esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Practical Implications and Technical Challenges  
The practical implication of our work is that our proposed Chinese text classification framework shows notable improvements over comparable baselines. Our proposed template generation model is able to generate high-quality task-specific templates for each corpus. In addition, experiment results further illustrate our proposed joint correlation score function is able to select informative samples as demonstration examples.  
Although our proposed framework achieves state-of-the-art performances on various independent tasks, prompt learning for Chinese text classification is yet to be fully explored. In summary, the technical difficulties and challenges can be summarized as follows:  
* To achieve the best performance on different tasks, the template generation module needs to be retrained on different corpus in order to generate task-specific templates, which is inefficient in real-life applications.
* During template evaluation, the best-performing template needs to be selected by zero-shot prediction on the validation set, which is acceptable when the sample size is small; however, it can be time-consuming in traditional text classification tasks.
* In order to generate high-quality templates, text-to-text pretrained models are used for fine-tuning and text generation tasks, a process that requires a high level of computer hardware. For example, the BART we use requires at least 600 M of memory for reading the model.
## 4 Simulation Analysis
### Packet Delivery Ratio-PDR  
It is the ratio of successfully received data packets by the sinks and the total number of generated data packets by the source node. Let, \(K_{1}\) denotes the set of successfully received data packets received by sink 1 and there are \(m\) sinks and \(n\) data packets generated, the PDR is denoted as:  
\[PDR=\frac{\left|\bigcup\limits_{j=1}^{m}K_{j}\right|}{n}\]  
The main objective of any routing or forwarding scheme is to improve different performance metrics of the network and PDR is one of them. Therefore, we also analyzed and discussed the PDR performance metric of the EDOVE scheme. PDR performance of the EDOVE and WDFAD-DBR for varying network sizes and transmission ranges are shown in Figures 8 and 9, respectively. Figure 8 shows that the PDR of the EDOVE is improved compared to the WDFAD-DBR for different size networks. As we discussed the phenomenon previously that the number of network-wide forwarded copies of data packets is directly proportional to the network size for the fixed \(t_{r}\). Because of the large data packet transmissions, the packet collision probability becomes high, and the energy of  
Figure 6: Number of forwarded copies of data messages in the network for different transmission ranges.  
Figure 7: Data packet forwarding load reduced by EDOVE compared to WDFAD-DBR.  
the intermediate relaying nodes depletes quickly. As most of intermediate forwarder nodes' battery power depletes, they could not forward the packets. As EDOVE fairly selects forwarders by using their normalized residual energy in the holding time computation, it avoids penalizing the low battery power nodes in the network. Hence, the network using EDOVE survives longer than the network that uses the WDFAD-DBR scheme. The energy consumption and the number of dead nodes are also analyzed and discussed later in this section. The dead nodes create a void region and/or a layer is created that completely disconnects the source node from the sink(s). These events collectively result in a drastic reduction in the PDR. Additionally, it can also be observed from the figure that as the network size increases, PDR of EDOVE either remains constant or slightly increases, which is not the case of WDFAD-DBR.  
Next, in the fixed network size and variable transmission range scenario, as the transmission range increases node density increases as well, refer Figure 9. This increases the probability of successfully forwarding the data packets toward the sink(s) because a large number of redundant transmissions can be suppressed as well as the number of hops between source and sink(s) decreases. This increase in suppression and decrease in hop distance, increases the network lifetime, does not immediately create any void region, and more packets are successfully forwarded towards the sink. On average, EDOVE has 17% more PDR than WDFAD-DBR for any size network and \(t_{r}=600\) m. Similarly, EDOVE improves about an average of 12% PDR for any \(t_{r}\geq 700\) m contrast to WDFAD-DBR.
