esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calibration and Characterization
### Calibration Examples
The elevation angle has been swept from \(0^{\circ}\) (zenith) to \(70^{\circ}\), in steps of \(10^{\circ}\); In subfigure (**b**) the hot, cold and load calibration points are depicted. The cold calibration points have been obtained using the previous tipping curves procedure. Figure 10: Hot-cold calibration of the C-band V-polarization channel. Subfigure (**a**) shows the calibration procedure for averaged values in the whole bandwidth; whereas (**b**) depicts the \(a\) and \(b\) values obtained for each sub-band.
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.7 Computation Complexity  
Additionally, Figure 13 illustrates the computational complexity of the proposed method compared to MTD and HI. It is observed that as the length of the subfragment increases, the computational complexity of the proposed method exceeds that of the other methods. By comparing the results, it is shown that the proposed method strikes a balance between the detection performance and computational efficiency.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 4 Applications Domains
To solve the lack of attributes for the classification, they look for patterns from the complete data, and then, they estimated the missing values with the closest representative pattern. In the first stage, they used a fuzzy clustering approach that models the noise and uncertainty of the database. During the second stage, they use a Bayesian approach to get the cluster centroids which can be translated as the representative. They used accuracy and sensitivity as their evaluation metrics. They tested their algorithm in a database with 858 patients. The proposed solution can identify the patterns and is able the predict the missing values with an accuracy of 76%  
In 2020, [PERSON] et al. [22] proposed a method that looks in medical databases to predict the risk of developing hypertension; as they mentioned, most medical databases have the class imbalance problem. Their method is divided into four phases: data collection, data preparation, learning model, and data exploration. For the data collection, they used databases from primary health centers. In the preparation phase, they used mean substitution [163] for missing values and interquartile range [164] to detect outliers. The learning model uses SVM with AdaBoost [165]. Finally, for the exploration phase, they used fuzzy-based association mining rule. They used precision, recall, accuracy, and F-measure to evaluate their performance. They compared their results against ANN, IBK, and NaiveBayes. Their results showed an improvement in medical diagnosis, but they said that it needs further work in classification errors. In 2020, [PERSON] et al. [166] proposed an algorithm, called FEPDS, to extract fuzzy emerging patterns in Data Stream environments. The algorithm can be described in three main steps, a recollection, an adaptive learning step, and a learning multi-objective evolutionary algorithm. First, they collected instances by batches and once the batch is collected, the learning method updates the fuzzy patterns model according to the current batch. Then, they used a multi-objective evolutionary learning algorithm that extracts pattern models according to simplicity and reliability. Finally, the algorithm is continuously evaluating the instance against unseen instances to determine the quality of the new information acquired. The algorithm was tested in the profiles of customers of New York City taxis corresponding to the fare amount; it was also included recurrent behaviors such as payment by card on medium or high fares and the daily travel to work of the citizens. They conclude that FEPDS can adapt the model to abrupt changes without losing performance in terms of execution time. The algorithm also has great scalability and performance in terms of memory consumption, allowing its usage in real-world environments.
## 3 EQPD-MAC Protocol Development
### Method
#### 3.1.3 Highest Priority Packet Transmission  
The proposed EQPD-MAC protocol supports multi-priority transmission and employs a technique to minimize the latency of the highest priority packet. The EQPD-MAC has four distinct priority levels for data packets, as shown in Table 3: normal, important, very important, urgent. This is extensible to any number of priority levels.  
Consider a case of two sensor nodes, \(S_{1}\) and \(S_{2}\), that try to send their respective priority data packets to the sink, as shown in Figure 4. In order to send the packets, \(S_{1}\) and \(S_{2}\) must first synchronize with the sink. After that, \(S_{1}\) and \(S_{2}\) perform CCA and transmit Tx-beacon with \(P_{1}\) and \(P_{4}\), respectively. At the sink, the timer is immediately cancelled since \(S_{2}\) has the highest priority packet. The sink sends an Rx-beacon containing the source address of \(S_{2}\). After receiving the Rx-beacon, \(S_{1}\) and \(S_{2}\) will inspect the beacon. Since Rx-beacon contains \(S_{2}\) as the selected sender, \(P_{4}\) packet will be sent to the sink node at frame 1. In the meantime, \(S_{1}\) will be forced to go to sleep and is given another scheduled time at frame 2 to send its \(P_{1}\) packet. After receiving the priority packet from \(S_{2}\), the sink node sends an  
Figure 3: Multiple rounds of data transmission.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Priority** & **Data Packet Category** \\ \hline P\({}_{1}\) & Normal \\ \hline P\({}_{2}\) & Important \\ \hline P\({}_{3}\) & Very Important \\ \hline P\({}_{4}\) & Urgent \\ \hline \hline \end{tabular}
\end{table}
Table 3: Assignment of Priority Packets.
