Steel & 20 mm & Oxygen & Soft & Visible & \
\begin{tabular}{c} Dependence: Gas \\ pressure, Ramp time, \\ Duty Percentage \\ No dependence: Focal \\ position, Standoff \\ \end{tabular} \\ \hline Stainless Steel & 10 mm & Nitrogen & Fast & Infrared &
\begin{tabular}{c} No clear dependence \\ \end{tabular} \\ Aluminium & 10 mm & Oxygen & Fast & Visible & No clear dependence \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resume of monitored experiments and dependence of process parameters.  
Figure 5: Piercing monitored signal for aluminum 10 mm thick, varying (**a**) focal position; (**b**) duties percentage. Focal positions of 1.5 or 2.5 and duties percentages of 25 25 and 22 22 provoked explosions.
# Biohydrogel Based on Dynamic Covalent Bonds for Wound Healing Applications
## 3 Results and Discussion
### Formulation and Relevant Characterization of Hydrogels
#### 3.1.1 Interfacial Tension between Fluids and Hydrogel  
The interfacial tension between the soft hydrogels and the selected fluids (PBS and water) was relatively low, likely because the hydrogels were miscible with these fluids and could dissolve in them over time. The interfacial tension between the hydrogels and PBS and distilled water was 1.89 and 1.70 dynes/cm, respectively. The slight difference in the interfacial tension of the material with PBS and water is not significant (\(p>0.05\)), and may be attributed to the inhibitory common ion effect with PBS.  
The relatively low interfacial tension suggests that these hydrogels would interact effectively with biofluids when in contact with biological surfaces, allowing continuous engagement. This attribute would be valuable in the process of wound healing via fluidization [23] and biodegradation [24] of hydrogel material because it would increase the surface area available for activity and breakdown.
## 2 Importance of Thermal Condition Monitoring
### Protection and Control Components  
The uses of electrical power protection and control device are strongly dependent on the method of operation and the maintenance conditions applicable [38; 39]. The compositions of power devices include organic or inorganic insulation systems. The electric field intensity and partial discharges includes other electrical phenomena impact on its thermal performance. The temperature of protection components become an essential determining factor because it affects the properties of the electric system functioning. This poses significant aging factor when power actuating equipment operates beyond their threshold. Again, during the full and unstable load conditions, temperatures increase and cause material characteristics change due to stress on the components. Abnormal high sustained temperatures may also cause overheating of the protection control systems. Therefore, the performance becomes unreliable and causes a high degree of failures [39].  
The issue of heat dissipation becomes worse when there are no adequate ventilation slots in the protection enclosures or compartment. It is also necessary to consider that additional power losses are caused by the switchgear actuation and the auxiliary functions due to thermal effects. The impact affects the performance of relays and other controlling components. The losses are caused by the load conditions and overheating of protection control systems, thus impact extremely on the operations and lifespan of the protection control equipment [40]. The thermal condition monitoring focuses on the temperature measurement of the protection devices and the transformer insulation cooling system (coolant or oil) for real-time online measurement of thermal behaviors.
# GAO-FCNN-Enabled Beamforming of the RIS-Assisted Intelligent Communication System
## 3 Gao-Fnn
### Genetic Algorithm Optimization
Subsequently, the crossover step is provided in Figure 2. It is performed using a single-point crossover to generate offspring in GAO, following the crossover process of Algorithm 2:  
Figure 2.0 Figure 2.1  
After that, variation operations are performed as shown in Figure 2.2 The corresponding steps are demonstrated in Algorithm 2. Weight values are updated using gradients, and bias variants are adjusted accordingly. New values are applied to the model. Then, performance improvements are observed. \[\left\{ \begin{array}{l}\mathbf{W}^{(l)}\rightarrow\mathbf{W}^{(l)}+\varepsilon \Delta\mathbf{W}^{(l)}\\ e^{(l)}\to e^{(l)}+\varepsilon\Delta e^{(l)}\end{array} \right., \tag{26}\]  
where \(\Delta\mathbf{W}^{(l)}\) represents the updating of weight value and \(\Delta e^{(l)}\) represents the updating of bias. Next, the quantity update process is performed. And the new quantity is generated through selection, crossover, and variation operations,  
\[\mathbf{U}_{new}=\{\mathbf{u}_{new1},\mathbf{u}_{new2},\ldots,\mathbf{u}_{ new0},\ldots,\mathbf{u}_{new0}\}, \tag{27}\]  
Furthermore, the fitness is reevaluated and the optimal chromosome is updated as shown in Figure 2. If the system performance is not optimized or the termination condition is not fulfilled according to the saved chromosome, steps 3 to 6 will be repeated in Algorithm 2. The maximum number of iterations or generations in Algorithm 2 is defined as the population size \(O\) (i.e., \(\text{FESmax}=O\)), and \(z\) is the evolutionary generation count. So, \(O\) is the maximum rate parameter of \(z\). This is treated as a termination condition. Otherwise, the final output results of GAO can be obtained. This training process can be concluded when the allowed maximum number of iterations is achieved. And the neural network parameters corresponding to the individual with the best fitness are finally provided,  
\[\mathbf{u}^{*}=\operatorname*{argmax}_{\zeta_{\omega}}f(\mathbf{u}_{o}), \tag{28}\]  
where \(\mathbf{u}^{*}\) represents the optimal neural network parameter configuration of the joint beamforming issue in the RIS-assisted intelligent communication system. It is also the optimal result obtained by the GAO, then it can be used for the replacement in Algorithm 2. Finally, in order to satisfy the amplitude limitation of the nonactive beamforming matrix \(\mathbf{\Phi}^{(i+1)}\), i.e., \(\beta_{m}^{(i+1)}\in[0,1],m=1,2,\ldots,M\), the optimization of \(\mathbf{\Phi}^{(i+1)}\) at each iteration is normalized as outlined as follows:  
\[\beta_{m}^{(i+1)}=\beta_{m}^{(i+1)}/\beta_{\max}^{(i+1)}, \tag{29}\]  
where \(\beta_{\max}^{(i+1)}\) represents the highest value of the \((i+1)\)-th sample in \(\beta_{1}^{(i+1)}\), \(\beta_{2}^{(i+1)}\), \(\ldots\), \(\beta_{M}^{(i+1)}\).