## 3 Case Study
### Assessment of the POCS Pressure Drop
#### 3.2.2 One-Dimensional Performance Simulation  
To provide a complete characterization of such an application, a 1D engine simulation is performed, resorting to a 1D thermo-fluid dynamic model (Gasdyn) capable of simulating the whole engine system with a limited computational time. The code allows one to determine the volumetric efficiency, torque, power, fuel consumption, pollutant emissions, and tailpipe noise on the basis of the operating conditions of the engine [19; 20]. The 1D setup is made on a prototype engine for high-performance applications with the aim of comparing a base case with the case in which the POCS is inserted in the exhaust ducts. The single bank of a V12 reference engine is considered and the POCS presence is modeled by introducing a dedicated source term in the momentum conservation equation. The source term is in the form of:  
\[\frac{\Delta p}{l}=\frac{f_{w}}{2d}\cdot\rho U^{2} \tag{13}\]  
where \(\Delta p\) represents the induced pressure difference across the POCS, \(\rho\) is the fluid density, \(d\) is the pipe diameter, \(l\) is the structure length, \(U\) is the mean flow velocity, and \(f_{w}\) is the friction factor (estimated by means of the 3D CFD results). The full-load behavior of the engine is investigated through the described simulation framework, starting from 1500 to 9000 rpm.
# Fracture Toughness of Hybrid Components with Selective Laser Melting 18 Ni300 Steel Parts
## 3 Results and Discussion
### Fracture Toughness
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Valid** & **K\({}_{\rm Q}\)** & **Average K\({}_{\rm Q}\)** & **J\({}_{\rm c}\)** & **Average J\({}_{\rm c}\)** & **Critical Crack,** \\
**Reference** & **K\({}_{\rm Q}\) = K\({}_{\rm lc}\)** & **(MPa.m\({}^{1/2}\))** & **(MPa.m\({}^{1/2}\))** & **(MPa.m\({}^{1/2}\))** & **(N/mm)** & **(N/mm)** & **a\({}_{\rm c}\) (mm)** \\ \hline
2 SM01\_3 & No & 72.99 & & & 59.23 & & 31.26 \\
2 SM02\_3 & No & 80.44 & & 67.86 & 65.68 \(\pm\) & 31.31 \\
2 SM03\_3 & Yes & 90.85 & & 73.26 & 5.35 & 31.11 \\
2 SM04\_3 & No & 76.40 & & 62.4 & & 31.96 \\ \hline
2 SM13\_6 & No & 76.38 & & 42.9 & & 29.39 \\
2 SM14\_6 & No & 68.93 & & 40.1 & & 30.62 \\
2 SM15\_6 & No & 77.77 & & 46.3 & & 31.03 \\
2 SM16\_6 & No & 68.32 & & 40.3 & & 29.11 \\ \hline
4 SM05\_6 & No & 49.22 & & 19.71 & & 29.65 \\
4 SM06\_6 & No & 52.56 & 55.42 \(\pm\) 4.71 & 12.27 & 18.65 \(\pm\) & 28.89 \\
4 SM07\_6 & No & 60.71 & & 23.71 & 4.10 & 30.23 \\
4 SM08\_6 & No & 59.18 & & 18.93 & & 30.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fracture toughness tests results for SM specimens. The estimated K\({}_{\text{Q}}\) for the specimens built with 200 mm/s of scan rate and 6 mm thick showed a decrease about 10% from the K\({}_{\text{Ic}}\) value of the commercial steel grade 300 [18]. On the other hand, the 3 mm thick samples presented similar values between the estimated K\({}_{\text{Q}}\) and the K\({}_{\text{Ic}}\) value of the commercial steel grade 300 [26] around 80 MPa.m\({}^{1/2}\). [PERSON] et al. [12] show the level of defects observed in two samples produced with 200 mm/s and 400 mm/s of speed rate scan, respectively. It can be observed that the level of defects (area) increases from 0.74% to 7.37% when the speed increases from 200 to 400 mm/s. Besides, the fracture toughness decreases with the increase of scan speed rate, as previously mentioned (Table 5). Therefore, it can lead to a conclusion that the fracture toughness of SLM maraging steel grade 300 is lower that the commercial steel grade 300 produced by conventional processes, due to the internal defects (porosities, unmelted powder, unmelted zones, among others) and their effects.