# Dynamic Simulation Model and Performance Optimization of a Pressurized Pulsed Water Jet Device
## 3 Simulation of Dynamic Characteristics of the PPWJ Device
### Simulation Results  
Taking the oil inlet pressure of 8 MPa as an example, the operational state of the device can be obtained by simulation. The displacement curves of the piston and valve core are shown in Figure 5a. The PPWJ is constructed by coupling the piston and the valve core, and they move back and forth at a limited displacement. When the piston backward strokes or is at the end of a stroke, the position of the valve core begins to switch. The volume and maximum displacement of the valve core are lower than those of the piston. The switch is completed in a short time, so the piston displacement curve is approximate to a sawtooth waveform. A temporary pause occurs when the valve core position switch is completed, and the displacement curve is approximate to a square waveform.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Piston outer diameter \(d_{\rm p1}\) (mm) & 22 & Valve core outer diameter \(d_{\rm v2}\) (mm) & 24 \\ Piston outer diameter \(d_{\rm p2}\) (mm) & 53 & Valve core outer diameter \(d_{\rm v3}\) (mm) & 28 \\ Piston outer diameter \(d_{\rm p3}\) (mm) & 60 & Valve core outer diameter \(d_{\rm v4}\) (mm) & 26 \\ Piston outer diameter \(d_{\rm p4}\) (mm) & 60 & Valve core maximum displacement \(h_{\rm v}\) (mm) & 10 \\ Piston maximum displacement \(h_{\rm p}\) (mm) & 35 & Rated flow rate of the pump \(q_{\rm r}\) (L/min) & 30 \\ Nozzle diameter \(d_{\rm n}\) (mm) & 0.45 & Water inlet pressure \(p_{0}\) (MPa) & 0.3 \\ Nozzle discharge coefficient \(C_{\rm d}\) & 0.95 & Hydraulic oil viscosity \(\mu\) (mm\({}^{2}\)/s) & 48 \\ Valve core inner diameter \(d_{\rm v0}\) (mm) & 15 & Hydraulic oil bulk modulus \(K_{\rm e}\) (MPa) & 1200 \\ Valve core outer diameter \(d_{\rm v1}\) (mm) & 24 & Water density \(\rho_{\rm w0}\) (kg/m\({}^{3}\)) & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in the simulation models.  
The velocity of the piston and the pressure of the jet are depicted in Figure 5b. During the backward stroke of the piston, the jet pressure equals the water inlet pressure. The jet pressure is pressurized during the piston stroke, and the increase, stability, and decrease in the jet pressure correspond to the acceleration, constant speed, and deceleration of the piston stroke, respectively. The jet pressure fluctuates periodically between high and low pressures and can provide a stable boosting effect during the uniform piston stroke.
## 6 Data Preprocessing and Standardization
### Handling Elongated Characters and Variants in Colloquial Roman Urdu  
Textual data in informal languages (such as colloquial Roman Urdu) presents unique orthographic and syntactic constraints as compared to their standard counterparts. One of the crucial problems of such data is lexically variant words, which can highly impact the accuracy of classification models and intermediary natural language processing tools and steps [35]. Colloquial Roman Urdu users typically use elongated word variants. Some example instances comprising elongated word variants and their standard Urdu transliterations are shown in Table 3.  
Such words were normalized to avoid Out-of-Vocabulary (OOV) words, reduce word base, eliminate multiple word representations for the same words, and dimensionality reduction of the corpus. The example output of variant normalization is highlighted in Figure 4.  
Figure 3: Tokenization of Roman Urdu text.  
Figure 2: Text preprocessing steps on Colloquial Roman Urdu micro text.
# Study on Failure Mechanism of Mudstone Based on Digital Core and Digital Volume Correlation Method
## 4 Evolution Characteristics of Digital Core Fracture Model
### The Changing Law of Pore Coordination Number  
The pore coordination number represents the pore throat number connected between pores and pores, and the pore coordination number can be directly related to the connectivity of the pore network model. The wide distribution of the coordination number can be understood as the rich development of micro-joints in this area. The coordination number is often used to evaluate the reservoir transport capacity of porous media.  