## 1 Introduction
[39] studied the mechanical response of an existing tunnel and the stability of the underground excavation area when a subway station crossed an existing tunnel at a distance of zero. [PERSON] et al. [40] studied the influence of new tunnels with different excavation sequences and grouping reinforcement ranges on existing pipelines above. Using the finite element model, [PERSON] et al. [41] analyzed the ground deformation caused by a new tunnel undercrossing an existing tunnel, and obtained some parameters to control the deformation. [PERSON] and [PERSON] [42] studied the support pressure of the excavation face when a new tunnel crossed beneath an existing tunnel, and analyzed its influence on the surface settlement and segment deformation. Based on the model test method, [PERSON] et al. [43] studied the deformation characteristics and the variation law of soil pressure of an existing horseshoe tunnel and rectangular tunnel when a shield tunnel passed underneath. Through field monitoring, [PERSON] et al. [44] studied the settlement characteristics of an existing tunnel and the surface when twin tunnels were constructed beneath existing shield-driven twin tunnels. Through field monitoring, [PERSON] et al. [45] studied the settlement and stress of an existing tunnel when a twin tunnel was excavated beneath the existing tunnel twice. In summary, the research on new tunnels undercrossing existing structures mainly focuses on existing tunnels, existing foundation pits and existing buildings, and the research on new tunnels under existing rectangular pipe jacking needs to be supplemented. The safety of rectangular pipe-jacking tunnels cannot yet be accurately evaluated, resulting in a lack of data while taking corresponding control measures, seriously restricting the scale of construction of subway tunnels adjacent to existing tunnels excavated using the rectangular pipe-jacking method in urban areas. This important problem urgently needs to be solved. Based on the shield tunneling of Line 2 of Hohhot's urban rail transit system beneath existing rectangular pipe jacking, a set of protective measures has been devised. These measures encompass interlayer soil grouting and internal steel support reinforcement. This study involved the use of numerical simulations to analyze the settling behavior of the existing rectangular pipe jacking under various reinforcement scenarios, thus furnishing theoretical guidance for on-site construction. Subsequently, the effectiveness of the engineering implementation was assessed through on-site monitoring, with a comprehensive comparison between simulated and actual measurements serving as validation. The study revealed the deformation characteristics of the underground pedestrian passage excavated by the rectangular pipe-jacking method. This could improve the design theory of rectangular pipe-jacking tunnels and lay the foundation for controlling the safety of the existing rectangular pipe-jacking tunnels during the construction of new tunnels.
## 3 Hypothesis Development and Research Model
### User-Perceived Factors
#### 3.1.3. Aesthetic Quality  
Another critical aspect of AR use is the role of aesthetics [33], which considers perception, interpretation, visualization, and description of reality. Aesthetic quality can be defined as the visual appeal and attractiveness of the marketing environment [34]. Aesthetic quality is a prominent factor in any visualization technology integrated into the environment. Users of AR technology can manipulate aesthetic aspects such as design, vividness, color, and virtual reality elements [35]. These aesthetic features provide consumers of cultural heritage products with a high degree of control and pleasure, contributing to a pleasant shopping experience [36]. These aesthetic elements also enable the best flow experience [37]. In addition, the dynamic visual effects provided by AR technology can lead to a smooth user experience and leave a good impression on consumers [38]. Consumers with high cognitive innovation abilities are likely to pay more attention to the aestheticsbrought about by AR technology, which may further affect their sustainable relationship behavior with the technology and related products [6].  