# Hardware Development and Safety Control Strategy Design for a Mobile Rehabilitation Robot
## 5 Experimental Validation
### Discontinuity Recovery Safety Performance Test  
This experiment was conducted to show the motion recovery from significant angular errors. We used a discontinuous position function to examine the performance on resuming motion and compare it with the PID stiff position control. The discontinuous position function is shown as:  
\[y_{d}^{\text{angle}}(t)=\begin{cases}28.1-17.7\cos(6.5t)-11.2\sin(6.5t)\\ +1.2\cos(12.9t)-17.2\sin(12.9t)\\ -1.9\cos(19.4t)+3.5\sin(19.4t)\\ +0.7\cos(25.9t)+1.2\sin(25.9t)\\ -0.1\cos(32.4t)-0.7\sin(32.4t)\\ -15\\,3.75\leq t\end{cases},0\leq t<3.75\. \tag{36}\]  
The parameters for PID stiff position control were chosen to be \([K_{p},K_{I},K_{D}]=[15,0.2,0.5]\). The design of the LES was identical to those used in the first three experiments, as listed in Table 2. Figure 17 shows the tracking results of both control strategies. The results show a quite similar performance between the ILESO-PSMC tracking and the PID stiff position controller when the reference trajectory was continuous and smooth. However, the PID controller shows oscillations when positional discontinuity appears, while ILESO-PSMC still performed smooth, slow, and safe position tracking toward the desired value. Overshoot occurred during the quick recovery process when using the PID, while overdamped recovery occurred when using the ILESO-PSMC. Thus, the ILESO-PSMC can guarantee the system's safety by canceling the overshoot during the position tracking. Figure 17 shows no chattering in the tracking trajectory when using the ILESO-PSMC. Hence, the ILESO-PSMC presents the advantages of safety and accuracy, which are inherited from the ideal SMC; conversely, the PID stiff position control cannot provide these advantages because of the overshoot.  
Figure 17: Discontinuity recovery safety test using PID and ILESO\(-\)PSMC. (**a**) Joint angle tracking response; (**b**) joint angle tracking error; (**c**) current control signal.
## 1 Introduction  
Electric grids are evolving, where capabilities and functionalities are added to the existing infrastructure to improve efficiency and quality of service. To this end, current technology's growing processing and intelligence capabilities are being used, giving rise to the so-called SG or smart grid. It will be the basis for a balance between energy supply and demand and the first step towards smart energy, a system in which the consumer will also become a producer of surplus energy [1; 2; 3]. In this way, the digitalization of the energy sector becomes a crucial factor for decarbonization by increasing energy efficiency and supporting the use of renewable energy sources [4; 5; 6] through the integration of variable production technologies that improve the quality and security of supply [7; 8], thus creating an efficient, resilient, and competitive energy market [9; 10].  
The SG is defined as an electrical grid that can integrate the actions of all connected users to be efficient in providing a sustainable, economic, and secure electricity supply [11; 12]. The transformation of grids towards a smart, secure, and reliable infrastructure will enable the challenges of a complete electrification of the economy, the integration of renewable sources, sustainable mobility, and a more empowered and connected consumer to be met [13].  
To reach the full potential of the electricity market in a competitive and robust manner, investment in research and innovation must be made to develop the infrastructure by implementing the necessary technologies. This paper proposes the design of a comprehensive and flexible architecture based on different elements of information and communication technologies (ICT) that allow the processing of data produced by energy sources in the generation phase and consumption data recorded by a low-cost meter. The architecture uses components such as IoT, cloud computing, AI, and mobile technology to allow the user to consult their consumption information, providing the possibility to improve the efficiency of their energy consumption. It raises the level of understanding of disturbances in energy production and consumption, such as fluctuations introduced by system participants dynamically, in turn facilitating planning that not only considers the technical aspects but also how participants respond to changes in economic terms.  
This work seeks to present an efficient model that allows the integration of data from various sources, such as consumption meters and energy production sensors. The objective is to facilitate the interpretation and use of information on energy consumption and production in Panama using data analysis and visualization tools. With this solution, it is expected to contribute to the improvement of energy efficiency and decision-making on the management of energy consumption and production.  
The structure of this study is as follows: Section 2, background; Section 3, definition of the problem and motivation; Section 4, materials and methods; Section 5, results and discussions; and finally, in Section 6, the conclusions.
# Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach
## 4 Experimental Results and Discussion
### Comparative Performance Analysis  
In this section, the accuracy performance of the proposed RoughLSTM model is compared with other approaches, namely CNN-LSTM [32], WaveletLSTMa [16], and conventional LSTM models. The obtained results are evaluated based on axis-based accuracy values for various operations in MO1, MO2, and MO3 machines. Moreover, the results for each machine are presented in Table 5, Table 6, and Table 7, respectively.  
As seen in Table 8, in the experiments conducted on the MO1 machine, RoughLSTM achieved the highest or second-highest accuracy values in many operation and axis combinations. In the OP01+OP02 operation, RoughLSTM provided the best results for the x-axis (0.8425) and z-axis (0.8245), while achieving the second-best performance for the y-axis (0.8312). Similarly, in the OP04+OP10 operation, RoughLSTM achieved the highest accuracy for the y-axis (0.8250) and z-axis (0.8367), while maintaining competitive performance in other axes.  
achieved the highest accuracy for the y-axis (0.9389) and z-axis (0.9514). In the OP07+OP11 operation, RoughLSTM demonstrated the best performance for the z-axis (0.9542) and y-axis (0.9347). These findings highlight the effectiveness of RoughLSTM in specific axes on the MO2 machine.  
Table 10 presents the results for the MO3 machine, where RoughLSTM exhibits varying performance across different operations. In the OP04+OP07 operation, RoughLSTM provided the highest accuracy for the x-axis (0.8750) and demonstrated competitive performance for the z-axis (0.8233). In the OP10+OP14 operation, RoughLSTM achieved the highest accuracy for the y-axis (0.8792).  
The findings reveal that RoughLSTM outperforms other models in many scenarios. Specifically, RoughLSTM has achieved the highest or second-highest accuracy values in many operations on the z-axis. While the LSTM model generally exhibits the lowest performance, RoughLSTM is found to be competitive with or superior to CNN-LSTM and WaveletLSTMa.  
In conclusion, the RoughLSTM model demonstrates consistent accuracy across different operation and axis combinations, making it a viable alternative to existing approaches.
# Settlement Prediction of Footings Using \(V_{s}\)
###### Abstract  
The shear wave velocity (\(V_{S}\)) is a key parameter for estimating the deformation characteristics of soil. In order to predict the settlement of shallow footings in granular soil, the \(V_{S}\) and the concept of [PERSON]'s framework were adopted. The \(V_{S}\) was utilized to represent soil stiffness instead of cone tip resistance (\(q_{c}\)) because the \(V_{S}\) can be directly related to the small-strain shear modulus. By combining the \(V_{S}\) measured in the field and the modulus reduction curve measured in the laboratory, the deformation characteristics of soil can be reliably estimated. Vertical stress increments were determined using two different profiles of the strain influence factor (\(I_{z}\)) proposed in [PERSON]'s method and that calculated from the theory of elasticity. The corresponding modulus variation was determined by considering the stress level and strain at each depth. This state-dependent stress-strain relationship was utilized to calculate the settlement of footings based on the theory of elasticity. To verify the developed method, geotechnical centrifuge tests were carried out. The \(V_{S}\) profiles were measured before each loading test, and the load-settlement curves were obtained during the tests. Comparisons between the measured and estimated load-settlement curves showed that the developed method adequately predicts the settlement of footings, especially for over-consolidated ground conditions.  
Keywords:settlement; shallow footing; shear wave velocity; centrifuge test +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Thermodynamic Study  
Thermodynamic studies were performed to determine the nature of the adsorption processes. In this work, the thermodynamic equilibrium constants (\(K_{0}\)) were calculated via \(\ln(K_{0})\) at different investigated temperatures (293, 303, and 313 K) by plotting \(\ln(C_{s}/C_{e})\) versus \(C_{s}\) and extrapolating \(C_{s}\) to zero, as illustrated in Figure 4a [30]. The standard enthalpy change (\(\Delta H^{0}\)) and the standard entropy change (\(\Delta S^{0}\)) of the adsorption process were respectively obtained from the slope (\(-\Delta H^{0}/R\)) and the intercept (\(\Delta S^{0}/R\)) of the plots of \(\ln(K_{0})\) as a function of \(1/T\), as shown in Figure 4b.  
