esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Physical Similarity Models
### The Fabrication of Physical Similarity Models  
By conducting indoor physical simulation experiments on the hydraulic fracturing of horizontal coal seam roof boreholes, the study investigated the propagation laws and layer-penetrating mechanisms of hydraulic fracturing in coal rock layers. The No. 13-1 coal seam in the Huainan Mining Area is often accompanied by a layer of mudstone or sandy mudstone pseudo roof, with a thickness generally ranging from 0.5 to 2.0 m. Therefore, the physical similarity simulation experiments in this study considered the influence of mudstone interlayers on fracture propagation and conducted two sets of triaxial confinement physical simulation experiments with different thicknesses of mudstone pseudo roof. The schematic diagram of the physical simulation experiment plan is shown in Figure 10a, where the horizontal wellbore was set inside the model roof, 40 mm vertically above the coal seam, parallel to the coal rock interface and the direction of minimum horizontal principal stress, with a vertical downward perforation of 1 cm at the front end of the wellbore. To simulate the real effect of strata fracturing, a physical model based on the \"roof-mudstone pseudo roof-coal seam\" bonding with rock mechanics strength was established, with the dimensions of 300 mm \(\times\) 300 mm \(\times\) 300 mm. The coal seam thickness in the physical model was kept constant at 8 cm, while the thicknesses of the mudstone pseudo roof in the two groups of experiments were 4 cm and 6 cm, respectively, corresponding to the roof thicknesses of 18 cm and 16 cm. Acoustic emission monitoring devices were embedded in the roof as shown in Figure 10b. Coal seam and roof rock samples from the Huainan Mining Area were collected, and the mechanical properties of the rocks under true triaxial conditions were tested. Based on the mechanical parameters of the coal and rock samples, and following the principle of similarity, the reasonable proportions of similar materials were determined to prepare the coal-rock composite fracturing specimens. The optimal formulation for the roof rock layer was determined based on the following mechanical parameters of the coal seam and roof: the coal sample was selected to be consistent with the parameters of the target coal seam; mudstone pseudo roof material was prepared by mixing cement and quartz sand in a 1:1 ratio with water (using sand with a grain size of 70 to 100 mesh); and the roof material was prepared by mixing cement and quartz sand in a 1:1.5 ratio with water (using sand with a grain size of 40 to 70 mesh). The measured values of the rock parameters are shown in Table 3.  
Figure 10: Hydraulic fracturing physical simulation model, (**a**) schematic diagram of physical simulation, (**b**) physical simulation model image.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
The raw GC-MS data Agilent files (D), acquired in scan mode, were converted to mzML format using the MSC Convert (ProteoWizard Palo Alto, CA, USA) software. Peakdetection, isotopic peak grouping, peak list alignment, and gap-filling were conducted using MZmine 2.5 [14]. The processed data were exported to a csv. file for further analysis. Data were median normalized, auto-scaled and subjected to multivariate analysis using the MetaboAnalyst 5.0 [15]. Statistical significance (\(p<0.5\)) was assessed using the SPSS software (v. 23, BM SPSS Statistics, IBM Corp., Chicago, IL, USA). The results were expressed as a mean \(\pm\) standard error (SE) of three to four replicates. MS Excel was used for regression analysis and data visualization.