Aesthetic perception can be significantly influenced by the flow of consumer experience through technology. According to [PERSON] et al. [39], individuals develop mental images of products after gaining a conceptual understanding or knowledge of cultural heritage through AR. [PERSON] and [PERSON] [40] further point out that when consumers appreciate the aesthetic quality representations of cultural heritage products, they are more likely to experience the flow state because aesthetic factors play a crucial role in immersing consumers in the AR usage experience. [PERSON] and [PERSON] [35] observed that the aesthetic entertainment and visual appeal provided by AR technology improve customers' efficiency in completing cultural heritage shopping activities, thus bringing the best shopping experience and generating a flow experience. Building on the existing literature, this study proposes that as AR technology increasingly permeates the design and delivery of cultural heritage products, the aesthetic quality of products demonstrated through AR could significantly impact consumers' flow experience.  
Aesthetic quality is essential in analyzing AR for cultural heritage product purchasing behavior and consumer research. This is because it directly affects the user's perception and experience, ranging from usability and ease of use to deep emotional engagement. Therefore, the following hypothesis is proposed:  
**Hypothesis 3a**.: _The aesthetic quality factor positively affects consumers' perceived usefulness in cultural heritage product purchases._  
**Hypothesis 3b**.: _The aesthetic quality factor positively affects consumers' perceived ease of use in cultural heritage product purchases._  
**Hypothesis 3c**.: _The aesthetic quality factor positively affects consumers' flow experience in cultural heritage product purchases._
# A Reliable Publish-Subscribe Mechanism for Internet of Things-Enabled Smart Greenhouses
## 4 Performance Evaluation
### Evaluation Model
 } & \multirow{3}{*}{94.88} & \multirow{3}{*}{474.4} & \multirow{3}{*}{35.4249} & \multirow{3}{*}{177.1245} & \multirow{3}{*}{128.73} & \multirow{3}{*}{643.65} & \multirow{3}{*}{46.3341} & \multirow{3}{*}{231.6705} \\  & & & & & & & & \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Message processing \\ rate (msgs/s) \\ \end{tabular}  } & \multirow{3}{*}{23.72} & \multirow{3}{*}{118.6} & \multirow{3}{*}{8.856225} & \multirow{3}{*}{44.281125} & \multirow{3}{*}{32.1825} & \multirow{3}{*}{160.9125} & \multirow{3}{*}{11.583} & \multirow{3}{*}{57.917} \\  & & & & & & & & \\ \hline \multirow{3}{*}{\ \begin{tabular}{c} Average latency \\ (in ms) \\ \end{tabular}  } & \multirow{3}{*}{0.74} & \multirow{3}{*}{0.74} & \multirow{3}{*}{0.96} & \multirow{3}{*}{0.96} & \multirow{3}{*}{1.38} & \multirow{3}{*}{1.38} & \multirow{3}{*}{0.87} & \multirow{3}{*}{0.87} \\  & & & & & & & & \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Subscription \\ throughput (msgs/s) \\ \end{tabular}  } & \multirow{3}{*}{128.216} & \multirow{3}{*}{641.081} & \multirow{3}{*}{36.900} & \multirow{3}{*}{184.504} & \multirow{3}{*}{93.282} & \multirow{3}{*}{466.413} & \multirow{3}{*}{53.257} & \multirow{3}{*}{266.287} \\ \cline{1-1} \cline{5-5}  & & & &Table 2 displays the number of message transaction processes in one second for both the original and proposed broker mechanisms, based on the number of messages and events that occur per second in the greenhouse, which, in this investigation, is based on the premise of a constant value of 4. It is assumed that one package containing four events (which occur in a single second) is transferred from the greenhouse environment to the broker every second. Based on the assessment tables supplied in this subsection, we estimate that our proposed approach, i.e., a novel categorization for the topics accessible in the Mosquito 1.4.15 broker, is compatible with the requirements of a smart greenhouse operated with IoT. Moreover, the subscription throughput in MQTT protocols refers to the ability of the MQTT broker to handle a large number of subscriptions or topic filters from multiple clients simultaneously, which is denoted by the abbreviation \(P^{*}\). In other words, when a subscriber subscribes to a topic, the subscriber expresses an interest in receiving messages that are published on that topic. The MQTT broker, which is responsible for receiving and delivering messages, needs to be able to handle the traffic of messages that are published on the topic and deliver them to all the subscribers who have subscribed to that topic. Therefore, subscription throughput (\(P^{*}\)) is a critical factor in MQTT because it determines how quickly messages can be delivered to subscribers [16; 28; 43; 44]. The subscription throughput in an MQTT broker can be calculated using the following Formula (6), where \(L^{*}\) is the number of messages subscribed per second, and \(T\) is the average latency of message delivery. \[P^{*}=L^{*}/T \tag{6}\]
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Efficacy of Fusion Strategy  
Both feature-level and score-level fusion strategies are effective, with greater benefits on the iLIDS-VID dataset. The conditional concatenation method, especially when combined with principal component analysis (PCA), improved performance on both datasets. Specifically, the PCA-enhanced method increased rank-1 accuracy by 0.32% on iLIDS-VID and 0.38% on MARS compared to simple concatenation. These results indicate that reducing dimensionality and aligning features significantly improve the integration of appearance and gait information.