esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Method  
### Geometric Models and Boundary Conditions  
Based on the model published in Ref. [3], as shown in Figure 2, the stator, rotor and end wall form an enclosed fluid chamber and all walls are hydrodynamically smooth. The model sizes are shown in Table 2.  
In this paper, ANSYS Fluent is used to calculate the steady flow in the gap. Density and dynamic viscosity of the mediums are from the NIST RefProp database to enhance the accuracy of the results. The boundary conditions of Section 4.1 are set according to Ref. [3]. Table 3 shows the boundary conditions of the numerical calculation. The end wall and stator wall are set as static non-slip walls and the rotor wall is set to rotate. It should be pointed out that since the skin-friction coefficient model contains two parameters: \(S/R_{0}\) and \(Re\), for determined \(S/R_{0}\), the flow in the gap will pass through different flow regions when the \(Re\) changes. The models of Region I and Region II are based on the laminar flow assumption, while the models of Region III and Region IV are based on the turbulent flow assumption. After \(S/R_{0}\) is given, the laminar and turbulent flow can be defined according to the four region models. Through analysis and calculation, it can be considered that the critical \(Re\) of laminar and turbulent flow is 4.6 \(\times\) 10\({}^{4}\) for any \(S/R_{0}\). The correspondence between the specific flow areas and the models are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters/mm** & **Geometry 1** & **Geometry 2** & **Geometry 3** & **Section 4.2** \\ \hline Relative gap \(S/R_{0}\) & 0.0127 & 0.0255 & 0.0636 & 0.005–0.065 \\ Axial length \(S\) & 3.18 & 6.35 & 15.85 & 1.25–16.20 \\ Outer radius \(R_{0}\) & & & 249.20 & \\ Inner radius \(R_{i}\) & & & 25.40 & \\ Gap width \(t\) & & & 6.35 & \\ Rim gap \(c\) & & & 1.60 & \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of geometric model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameters** & **Units** & **Water-Liquid** & **Air** & **Critical CO2** \\ \hline Temperature & K & & 298.15 & & 304.13 \\ Pressure & MPa & & 0.1 & & 7.38 \\ Density & kg/m\({}^{3}\) & 998.2 & 1.17 & 345.4 \\ Dynamic viscosity & Pa\(\cdot\)s & \(1.003\times 10^{-3}\) & \(1.85\times 10^{-5}\) & \(2.65\times 10^{-5}\) \\ \(Re\) & - & & \(2.0\times 10^{5}\)–1.0 \(\times 10^{8}\) & \\ \hline \end{tabular}
\end{table}
Table 3: Parameters for boundary conditions.  
Figure 2: Schematic view of the geometry.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 2 Related Work
### Channels and Spatial Attention Mechanisms  
The channel attention mechanism is predominantly utilized to process feature maps produced by CNNs. It concentrates on discerning the significance of individual channels within the input feature maps, thereby selectively focusing on the channel dimensions. The initial implementation of this mechanism is exemplified by the SENet model, introduced by [PERSON] et al. [38]. This model incorporates the squeeze-and-excitation (SE) module, which fundamentally computes the weights for each channel using global average pooling and fully connected layers. These weights are then leveraged to modulate the network's focus across various channels. To mitigate the computational burden imposed by the linear layers in the SE module, [PERSON] et al. [39] developed the efficient channel attention (ECA) module. This module utilizes 1D convolution with a specific kernel size, K, to efficiently derive channel attention from the feature map. In comparison to the SE block, the ECA module requires fewer parameters and its 1D convolution dynamically adjusts the channel weights. However, the channel attention mechanism predominantly centers on the relevance of individual channels, often overlooking spatial information. This oversight can lead to the omission of crucial structural details. Such limitations are effectively countered by the spatial attention mechanism. The pioneering application of spatial attention was the spatial Transformer network (STN), proposed by [PERSON] et al. [40] in 2015. This network allows the model to dynamically concentrate on varying regions of the input by learning spatial transformation parameters. To amalgamate both spatial and channel attention mechanisms, [PERSON] et al. [41] introduced the convolutional block attention module (CBAM). This module is a sophisticated attention mechanism that sequentially integrates channel and spatial attention, enhancing the model's ability to comprehend features more holistically and capture complex interdependencies among them.
