esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 2 Related Works
### Comparison of Recommendation Works  
In recent years, various recommendation techniques [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27] have been developed to improve accuracy, adaptation and flexibility using different filtering techniques and strategies. Table 1 illustrates a comparison between several approaches in terms of accuracy, online learning process, data set, scalability, adaptation and flexibility. We have concluded that the content-based recommendation systems [5; 6; 7; 8; 9; 10] support the cold start problem in case of updating the data set with new items. However, they suffer from low accuracy compared to collaborative filtering systems. Collaborative filtering techniques [11; 12; 13; 14; 15; 16; 17; 18; 19; 20] provide a better accurate recommendation but suffer from the cold start problem. The existing hybrid approaches [20; 21; 22] aim to combine different recommendation approaches to yield better recommendations across the board. However, these approaches are not flexible against context changes and evolution of users' needs. In other words, they neglect context-awareness during the recommendation process. Considering context information during the recommendation process improves the recommendation accuracy. Thus, several recommendation approaches adopt context awareness [23; 24; 25; 26; 27] to achieve a high recommendation accuracy. To cope with this limitation, the purpose of this research paper is to present a novel approach. It consists in enriching the user's agenda with relevant situations rules. We have the opportunity to classify situations rules by role, location and time in order to guide and filter information depending on their context and preferences to adapt the search process to the specific needs of users.
# Evaluation of Shear Effect on Deflection of RC Beams
## 4 Discussions
### Effective Moment of Inertia  
The experimental result of the effective moment of inertia \(I_{e,exp.}\) was obtained by using the following equation based on the geometrical conditions and general elastic theory for the deflection measured from the LVDT installed at the mid-span of the specimens:  
\[I_{e,exp.}=\frac{M_{a}}{24E_{c}\Delta}(3l^{2}-4a_{v}^{2}) \tag{3}\]  
where \(M_{a}\) is the applied moment at which the deflection is calculated, \(E_{c}\) is the elastic modulus of the concrete, \(\Delta\) is the deflection of the beam, \(l\) is the clear span of the beam, and \(a_{\varphi}\) is the shear span of the beam. For the analytical result of the effective moment of inertia, the following formula proposed by ACI 318-19 [1] was used:  
\[I_{e}=\frac{I_{cr}}{1-\left(\frac{(2/3)M_{cr}}{M_{a}}\right)^{2}\left(1-\frac{ I_{cr}}{I_{g}}\right)} \tag{4}\]  
for \(M_{a}>(2/3)M_{cr}\), \(I_{e}=I_{g}\) for \(M_{a}\leq(2/3)M_{cr}\), \(I_{cr}\) is for the cracked section transformed to concrete, \(I_{g}\) is for the gross concrete section about the centroidal axis, \(M_{cr}\) is the cracking moment.  
Figure 6 shows the comparison between the experimental and analytical results of the effective moment of inertia. After the crack occurred, the analytical and experimental results had similar curves. As shown in Figure 6, as the shear span-to-depth ratio decreased or the tension reinforcement ratio increased, the effect of shear increased and the difference between the experimental and analytical results increased. In other words, comparing the experimental and analytical results for the \(I_{e}\) value at the yield moment, the difference for B6-4.0 series specimens subjected to relatively low shear stress was about 1.3 times, while the difference for B3-2.5 and B6-2.5 series specimens having relatively high shear stress was about 1.4 and 1.5 times.  
As shown in Figure 6, for each series specimens, no significant difference was observed in most of the specimens for the shear capacity ratio. Although Specim B6-2.5c showed almost no difference between the experimental results and the analysis results in the initial working load stage after cracking moment, as the load increased with the yield moment, it showed almost similar differences with Specimens B6-2.5a and B6-2.5b.  
Figure 6: Moment versus effective moment of inertia relationships: (**a**) B3-2.5 series; (**b**) B6-2.5 series; (**c**) B6-4.0 series.
## 3 Networks and Methods
### Time-Serialization Convolutional Neural Network  
The TSCNN is a type of feed-forward neural network with input and output layers that handle various types of data. In a TSCNN, one- or two-dimensional (2D) time-signal data are often processed.  
The TSCNN has multiple hidden layers, including convolutional, pooling, and fully connected layers. The convolutional layer can effectively extract feature information from the input data through convolutional operations, and the pooling layer can further reduce the dimensionality of the data to stabilize the network. Finally, the fully connected layer connects the output of the pooling layer with neurons to obtain output results such as labels.  
Figure 7 illustrates the TSCNN structure, which includes multiple convolutional, pooling, and fully connected layers. This model can effectively handle different types of data and is widely used in tasks such as image analysis, speech recognition, and natural language processing.  