The pore network model visually shows the change of the pore network at different loading stages (Figure 8). In the initial stage, most of the pores were isolated pores, which were dispersed in the rock. With the loading, the connected throat appeared between the adjacent pores and further expanded into a network. The pore-connected throat initially appeared near the mineral zone and gradually developed into a pore network in the two mineral accumulation areas. In the initial stage, the average coordination value of primary pores in mudstone was close to 0, indicating that the connectivity of primary pores was poor. At the first loading stage, the average coordination value of pores did not change much from that of the previous stage, and the pore compaction stage in the corresponding stress-strain curve was basically consistent with the pore change characteristics of this stage. At the second loading stage, the increase in the average coordination number of pores corresponded to the pore connectivity in the 3D pore network model. At the stage before the peak stress of loading failure, the average coordination number of pores increased rapidly, which may have been due to the fact that the pores in this stage had developed to the critical state of failure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Loading Phase** & \begin{tabular}{c} **Fracture** \\ **Volume (mm\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Pores** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Throat** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Macro Cracks** \\ \end{tabular} \\ \hline Initial stage & 0.804 & 805 & 2 & 0 \\ The first stage & 1.035 & 810 & 26 & 0 \\ The second stage & 2.32 & 1286 & 264 & 2 \\ The third phase & 180.75 & - & - & 5 \\ The fourth stage & 210.56 & - & - & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fracture development parameters of mudstone at different loading stages.  
Figure 7: Complete failure result of mudstone and 3D image of fractures.
## 5 Conclusions  
The efficient prediction of the full-field residual stress from a limited number of measurement points is still a challenging task. However, by replacing the FEMU technique in the conventional inverse method with a CNN, a novel inverse method is proposed in this paper to determine the residual stress field of structural components. The main conclusions are as follows:  
1. The machining process and conditions of structural components need not be known and the full-field residual stress satisfying mechanical constrains can be inversely determined from limited measurement points.
2. In the proposed method, the U-Net architecture trained by the temperature and stress fields exhibited superior performance in predicting residual stress field and greatly improved the computational efficiency. In fact, the residual stress determined by this method reached an accuracy close to that of the X-ray diffraction method.
3. Moreover, the proposed inverse method based on neural networks is not only suitable for the residual stress prediction but can also undertake inverse identification of various material parameters such as damage factors.  
Conceptualization, G.L.; methodology, T.X., L.W. and X.G.; formal analysis, T.X.; investigation, T.X., L.W. and X.G.; writing--original draft preparation, T.X.; writing--review and editing, G.L.; supervision, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Values** & **0.2** & **0.5** & **0.8** \\ \hline RMSE (MPa) & 7.82 & 7.45 & 7.61 \\ Iteration times & \(2.5\times 10^{6}\) & \(10^{6}\) & \(2.3\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE and iterations of the inverse algorithm under different initial values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Procedure** & \multicolumn{2}{c}{**Time**} \\ \cline{2-3}  & **CNN-Based** & **FEMU-Based** \\ \hline Each iteration & 0.08 s & 21 s \\ Training & 22.91 h & 0 \\ Sampling & 59.85 h & 0 \\ Total & 82.76 h & 5833.33 h (estimated) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of running time between CNN- and FEMU-based methods.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant number: U1837602, 11872115).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw/processed data required to reproduce these findings cannot be shared at this time, as the data also form part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Substrate Specificity of LipGt6  
The substrate specificity of LipGt6 was determined by using \(p\)NP esters of different chain lengths as substrates. Recombinant LipGt6 exhibited the highest activity towards medium- and long-chain fatty acids, followed by \(p\)NP-C8, \(p\)NP-C10, \(p\)NP-C12, and \(p\)NP-C14, and some degradation activity towards \(p\)NP-C16 and \(p\)NP-C18; therefore, LipGt6 is a lipase, not an esterase (Figure 3). The purified enzyme LipGt6 exhibited a specific enzyme activity of 10.87 U/mg using \(p\)NPC8 substrate. After purification treatment, the enzyme showed 3.59-time purification.  
Figure 4: Effects of pH and temperature on the activity of recombinant LipGt6. (**a**) Effects of pH on the activity of LipGt6 using \(p\)NP-C8 as a substrate. (**b**) Effects of temperature on the activity of LipGt6 using \(p\)NP-C8 as a substrate. (**c**) Effect of pH on the stability of LipGt6 using \(p\)NP-C8 as a substrate. (**d**) Effects of temperature on the stability of LipGt6 using \(p\)NP-C8 as a substrate. The data points in Figure 4 are the average of triplicate experiments, and the error bars represent the standard deviation.  
Figure 3: Substrate specificity of recombinant LipGt6. Reactions were incubated at 50 \({}^{\circ}\)C with 50 mM Tris-HCl buffer, pH 9.0. C2, \(p\)NP-C2; C4, \(p\)NP-C4; C8, \(p\)NP-C8; C10, \(p\)NP-C10; C12, \(p\)NP-C12; C14, \(p\)NP-C14; C16, \(p\)NP-C16; C18, \(p\)NP-C18. Data points are the average of triplicate experiments, and the bars indicate the standard deviations.  