This newly introduced neural network operates in an error-driven and online-optimized manner, reducing its dependence on precise system information. The recursive structure within the neural network enables the storage and utilization of historical information, thereby enhancing estimation accuracy and dynamic approximation. (2) STSMC merges the robustness and simplicity inherent in traditional sliding mode control, effectively mitigating the chattering phenomenon. To mitigate inaccuracies and complexities stemming from manual parameter adjustment, a sliding mode gain adaptive law was devised to attain an optimal array of gain solutions. (3) The designed controller algorithm was tested on the corresponding hardware experimental platform and had better harmonic compensation performance than the traditional ASMC and STSMC, verifying its feasibility under actual working conditions.
## 1 Introduction
[23] studied stabilization of subgrade soft soil with geopolymer made from POFA as a precursor and sodium hydroxide (NaOH) with sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)) as alkali activators to find the optimum Na\({}_{2}\)SiO\({}_{3}\)/NaOH ratio and POFA/soil ratio based on the compressive strength developed and the microstructural improvement of the soil. Moreover, [PERSON] and [PERSON] [24] evaluated the effectiveness of using POFA-based geopolymer to stabilize high plasticity silt to conclude that physical and chemical properties of soft soil were enhanced by POFA-based geopolymer. Despite these developments and research achievements, using geopolymer for soil stabilization applications is still at its early stages and requires more demonstration. Moreover, comprehensive studies about geopolymers produced from POFA as a precursor with NaOH and Na\({}_{2}\)SiO\({}_{3}\) as activators in soft soil stabilization are limited. The role of different clayey soils and alkaline solutions type in geopolymerization need to be well addressed as well. However, considering the method of geopolymer application to soil, previous research mixed POFA with the soil and then added the alkaline activators. Therefore, for a better understanding of the effect of POFA-based geopolymer on soil's properties, a well-designed geopolymer should be prepared and applied to the soil. This research focuses on the effectiveness of using POFA-based geopolymer in clayey soil stabilization. Due to that matter, the changes in the mechanical performance of two types of clayey soil before and after treatment with POFA-based geopolymer were evaluated based on unconfined compression (UCS) and direct shear tests (DST). Furthermore, the changes in the microstructural composition of both treated and untreated soils were determined with the aid of field emission electron microscopic (FESEM) with energy dispersive X-ray tests (EDX) to understand the underlying mechanisms of stabilization.
## 5 Empirical Model
### Mediating Effect Model  
The previous theoretical analysis shows that high-speed rail is able to enhance the degree of industrial coordination and agglomeration, which in turn makes a difference regarding the level of green technology innovation. Thereby, this article refers to the classic three-step model [74] to construct an intermediary effect model to test the intermediary effect of industrial collaborative agglomeration. The specific steps are as follows:  
Step 1: Assess the effect of high-speed rail on green patents (baseline model);  
\[\ln\text{GreenPatent}_{i,t}=\alpha_{0}+\alpha_{1}\ln\text{HSRFreq}1_{i,t}+ \alpha_{2}X_{i,t}+\mu_{i}+\gamma_{t}+\varepsilon_{i,t}\]  
Step 2: Test the influence of high-speed rail on industrial collaborative agglomeration;  
\[\text{COAGG}_{i,t}=\beta_{0}+\beta_{1}\ln\text{HSRFreq}1_{i,t}+\beta_{2}X_{i,t }+\mu_{i}+\gamma_{t}+\varepsilon_{i,t}\]  
Step 3: Introduce the Industrial collaborative agglomeration into baseline model.  
\[\ln\text{GreenPatent}_{i,t}=\gamma_{0}+\gamma_{1}\ln\text{HSRFreq}1_{i,t}+ \gamma_{2}\text{COAGG}_{i,t}+\gamma_{3}X_{i,t}+\mu_{i}+\gamma_{t}+\varepsilon_{ i,t}\]  
To test the mediation effect, it is first necessary to pass the baseline model regression test. Then, a further test is needed to prove that the influence of high-speed rail on the industrial collaborative agglomeration is significant. Finally, the collaborative agglomeration is introduced into the baseline model to form a new empirical model, and there is a need to observe the changes in regression coefficients. If the coefficients \(\alpha_{1}\), \(\beta_{1}\), \(\gamma_{1}\), and \(\gamma_{2}\) are all significant, and \(\alpha_{1}>\gamma_{1}\) or the significance of \(\gamma_{1}\) is lower than the significance of \(\alpha_{1}\), then it is believed that the mediation effect exists, which means that high-speed rail can influence the advance of green technology innovation by involving industrial collaborative aggregation. Otherwise, the transmission mechanism does not exist, and the analysis is ended.