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 2 Basic Theory of SAPS
### Basic Equations
\begin{array}{l}u_{ci}=u_{ci}(z)e^{j2\pi ft}\\ \phi_{i}=\phi_{i}(z)e^{j2\pi ft}\\ F_{ci}=F_{ci}(z)e^{j2\pi ft}\end{array} \right.\quad i=1,\;2,\;\cdots,\;n \tag{7}\]  
where \(F_{ci}(z)\), \(u_{ci}(z)\), and \(\phi_{i}(z)\) are the amplitude of the normal force, displacement, and electric potential at the \(i\)th composite layer, respectively. Combing Equation (1) and Equations (5)-(7) yields  
\[\left\{ \begin{array}{l}u_{ci}(z)=A_{ci}\sin(k_{c}z)+B_{ci}\cos(k_{c}z)\\ \phi_{i}(z)=\chi_{2}[A_{ci}\sin(k_{c}z)+B_{ci}\cos(k_{c}z)+C_{ci}z+D_{ci}]\\ F_{ci}(z)=\chi_{0}k_{c}[A_{ci}\cos(k_{c}z)-B_{ci}\sin(k_{c}z)]+\chi_{3}S_{\rm part }C_{ci}\end{array} \right.\quad i=1,\;2,\;\cdots,\;n \tag{8}\]  
where \(k_{c}^{2}=4\pi^{2}\chi_{1}f^{2}/\chi_{0}\); and \(A_{ci}\), \(B_{ci}\), \(C_{ci}\), and \(D_{ci}\) are constants related to the boundary conditions of SAPS. Additionally, the similar expressions for displacement \(u_{li}(z)\) and normal force \(F_{li}(z)\) of the composite of the copper lid (lid I or lid II) and protective shell (as shown in Figure 4) are  
\[\left\{ \begin{array}{l}u_{li}(z)=A_{li}\sin(k_{l}z)+B_{li}\cos(k_{l}z)\\ F_{li}(z)=\chi_{4}k_{I}[A_{li}\cos(k_{l}z)-B_{li}\sin(k_{l}z)]\end{array} \right. \quad i=1,2, \tag{9}\]  
where \(\chi_{4}=C_{33l}S_{l}+C_{33s}S_{s}\), \(\chi_{5}=\rho_{i}S_{l}+\rho_{s}S_{s}\), \(k_{l}^{2}=4\pi^{2}f^{2}\chi_{5}/\chi_{4}\), \(S_{l}=\pi d_{in}^{2}/4\), \(C_{33e}\) and \(\rho_{e}\) are the elastic moduli and density of the copper lids, and \(A_{li}\) and \(B_{li}\) are the constants related to the boundary conditions. Equations (8) and (9) indicate that there is a total of \(4+4n\) constants, and we obtain \(4+4n\) equations to solve these constants by using mechanical and electric boundary conditions of SAPS in the next two sections. Figure 4: The composite layer of the protective shell and copper lid.
## 3 Proposed Circuit Implementation
### Class-AB Output and Drive Circuit  
The gain of Class-AB is  
\[G_{AB} =(g_{mn}\vartheta_{gsn}-g_{mp}\vartheta_{gsp})\cdot R_{out}\] \[=(g_{mn}+g_{mp})\cdot R_{out}\vartheta_{in} \tag{6}\]  
where  
\[g_{mn}=K_{n}\frac{W}{L}(V_{GSn}-V_{THn}) \tag{7}\]  
\[g_{mp}=K_{p}\frac{W}{L}(VDD-V_{AB}-V_{GSn}-V_{THp}) \tag{8}\]  
In order to maintain a fixed voltage difference between the NMOS and PMOS gates, a differential difference amplifier (DDA) and MN11 and MN12 are designed in the circuit to form a Class-AB drive circuit [43].  
Compared with the fully differential op amp, the differential op amp adds a pair of differential pairs, which can realize such operations.  
\[V_{OP}-V_{ON}=A[(V_{PP}-V_{PN})-(V_{NP}-V_{NN})] \tag{9}\]  
The differential operational amplifier is connected to transistors MN11 and MN12 to form a regulated cascode structure. The difference between the drain voltages of MN11 and MN12, detected by the DDA, is compared to a reference voltage and output to the gate of the transistors, forming a negative feedback loop that stabilizes the drain DC voltage. This negative feedback also reduces the gain of the common-gate stage involving MN11 and MN12, which in turn reduces the overall gain of the op amp.  
In the circuit, \(g_{\text{m2}}\) and \(g_{\text{m3}}\) constitute a two-stage op amp. Miller compensation will produce a right-half-plane zero due to the existence of a feedforward signal path, reducing the phase margin and response speed of the op amp [44]. Cascode compensation does not have a feedforward path, and the response speed is greatly increased, but cascode compensation will produce a complex plane pole. When the capacitance is large, a gain spike may be generated at the pole position, reducing the gain margin of the op amp. This design uses cascode compensation and Miller compensation at the same time, which weakens the influence of the feedforward signal path of Miller compensation and increases the response speed of the op amp.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 4 Analysis of Experimental Results  
### Experimental Scene Construction and Parameter Setting  
In this section, to verify the performance of the DL-based CSK-SS communication system, the demodulation of the actual signal is tested in the water tank experiment. The experiment also consists of two stages: offline training and online test. The size of the water tank is 45 m (length) \(\ast\) 6 m (width) \(\ast\) 5 m (depth). There are anechoic tiles installed on the walls on both sides of the water tank, and the bottom of the water tank is covered with smooth tiles, which will make the sound waves reach the receiving end of the system after multiple reflections from the bottom and the water surface, thereby simulating the propagation process of the signal in the shallow water channel. Figure 15 shows the scene construction of the experiment, in which A represents the position of the transmitting transducer, B1, B2, B3, and B4 represent the position of the receiving hydrophone in the offline training stage, and C represents the position of the receiving hydrophone in the online test stage. In the experimental equipment, the power amplifier (PA) is B&K2713 [33], the operating frequency band of the transmitting transducer is 8-16 kHz, the attenuator is BEHRINGER DI-100 [34], and the receiving hydrophone is ST300 HF [35].  