Figure 2: SDS-PAGE analysis of recombinant LipGt6. Lane M: standard protein molecular mass markers; lane 1: crude enzyme extract; lanes 2: flow-through fraction; lanes 3, 4, 5, and 6: elution fractions with 5, 10, 50, and 100 mM imidazole, respectively.
# Dynamic Modeling and Output Characteristics Analysis of the Hub-Drive Reduction System
## 5 Full-System Load Test and Validation of the Hub Drive System  
In the described dynamic modeling of the hub drive system, the precise modeling of the gear meshing contacts was achieved through the definition of contact stiffness and friction parameters. To validate the reliability and accuracy of the simulation model under real-world conditions, a bench experiment was conducted to analyze the vibration characteristics of the high-torque hub drive system under load. The experimental results were then compared with the simulation outcomes to verify the correctness of the dynamic model.
# Cross-Layer Methods for Ad Hoc Networks--Review and Classification
## 3 Cross-Layer Method Publications Review
### One Layer
#### 3.1.1 Physical Layer  
Cross-layer methods that are focused on the physical layer use multi-element antenna systems to optimize the antenna beamforming and space-time coding for the minimization of interference between users and the maximization of bandwidth (Table 2). These methods use the data about neighboring nodes from the link layer and the data about data flows from the network layer.  
[PERSON] et al. [45] consider the problems of the development of the channel layer protocol for ad hoc networks with nodes with multiple antenna elements with MIMO technology. Network nodes are capable of forming directional antenna patterns, interference rejection, and MIMO space-time coding of signals. For single omnidirectional antennas, nodes send a transmission request packet, and the target node responds with a permission packet. All other nodes that have accepted the permission and request packet cease to use the radio channel for the time required to transfer the packet between the two nodes. If the receiving node can use a radio channel, the node allows the packet to receive. In the case of multiple antennas with independent transceivers, the node can receive multiple packets at the same time under sufficient spatial separation of senders; when transmitting packets, it can beamform in a way that reduces interference. As a result, requests for transmission packets are advisory in nature and are used to assess the needs of data streams. Therefore, the authors conclude that the interaction of physical and channel layers is necessary.  
[PERSON] et al. [46] propose a way to maximize bandwidth by using MIMO. The work uses the interaction of physical, channel, and network layers. Based on the knowledge of the amount of data streams passing through nodes, each node communicates with adjacent nodes to derive distributed complex coefficients of antenna elements for joint nullification of antenna directional patterns and simultaneous acceptance of packet transfer from several nodes depending on the data flows to be transmitted to nodes.
## 4 Results and Discussion
### Knowledge of RPs about RL of Medicines  
The data regarding the answers given by the RPs to questions concerning their knowledge of RL, demonstrated that of the total number of pharmacists, 35% have partial knowledge of postconsumer reverse logistics of their place of work and approximately 70% of the RPs said that they have partial knowledge of the harm caused to the environment by the irregular disposal of medicines. Only 16.8% claimed to have complete knowledge of the hazards of medicinal waste in the environment. However, approximately 40%-50% of interviewees had partial knowledge of NSWP, RL, and the management of postconsumer medicines, as demonstrated in Table 2.  
However, the present study revealed that RPs in the municipality of Sao Paulo still do not know their role in a postconsumer RL system or how to act as an advisor in this matter, in order to meet the obligations of the NSWP. In relation to Question 9 (\"Do you have any suggestions for methods of collaboration with RL of postconsumer drugs in ways that support the client?\") and Question 10 (\"What are your personal observations about postconsumer RL?\"), the results were grouped based on the similarities in the open responses (Figure 4). About 25% of the respondents agreed that the RL depends on the involvement of the upper management of the establishment, where they operate, while 10% reported that there should be an educational plan for clients and 50% state that there should be incentives for and disclosure to the consumers themselves. It is noteworthy that only 4% agree that the responsibility lies with themselves in their orientation to consumers, which indicates that it is necessary to increase the knowledge of the RL. Comparing these results in the Sao Paulo city to the literature data about the knowledge or the challenges in the RL of postconsumer drugs, the topic tends to be observed by the pharmaceutical professional, as a punctual or individualized form, according to institutions in Brazil. The Hospital of the Clinics, which is part of the Faculty of Medicine of the University of Sao Paulo (Brazil), has a program called the Safe Return of Medicines, where medicines are received and evaluated by pharmacists. If the medicines are in perfect condition, they are dispensed to other patients [33]. The Brazilian pharmacists in this present study (10%), pointed that is important to have an educational plan for customers. As an example of the Food and Drug Administration (FDA), that advises that if a drug withdrawal program is not available, in consideration of the impact on the environment and human health, it is recommended that some medications be disposed of in a sink or domestic bathroom. The active ingredients in the list, published by the FDA in 2017, that presented low