## 3 Results and Discussion
### Electric Performance  
Figure 4 displays the voltage response of the MFC system, showing that the pseudo-stationary sate is reached after 50 h of operation. Typically, the breaking of the long organic chains and the formation of biofilm take place in the first hours of operation (24 h). Beyond this point, an increase in voltage output is observed and a further stabilization of the system is appreciated after 50 h. Figure 5 illustrates the results of the polarization tests performed after 72 h of operation. As can be seen, the maximum level of power density is 45 (mW/m\({}^{3}\)) at a current density of around 500 mA/m\({}^{3}\), corresponding to an  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Element**} & **70\% w/w [MTOA\({}^{+}\)][Cl\({}^{-}\)] + poly(vinylchloride) (PVC)** \\ \cline{2-3}  & **Fresh** & **After Operation** \\ \cline{2-3}  & **C. Norm (\% wt)** & **C. Norm (\% wt)** \\ \hline C & 36.05 & 22.16 \\ N & 13.76 & 4.91 \\ O & 9.27 & 19.97 \\ Cl & 40.91 & 37.63 \\ P & - & - \\ S & - & 5.88 \\ Ca & - & 0.59 \\ Fe & - & 0.63 \\ Zn & - & 5.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: EDX spectrum data of membranes before and after use in MFCs in contact with oil industry wastewater.  
Figure 3: Energy dispersive X-ray (EDX)spectra of the [MTOA\({}^{+}\)][Cl\({}^{-}\)] (70\(\%\)\(w/w\)) membrane, fresh and after operation in MFCs with white oil refinery wastewater.  
internal resistance of 0.98 k\(\Omega\). This level of power could be increased by mixing wastewater with other microbial promoting substrates [26; 27], although this proves that direct white oil refinery wastewater with natural occurring bacteria without previous treatment (direct use of wastewater effluent) can be used for energy production. Finally, as regards the evolution of the Coulombic efficiency for the MFC system, after operation, the Coulombic efficiency of the system reaches only 1.5%. This low level of Coulombic efficiency can be attained in MFCs fed with wastewater that are not strengthened by the addition of other microbial cultures specifically selected for electricity production.
## 5 MIP Applications for the Extraction of Antibiotics in Milk
### Penicillins
The MMIPs were then collected and washed with water (3 mL) and the analytes were eluted with 5% acetic acid in methanol (3 \(\times\) 1 mL) and sonication for 30 s. The eluate was evaporated at 40 \({}^{\circ}\)C, the dry residue was reconstituted with 1% formic acid in methanol (1 mL) and filtered through a 0.45 um membrane. In comparison with the respective NIP, MIP adsorption capacity for PEN V was higher, with Q\({}_{\text{max}}\) values of 70.71 and 139.88 umol/g. Furthermore, the proposed extraction protocol was effortless and less time consuming in comparison with a conventional SPE protocol, because it provides easier and faster sample loading, sorbent collection and washing and analyte elution [63]. A MSPD-HPLC-UV was developed for the determination of penicillin degradation products, penicillin acid and penilloic acid. Penicillin acid (0.5 mmol) was chosen as the template molecule,MAA (2 mmol) as the functional monomer, EGDMA (5 mmol) as the cross-linker, methanol-acetonitrile (1:1, \(v/v\); 20 mL) as the solvent, AIBN (16.4 mg) as the initiator and modified silica gel (20 mg) as the support. Polymerization was carried out at 60 \({}^{\circ}\)C for 24 h and the template was removed by Soxlet extraction. Penilloic acid-selective surface molecularly imprinted polymers (SMIPs) and spiked milk sample were mixed at a ratio of 3:2 into a glass mortar, blended with a glass pestle and the dry homogenous mixture was packed into an empty cartridge. The cartridge was washed with dichloromethane (2 mL) and the analytes were eluted with 10% acetic acid in methanol (3 mL). The eluate was evaporated under nitrogen stream at room temperature, the dry residue was reconstituted with mobile phase (3 mL) and the reconstituted eluate was filtered through a 0.45 \(\upmu\)m filter. In comparison with the respective NIP, MIP adsorption capacity for penicillin acid and penilloic acid was higher, with Q\({}_{\text{max}}\) values of 22,670 and 21,090 \(\upmu\)g/g respectively, while binding specificity study between penicillin acid, penilloic acid, CLOX, AMP and OXA showed SMIP selectivity over penicillin acid, penilloic acid and imprinting factors were 6.3 for penicillin acid and 3.5 for penilloic acid. Furthermore, the authors compared the developed MSPD protocol with three commercially available SPE cartridges, Cleanert PAX (500 mg, 6 mL), Cleanert PCX (500 mg, 6 mL) and Cleanert Florisil (500 mg, 6 mL) purchased from Bonna-Agela Technologies Inc. (Newark, USA), in terms of selectivity and extraction efficiency. The MSPD protocol displayed highest recoveries than the SPE protocols, with 88.5% recovery for penicillin acid and 85.9% for penilloic acid [64]. All the reported applications are summarized in Table 2.