Figure 14: BER curve with mismatches between training and test stages.  
Figure 15: Layout of the water tank experiment scene.  
The transmitted signal used in the offline training stage is composed of several data packets. The signal in each data packet is composed of an HFM signal and communication signal, in which the communication signal is the known source information modulated by CSK-SS. After being transmitted by the transducer, the hydrophone receives the signal after passing through the water tank acoustic channel. Finally, the received signal is processed as the training dataset of the neural network model to complete the training of the neural network model.  
In the online test stage, the hydrophone receives the transmitted signal and is directly input into the trained neural network model after resampling to complete the signal demodulation.  
The analysis of the simulation results shows that in the DL-based CSK-SS UWA communication system, the BiLSTM network has better performance than the LSTM network. Therefore, the neural network model used in the water tank experiment is BiLSTM and the parameter settings in the neural network are consistent with those in the simulation.  
The primary parameter setting and data structure of the transmitted signal are shown in Tables 2 and 3.
## 3 Results and Discussion
### Influence of Formulation Factors on the In Vitro Availability of Felodipine in Experimental Tablets  
_Type and percentage of matrix-forming polymer_  
The obtained results are shown in Figure 9a for 1% LSNa dissolution medium and Figure 9b for 0.4% CTAB dissolution medium. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ingredient**} & \multicolumn{2}{c}{**Density (g/mL)**} & \multicolumn{2}{c}{**Flowability and Compressibility Indexes**} \\ \cline{2-5}  & **Bulk** & **Tapped** & **CI** & **HR** \\ \hline macrocrystalline felodipine & 0.7461 & 0.8318 & 11.49 & 1.11 \\ microcrystalline felodipine & 0.2882 & 0.3440 & 19.36 & 1.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Volumetric characteristics of felodipine powders. Figure 8: Bulk and tapped densities of felodipine powders. Figure 9: (a) Felodipine release rate in 1% LSNa medium; (b) Felodipine release rate in 0.4% CTAB medium. The mean and standard deviation for \(n=6\) samples were calculated and the similarity factor (\(f_{1}\)) and difference factor (\(f_{2}\)) were determined (Table 11a,b). Tablets containing 15% HPMC K 100 lead to 94% feloipine dissolution in two hours on 1% LSNa medium, the active ingredient being almost completely released during this period. In the same medium, F2, with a concentration of 30% HPMC K100, showed 8.70% feloipine dissolution in two hours and 77.50% feloipine dissolution in 12 h, compared to dissolution rates of 8.35% and 87.08% after 2 h and 12 h, respectively, for the reference product. In the 0.4% CTAB aqueous solution, F1 had a dissolution rate of 6.00% at 2 h and 88.8% at 12 h, which are better values compared to those registered in LSNa medium. For F2 the feloipine dissolution was 9.16% at 2 h and 45.30% at 12 h, displaying a slight difference compared to 5.57% and 59.43% for the reference product. Difference and similarity factor values reveal that F1 did not exhibit a dissolution behaviour similar to the reference product in the two media under study. Meantime, F2 showed a release profile with a similarity factor of more than 50 in the CTAB medium and with statistically similar values in the LSNa medium, having a difference factor of 12.53 and a similarity factor of 58.87 (Table 11b). Increasing the amount of the cellulose polymer HPMC K100, which possesses the highest viscosity degree, in the composition of the experimental formulations lead to a delay in the release of feloipine correlating with the added percentage in all investigated dissolution media. The feloipine release was significantly higher in LSNa medium than in CTAB for all studied formulations and also for the reference product. Experimental formulation F2,
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 4 Performance Evaluations  
We evaluated the performance of the proposed ESACC scheme through extensive simulation tests. Assume a UASN contains preset numbers of nodes and events. Each node is static after a random or scheduled deployment, and the event may be affected by the flow of water. We verified the convergence of ESACC and display the experimental results.