Figure 3: Adsorption isotherm of Co(II) for p-HdA (open symbol) and b-HdA (solid symbols) at various temperatures. Note that the lines show the best fits of the Sips model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**adsorbent** & **BET Surface Area (m\({}^{2}\)/g)** & **pH** & **Temperature (K)** & \(q_{max}\) (mg/g)** & **Reference** \\ \hline Eggshell p-HdA & 11.84 & 4.5 & 398 & 3.41 & [1] \\ Commercial p-HdA & 21.0 & 6.0 & 298 & 14.0 & [6] \\ Biogenic p-HdA & 94.0 & 6.0 & 298 & 62.0 & [6] \\ Synthetic p-HdA & 67.0 & 5.0 & 293 & 20.9 & [19] \\ Synthetic p-HdA & 100.5 & 4.0 & 303 & 22.4 & [20] \\ Activated carbon & 603.0 & 6.0 & 303 & 1.19 & [45] \\ Clinoptiolite & 20.0 & 5.5 & 298 & 2.1 & [46] \\ p-HdA & 67.14 & 7.0 & 293 & 14.07 & This study \\ b-HdA & 34.91 & 7.0 & 293 & 7.73 & This study \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of the adsorption capacities of Co(II) using various adsorbents.  
The calculated parameters are summarized in Table 4. The positive value of enthalpy \(\Delta H^{0}\) confirmed the endothermic nature of the adsorption process. As a result, the adsorption of Co(II) onto the b-HdA was favorable at high temperatures [31]. Furthermore, the positive value of entropy \(\Delta S^{0}\) indicates that the randomness at the b-HdA and solution interface increased during the adsorption process due to the release of the solvated water molecules present on the surface of the b-HdA [38]. Furthermore, the negative values of the standard Gibbs free energy change (\(\Delta G^{0}\)) at all investigated temperatures indicated that the adsorption of Co(II) ions onto the b-HdA was thermodynamically favorable and spontaneous [38].
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 2 An Overview of MSE Growth of GaN  
Following [PERSON] et al. [18], magnetron sputter epitaxy is defined as epitaxial growth by magnetron sputter deposition of GaN under the same stringent vacuum and sample handling conditions as in practice in MBE. The background pressure of the main sputtering chamber is maintained below \(1\times 10^{-8}\) Torr to maintain a high material purity. This section begins with a review of the basic mechanism of reactive sputtering, which is the foundation of MSE, and an account of GaN deposition using alternative sputtering targets. Finally, the design considerations of an MSE chamber equipped with a liquid Ga target is discussed.
## 2 Related Work
### Public Opinion Scene Text Recognition
#### 2.1.2 Text Recognition  
Text recognition is the task of recognizing text in the context of unregulated scenarios. Earlier methods relied on character or word recognition but were unable to recognize characters and phrases in texts of arbitrary length and dictionaries. Since the deep learning text recognition method was proposed by academia, it has effectively solved the above disadvantages. For example, [PERSON] et al. proposed a CRNN network model based on structured sequence recognition [34], and [PERSON] et al. proposed a sliding window SlidingCNN-based method [35]. Text recognition methods based on deep learning can be classified in general as connectionist temporal classification (CTC)-based methods, attention-based methods, and end-to-end methods. The CTC mechanism accumulates conditional probabilities during the prediction phase and converts the neural network output features into string sequences to successfully solve the problem of temporal text alignment [36]. The most prevalent network models are DTRN [37] and others. However, these recognition methods are ineffective for finding character correlations. In order to obtain more important information, academics have proposed attention-based methods such as single-headed attention, multiheaded attention, and masked multiheaded attention. These methods have a greater improvement in solving intercharacter association information, attentional bias, and fuzzy text recognition. Common networks include MORAN [38] and ASTER [39]. In recent years, a number of academics have also proposed combining CTC and attention to improve the accuracy of text recognition; for example, SCATTER [40] is one such method. The end-to-end text recognition method is able to tightly couple the detection and recognition modules [41], which improves the model volume, detection speed, and recognition accuracy compared to the previous two phases of text recognition. In the text recognition module, text recognition for any angle of rotation is one of the study problems covered in this paper.