In this study, the input shape of the TSCNN was x * 2, where x represents the time step. In the convolutional layer, the number, size, and features of the convolutional kernels were obtained through GA, and L2 regularization was used to limit the parameters. A rectified linear unit (ReLU) function was used as the activation function. The strides of both the convolutional layer and the pooling layer were set to  
Figure 6: Graphical representation of the time-series data for model training.  
Figure 7: TSCNN model structure diagram.  
A max-pooling operation of size two was used for the pooling layer. The fully connected layer used the tanh activation function with an additional dropout layer, pruning 30% of the neurons to prevent overfitting.  
During model training, the Adam optimization algorithm and the MSE loss function were used to update the weights. The Adam algorithm is an adaptive learning rate optimization algorithm that converges faster. The MSE loss function is commonly used in regression problems to measure the gap between actual and predicted values.  
In summary, the TSCNN structure used in this study is feasible and considers various factors to optimize model performance. Common techniques such as the ReLU activation function, Adam optimizer, MSE loss function, and dropout layer were applied to avoid overfitting.
## 4 Experiments
### Results and Discussions
The uncertainty maps indicate that the regression models fit closely to the original error forms, which yield a small uncertainty value in each pixel. On the other hand, the estimated PV uncertainties are believed to coincide with the results using another approach with the uncertainty matrix. By adding and subtracting the uncertainty matrix to the calibration data set, the standard uncertainty of the peak (\(u_{\rm p}\)) and valley (\(u_{\rm v}\)) of the form errors expected to lie could be obtained separately. The PV uncertainties could be obtained in exactly the same manner as Equation (33), which would be similar to the results when using the proposed PV uncertainty evaluation method in the observed directions. This part of work will be conducted as future work. \begin{table}
\begin{tabular}{c c c c} \hline
**Uncertainty Source** & **Symbol** & **Type** & **Standard Uncertainty** \\ \hline \(Z\)-directional zeroth-order phase outputs & \(u[I_{0}(x,y)]\) & A & 0.0825 rad \\ Out-of-flatness of reference flat & \(u[\varepsilon_{R}(x,y)]\) & B & 2.11 nm \\ Regression coefficients matrix & \(u(A)\) & A & Multiple values \\ \hline PV uncertainty of \(\varepsilon_{Z}(x,y)\) (\(k=2\)) & \(u\{\text{PV}\ [\varepsilon_{Z}(x,y)]\}\) & & 4.12 nm \\ \hline \(X\)-directional positive first-order phase outputs & \(u[I_{X+1}(x,y)]\) & A & 0.0761 rad \\ \(X\)-directional negative first-order phase outputs & \(u[I_{X-1}(x,y)]\) & A & 0.0347 rad \\ Grating pitch & \(u[I_{X-1}(x,y)]\) & B & 100 nm \\ Mechanical adjustment error of the tilt stage & \(u(\varepsilon_{1,2})\) & B & 0.1\({}^{\circ}\) \\ Regression coefficients matrix & \(u(B)\) & A & Multiple values \\ \hline PV uncertainty of \(\varepsilon_{X}(x,y)\) (\(k=2\)) & \(u\{\text{PV}\ [\varepsilon_{X}(x,y)]\}\) & & 1.60 nm \\ \hline \(Y\)-directional positive first-order phase outputs & \(u[I_{Y+1}(x,y)]\) & A & 0.8420 rad \\ \(Y\)-directional negative first-order phase outputs & \(u[I_{Y-1}(x,y)]\) & A & 0.5013 rad \\ Grating pitch & \(u(g)\) & B & 100 nm \\ Mechanical adjustment error of the tilt stage & \(u(\varepsilon_{2})\) & B & 0.1\({}^{\circ}\) \\ Regression coefficients matrix & \(u(C)\) & A & Multiple values \\ \hline PV uncertainty of \(\varepsilon_{Y}(x,y)\) (\(k=2\)) & \(u\{\text{PV}\ [\varepsilon_{Y}(x,y)]\}\) & & 10.72 nm \\ \hline \end{tabular}
\end{table} 
Table 2: Uncertainty budget.
# AI-Driven Waste Management in Innovating Space Exploration
## 6 International Collaboration and Policy  
International collaboration involving multiple space agencies and private entities like NASA, ESA, Roscosmos, SpaceX, and Blue Origin is essential for advancing smart waste management in space. As space exploration becomes increasingly global, establishing standardised waste management protocols is vital. These collaborations create a practical framework under international environmental and space law, promoting long-term sustainability through the waste hierarchy concept. Such cooperative efforts are crucial for ensuring efficiency, safety, and sustainability in the international domain of space operations.  