## 2 Materials and Methods
### Spatial Resampling  
Once the spectral resampling was done and the results were obtained, another examination was added to this study. Using ENVI 5.2 (Exelis Visual Information Solutions, Boulder, CO, USA), the original Fenix image (1 m GSD) was spatially resampled to 5, 7.5, 15, and 30 m. The resampling method was a pixel aggregate, which averages all of the pixel values that contribute to the output pixel. The 7.5 and 30 m GSD were chosen, as they correspond to WV3 (commercial delivery) and the general in-orbit hyperspectral sensors, respectively. The 5 and 15 m samples were selected to observe a more gradual change between the different GSDs. Then, the models (i.e., a model for each sensor with its specific parameters) were applied to the different GSD images, so as to observe to what extent the model is still useful.  
In addition, another spatially resample process was executed for comparison (mentioned in Section 2.6) using the same technique mentioned. This process involved the thematic maps, which were based on the spectrally resampled original 1 m GSD image.
## 6 Experimental Results and Analysis
### Analysis of Experimental Results  
#### 6.3.1 Results of Data Standardization  
In order to reduce the influence of data magnitude on the model impact, Z-score standardization is applied to the WDBC dataset, and some of the results are shown in Table 4.
## 2 Materials and Methods
### Acceleration Data Recording and Processing  
Head and chest acceleration were recorded by using two Shimmer GSR3+ systems (Shimmer Sensing, Dublin, Ireland) with integrated inertial sensing via accelerometer and gyroscope at a sampling rate of 100 Hz. Before the beginning of each experimental session, the accelerometer and gyroscope of the IMU devices were calibrated on a flat and stable surface according to manufacturer guidelines to obtain their relative correct offsets, sensitivity, and alignment matrices. Once the calibration was completed, we positioned one device on the forehead and one on the chest of each participant. Then, from each device, the linear acceleration was obtained through data fusion of the acceleration and angular velocity with the use of the Madgwick filter [59] implemented in the _imufusion_ Python package. Finally, we used the linear acceleration from the three axes to calculate the modulus of the acceleration of the head and the chest. To describe the data distribution related to possible micromovements associated to vigilance, for each minute of the PVT, the median value and the median absolute deviation (MAD) of the modulus of the accelerations were estimated as a measure of the intensity and variability of participants movements, respectively. In other words, the median value of the acceleration describes the magnitude (i.e., how much) over time of the participants' head micromovements and body postures changes, while the MAD describes the variation over time in the magnitude exhibited by the participants' head micromovements and body posture variations due to vigilance variations. Additionally, the use of statistical parameters like the median and MAD allowed us to obtain distributions that were not heavily influenced by possible outliers due to sporadic large movement of the head and the body. In particular, values derived from the sensor on the head were used as estimations of head micromovements because they are related to attentional states [54], while the sensors on the chest were used as estimations of body posture changes (for a review see [60]). Acceleration median and MAD values were then normalized with respect to the corresponding values obtained during the movement-free condition (open eyed phase) by using median and median absolute deviation (MMAD) normalization due to its robustness to outliers [61].
## 3 Research Design
### Relevant Technological Concepts
#### 3.3.2 NLP for Text Summarization  
There are two methods of automatic text summarization. One is the extraction-based method of text summarization, and the other is the abstraction-based method. The differences according to [PERSON] [19] are:  
* Extraction-based or extractive text summarization involves extracting key phrases from the source and combining them to create a summary. Such extraction is done according to pre-defined metrics and without any alterations of the text.