With the commercialisation of space exploration and the growing involvement of various countries and private entities, standardised waste management protocols are more important than ever. This standardisation ensures that government-led and privately operated missions adopt a unified waste handling, processing, and disposal approach. The lack of coordination poses significant risks, including the accumulation of space debris that threatens satellites, spacecraft, and human habitats [80,81]. Coordinated efforts are necessary to mitigate these risks and enhance the safety of space operations.  
International standards emphasising waste treatment and resource recycling are pivotal for minimising the environmental impact of space activities. Agencies like NASA, ESA, and Roscosmos, with their experience in collaborative projects such as the International Space Station (ISS), play a key role in advancing waste management systems. These collaborations have led to developing technologies for air filtration, water recovery, and waste storage, which are crucial for long-duration missions like NASA's Artemis program and ESA's ExoMars mission.  
Collaboration allows countries and organisations to share knowledge and technologies, leading to more efficient waste management systems [82,83]. By working together, space agencies can establish common standards for international missions, ensuring adherence to shared environmental and safety protocols. As missions extend to lunar bases or Mars colonies, regulations must evolve to manage waste and ecological impacts effectively, focusing on sustainability and integrating advanced waste processing technologies.
## 2 Mathematical Modeling
### Sliding Mode Controller Design
#### 2.1.4 Stability Analyze  
Lyapunov theory is a fundamental method in the study of the stability analysis of dynamical systems. Thus, by applying the Lyapunov theory, we can analyze the stability of the proposed system. To provide the asymptotic stability of _(8)_, about the equilibrium point \(s=0\), the following three important conditions must be satisfied [34],  
1. \(\lim_{|s|\to\infty}V=\infty\),
2. \(\dot{V}\leq 0,\ \forall s\
eq 0\),
3. \(V(0)=0\).  
Thus, the Lyapunov candidate function is chosen as  
\[V(s)=\frac{1}{2}s^{2}, \tag{14}\]  
the time derivative of _(14)_ can be computed as,  
\[\dot{V}(s)=s\dot{s}=s\bigg{(}\bigg{(}\lambda-\frac{1}{\tau}\bigg{)}e_{3}-\frac {1}{\tau}u\bigg{)}\leq 0, \tag{15}\]  
hence, what the Lyapunov stability theory says is that if _(15)_ is satisfied, then the system is stable. To check whether _(15)_ is satisfied, first from (9), we have the total control input \(u\). Then, if we substitute _(9)_ to _(15)_, we obtain  
\[\dot{V} =s\dot{s}=s\bigg{(}\bigg{(}\lambda-\frac{1}{\tau}\bigg{)}e_{3}- \frac{1}{\tau}(\rho\,\text{sgn}(s)+(\tau\lambda-1)e_{3})\bigg{)}\leq 0, \tag{16}\] \[\tau\dot{V} =s((\tau\lambda-1)e_{3}-(\tau\lambda-1)e_{3}-\rho\,\text{sgn}(s) )\leq 0,\] \[\dot{V} =-\frac{\rho}{\tau}s\,\text{sgn}(s)\leq 0,\] \[\dot{V} =-\frac{\rho}{\tau}|s|\leq 0.\]  
Hence, the proof. Therefore, from _(16)_, this shows that the proposed system _(1)_ with the control input as _(9)_ is stable based on the Lyapunov stability theory.  
**Remark 2**.: _While designing the sliding variable given in (7), we need to notice that \(\dot{s}\) must be a function of the control \(u\) to successfully design the controller in (9). The state where \(s=0\) corresponds to a straight line in the state space of the system (1) is known as a sliding surface._
## 4 Proposed Method
### Multi-Scale Convolutional Kernels  
As mentioned in Section 4.1, we use a small kernel size for the short-scale module and a large kernel size for the long-scale module. Kernel sizes 2 and 15 are chosen empirically to apply to the short and long scales, respectively. The reason is that the kernel size of 2 aims to extract short temporal patterns, which are beneficial to detect point anomalies since the duration of point-wise anomalies is short (caused by individual time points). On the other hand, the kernel size of 15 tries to capture longer patterns to trigger pattern anomalies, since the duration of pattern-wise anomalies is long (caused by a sequence of continuous points). Moreover, multi-scale convolutional kernels help the model jointly learn patterns at different scales [34].  
While the kernel size of 2 is fixed for the short scale, the kernel size for the long scale can be changed. If the kernel size is too small, it is difficult to capture long patterns. Otherwise, too large kernel size will increase false positives because of overfitting. Note that different kernel sizes are only used for the first Conv1D in the hidden layers; the kernel size of 2 is fixed from the next layer. Figures 5 and 6 illustrates the feature extraction using different temporal scales.  
Figure 5: Short-scale module, window size = 30, strides = 2, kernel size = 2, no zero padding.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 4 Conclusions  
This study presented the successful development and validation of an inertial microfluidic device designed for accurate multi-cell counting, capable of simulating both normal and diseased physiological conditions. The device achieved counting accuracy at a microchannel size of 150 \(\upmu\)m \(\times\) 150 \(\upmu\)m and a flow rate of 1 \(\upmu\)L/min, yielding cell counts of 6.45 \(\times\) 10\({}^{7}\) cells/mL for 8 \(\upmu\)m particles and 1.10 \(\times\) 10\({}^{7}\) cells/mL for 15 \(\upmu\)m particles under normal conditions (600:1 ratio). Under diseased conditions (400:1 ratio), the counts were 4.5 \(\times\) 10\({}^{7}\) cells/mL and 2.16 \(\times\) 10\({}^{7}\) cells/mL, respectively.  
The system demonstrated excellent agreement with standard hemocytometer results, achieving percentage errors as low as 1.8% for the 8 \(\upmu\)m particles and 1.1% for the 15 \(\upmu\)m particles. This represents a substantial improvement over the 5-10% variability typically reported with manual hemocytometer-based counting [43]. In contrast to earlier microfluidic studies that relied on complex multi-layer channel designs, sheath flows, or external actuation methods, the device developed in this study utilizes a single-layer microchannel and passive inertial focusing. This design simplifies the system by eliminating the need for external forces while maintaining high counting accuracy [44]. In this study, a simple single-layer PDMS chip was fabricated using accessible 3D printing and soft lithography. This makes the device highly scalable, reliable, and suitable for low-resource point-of-care diagnostics. The developed microfluidic system not only fulfills the initial objective of accuracy, simplicity, and validation, but also represents a practical advancement toward efficient, adoptable diagnostic devices capable of multi-size cell detection in various healthcare settings. Future directions will include expanding the detection capabilities to a wider range of particle sizes, integrating automated image analysis tools, and further optimizing flow control to enhance the throughput and robustness for real-world clinical deployment.  
Conceptualization, M.Z. and E.U.; Methodology, M.Z., E.U. and A.M.; Software, M.Z. and D.M.; Validation, M.Z., M.I. and Z.A.; Formal analysis and writing review, M.Z., E.U., M.I., S.U. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be made available on a reasonable request.  
We extend our gratitude for the use of the Microfluidics Research Laboratory at the School of Mechanical and Manufacturing Engineering (SMME), National University and Sciences Technology (NUST), Islamabad.  
The authors declare no conflicts of interest.
## 3 Method
### Validation  
#### 3.5.1 Evaluation of Gap Filling Results  
Synthetic NDVI data generated using STDFA methods were evaluated using either HJ NDVI or GF NDVI on the same day. The Synthetic NDVI data in Luntai were evaluated using HJ NDVI on 26 April and GF NDVI on 14 July 2013. Synthetic NDVI data on 26 April 2013 were generated by inputting HJ NDVI data acquired on 22 April 2013 and 7 May 2013 and MODIS NDVI data acquired on 22 and 26 April 2013. Synthetic NDVI data on 14 July 2013 were generated by inputting GF NDVI data acquired on 10 and 18 July 2013 and MODIS NDVI data acquired on 10 and 14 July 2013. The evaluation was performed using parameters of correlation coefficient (\(r\)), variance (\(Var\)), mean absolute difference (\(MAD\)), bias, and root mean square error (\(RMSE\)).
# Ultra-Generalized Continuous Class F Power Amplifier with Finite Third-Harmonic Load Impedance
## 2 Analysis of the Proposed UCCF Mode  
### Gate Input Voltage Waveform of Proposed UCCF Mode  
A transistor is a voltage-controlled current source, and a change in the gate input voltage waveform can lead to a change in the drain current waveform. Therefore, it is necessary to analyze the gate input voltage waveform first. The gate input voltage waveform of the UCCF mode (\(v_{GS,UF}\)) in one signal period is shown in Figure 2 and can be represented as  
\[v_{GS,UF}=V_{GS0}+V_{GS1}\times pcr\times\frac{\cos(\theta)-\cos\left(\frac{ \alpha}{2}\right)}{\left(1-\cos\left(\frac{\alpha}{2}\right)\right)} \tag{1}\]  
Figure 1: The parasitic parametric CGH40010F model [31].  