* Abstraction-based text summarization includes the paraphrasing and shortening of elements within the source. Abstraction is used in text summarization in the context of deep learning problems and has the capability of overcoming grammar inconsistencies which are often associated with extractive methods. Abstractive text summarization algorithms create entirely new sentences which include the most relevant information.  
For its simplicity, extraction-based summarization was used within this work to develop a first prototype. It is assumed, however, that a future product based on our work could benefit from the use of abstractive summarization.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 4 Significance of Communications in IoMT
### Communication Protocols  
In general, communication protocols specify the rules and conventions for transmitting data over communication networks, thereby ensuring efficient and reliable communication. Communication protocols are crucial for modern interconnected systems, as they allow devices and applications to exchange data seamlessly. While there are many protocols available, a few stand out for their unique features. HTTP (Hypertext Transfer Protocol) is the foundation of the World Wide Web, and is used for client-server communication over the internet. For IoT and constrained environments, CoAP (Constrained Application Protocol) is lightweight and efficient, making it ideal for resource-constrained devices. AMQP (Advanced Message Queuing Protocol) and MQTT (Message Queuing Telemetry Transport) are messaging protocols, with AMQP prioritizing enterprise-grade reliability and MQTT focusing on lightweight publish-subscribe messaging. XMPP (Extensible Messaging and Presence Protocol) is used for real-time communication and fosters instant messaging and presence information exchange. COBRA (Common Object Request Broker Architecture) provides a framework for distributed object communication, while ZeroMQ offers a flexible socket-based messaging approach for high-performance applications. In industrial automation, DDS (Data Distribution Service) and OPC UA (Unified Architecture) ensure interoperability and real-time data exchange within distributed systems. Finally, DPWS (Devices Profile for Web Services) facilitates standardized communication for devices in a web services environment, helping to achieve interoperability in heterogeneous systems. Each of these protocols serves specific communication needs, collectively forming a diverse ecosystem essential for modern information exchange. These different communication protocols are explained briefly in Table 5:  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
# Evaluation of Clumping Effects on the Estimation of Global Terrestrial Evapotranspiration
## 2 Modeling Methodology
### The Two-Leaf Model for ET Modeling  
BEPS follows the sunlit-shaded two-leaf scheme in simulating canopy-scale GPP and ET [18; 31]. In BEPS, the vegetation canopy is stratified into two layers as the overstory canopy and the understory canopy. Both are divided into sunlit and shaded portions because the greatest difference in leaf irradiance exists between sunlit and shaded leaves [18]. The method of calculating sunlit and shaded LAI [46] was improved to incorporate the leaf clumping effect on the canopy radiation transfer [18]:  
\[LAI_{o\_sun}=zos\theta(1-e^{-0.5\Omega LAI_{o}/cos\theta}) \tag{9}\]  
\[LAI_{o\_sh}=LAI_{o}-LAI_{o\_sun} \tag{10}\]  
\[LAI_{u\_sun}=2 cos\theta(1-e^{-0.5\Omega(LAI_{o}+LAI_{u})/cos\theta})-LAI_{o\_sun} \tag{11}\]  
\[LAI_{u\_sh}=LAI_{u}-LAI_{u\_sun} \tag{12}\]  
where \(LAI_{o\_sh}\) and \(LAI_{o\_sun}\) are overstory shaded and sunlit \(LAI\), respectively. \(LAI_{u\_sh}\) and \(LAI_{u\_sun}\) are understory shaded and sunlit \(LAI\), respectively. \(\theta\) is solar zenith angle.  
Canopy ET was calculated as the sum of sunlit and shaded components as:  
\[ET_{o}=ET_{o\_sun}LAI_{o\_sun}+ET_{o\_sh}LAI_{o\_sh} \tag{13}\]  
\[ET_{u}=ET_{u\_sun}LAI_{u\_sun}+ET_{u\_sh}LAI_{u\_sh} \tag{14}\]where \(ET_{o\_sun}\) and \(ET_{o\_sh}\) are \(ET\) from a representative sunlit and a representative shaded leaf in the overstory canopy, respectively. \(ET_{u\_sun}\) and \(ET_{u\_sh}\) are ET from a representative sunlit and a representative shaded leaf in the understory canopy, respectively.