where \(V_{GS0}\) is the gate threshold voltage, \(V_{GS1}\) is the gate input voltage fundamental component, \(pcr\) refers to the peak-clipped ratio, \(\theta\) denotes the angular phase of the cosine wave, and \(\alpha\) denotes the drain current conduction angle. We rearranged the terms and normalized the gate input voltage using \(V_{GS1}\). The normalized gate input voltage \(v_{GS,norm}\) could then be expressed as  
\[v_{GS,norm}=\frac{v_{GS,U\!F}-V_{GS0}}{V_{GS1}}=pcr\times\frac{\cos(\theta)- \cos\Bigl{(}\frac{\alpha}{2}\Bigr{)}}{\Bigl{(}1-\cos\Bigl{(}\frac{\alpha}{2} \Bigr{)}\Bigr{)}} \tag{2}\]  
where  
\[pcr=\frac{v_{GS,U\!F}-V_{GS0}}{v_{GS,max}-V_{GS0}} \tag{3}\]  
and \(v_{GS,max}\) is the input voltage amplitude that just reaches the saturation point. Figure 2 depicts states of the gate input voltage waveform for various modes. As shown in Figure 2, the gate input voltage of the continuous class F (CCF) mode is an ideal cosine wave where \(\alpha\) equals \(\pi\) and \(pcr\) equals 1. The GCCF mode achieves the finite third-harmonic load impedance by expanding \(\alpha\), and in this mode, \(pcr\) is equal to 1. The PCC-CF\({}_{3}\) mode achieves the finite third-harmonic load impedance by inputting an overdriven gate voltage, and in this mode, \(\alpha\) is equal to \(\pi\). To leverage the advantages of both modes, the UCCF mode combines \(pcr\) and \(\alpha\) to reshape the drain current waveform. The GCCF and PCC-CF\({}_{3}\) modes are special cases of the UCCF mode.  
Figure 3a,b illustrate the variationn \(v_{GS,norm}\) with \(pcr\) and \(\alpha\). When \(pcr\) is the same, the peak-to-peak amplitude of \(v_{GS,norm}\) decreases with an increase in \(\alpha\). When \(\alpha\) stays the same, the peak-to-peak amplitude of \(v_{GS,norm}\) increases with an increase in \(pcr\). This indicates that both \(pcr\) and \(\alpha\) affect the input power, as an increase in \(\alpha\) decreases this parameter and an increase in \(pcr\) increases it. These opposing trends lead to the different effects of \(pcr\) and \(\alpha\) on the PA's performance.  
Figure 2: Gate input voltage waveforms with different modes.
## 5 Conclusions  
Even though AM continues to show promise for large-scale production of complicated geometric components, their mechanical behavior and, therefore, their reliability are hardly recognized. This provides a challenge for the complete implementation of the AM technique in diverse engineering, biomedical, and dental-implant applications. The hybrid lattice structure is the future material to maintain a balance between porosity (for bone ingrowth) and mechanical properties, graded TPMS lattice structures meet the demands of bone-in growth and high-stress regions for bearing high loads. Further tuning the morphological parameters and strut diameter for multidirectional will improve the strength and high-energy absorption with a systematic porosity distribution over implants. Using a hybrid gradient TPMS lattice structure for bio-implants will alter the porosity, cell size, and pore size separately with more flexible and smooth transitions of mechanical properties [106].  
Based on these, numerical models have been developed to enhance the fatigue resistance of components in an attempt to address this challenge. Cumulative findings indicate that numerical simulation results closely reflect experimental outcomes. This will decrease the enormous expense and duration of experiments [107]. Several pieces of research reveal that enhancing the surface polish and adjusting the surface geometry can increase the fatigue life of components. Formulating numerical simulation models based on the structure-process-fatigue properties (SPFP) method for AM components would be the first step in limiting prototypes, decreasing experimentation, optimizing the design, and enhancing the component's dependability. FEA can be used to make models that can be used to figure out the fatigue life of any part with a complicated shape. Lattice structures are future industrial materials. Because of its high performance and unique properties, topological optimization of porous lattice structures should be explored for biomedical and industrial materials. The experimental inquiry will concentrate on gaining an understanding of the typical behavior of porous structure materials.  
Data collection; Analysis; Methodology: all; format analysis, implementation, and validation: R.G.; Resources: L.M. and F.C.; writing--original draft preparation: R.G.; writing: R.G.; Supervision: L.M. and F.C. All authors have read and agreed to the published version of the manuscript.  
The publication of this work is supported by the Open Access Publishing Fund of the Free University of Bozen/Bolzano.  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.