## 2 Experimental Procedures and Research Methods
### Design of Experiments  
It is well known that the selection of laser cladding process parameters has a direct impact on the forming quality and performance of the ceramic coating [33; 34; 35], among which laser power, spot diameter, overlapping ratio, and scanning velocity have a significant impact. Therefore, laser power (\(L\)), laser spot diameter (\(D\)), overlapping ratio (\(S\)), and scanning velocity (\(V\)) were selected as experimental factors, and each experimental factor was set at 3 levels. According to the mainly process characteristics of laser cladding, some preliminary experiments of coating preparation using laser cladding were conducted. Based on the preliminary experimental results, three reasonable experimental parameters were selected for each process factor, as shown in Table 1. Table 2 is the \(L27\)-(\(3^{13}\)) orthogonal table designed using the Taguchi method, for the experiments of Ti(C, N) ceramic coating prepared via laser cladding. The laser cladding system used in the experiments is shown in Figure 2, which was described in detail in our previous paper [7].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Process Parameters**} & \multirow{2}{*}{**Notations**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**Level**} \\ \cline{3-5}  & & & **1** & **2** & **3** \\ \hline Laser power & \(L\) & W & 400 & 450 & 500 \\ Spot diameter & \(D\) & mm & 1 & 1.5 & 2 \\ Overlapping ratio & \(S\) & \% & 20 & 30 & 40 \\ Scanning velocity & \(V\) & mm/s & 5 & 7 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental factors and levels.  
Figure 1: The micro morphology image of pre-placed cladding powder: (**a**) TiCN; (**b**) SiO\({}_{2}\).  
The micro-hardness of the coating is an important index to evaluate the wear resistance of the material. Therefore, the micro-hardness of the coating is taken as the response of the experiment. The micro-hardness measurement of the coating is shown in Figure 3. The micro-hardness value (HV\({}_{0.2}\)) of the cross-section of the coating was measured using an automatic Vickers micro-hardness tester (HVS-1000Z, Vegour, Shanghai, China). In the transverse direction of the cross-section, the coating was divided into 6 sections on average, and the micro-hardness value was measured once on each section of the coating, a total of 6 times, and the arithmetic mean value was taken as the micro-hardness value of the coating.  
Figure 3: Schematic diagram of the measurement of coating micro-hardness.  
Figure 2: Schematic diagram of the laser cladding system.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 4 Working of Algorithm
However, this will not increase channel utilization if \(N<D\_{slots}\) in the TDMA frame. When we increase \(S_{d}\), the overall size of the TDMA frame \(T\) likewise increases. We observed that with large data sizes, \(M\) number of slots are not enough, as each node is assigned multiple data slots. Therefore, to analyze the impact on \(TH_{network}\), we increase the number of data slots and set it to \(N\), i.e., \(D\_{slots}=N\), where \(N=30\). With an increase in \(D\_{slots}\), the TDMA frame's size increases to \(858\) ms, and \(TH_{network}\) decreases to almost half for small data sizes, as depicted in Figure 21. This happens because, for a small data size, most slots are wasted, whereas with large data sizes, more nodes get the slots to transmit data but merely impact \(TH_{network}\) as the number of transmitting nodes on each frequency is not equal, and channel capacity goes undermining on few frequency channels. Figure 21: Network throughput with different number of sizes of data slots for \(N=30\). Figure 20: Network throughput of proposed design with different data types for \(N=30\). To deal with the issue, we can repeat calculated time slot vectors TSVs of each sub-net on the selected frequency channel. It will increase \(TH_{network}\) and channel utilization for small values of \(R_{t}\). ## 7 Comparison of VSCL-MAC with Other Cross-Layer Protocols

In this section, we present the qualitative and quantitative comparison of proposed VSCL-MAC protocol with other cross-layer and MAC protocols. ### Qualitative Comparison

All previously proposed cross-layer designs discussed in Section 2 are missing the realistic behavior of the tactical environment and specify our proposed design's novelty. The investigation shows that none of the techniques provide complete autonomous and distributed design, which works in multichannel tactical network settings. Consequently, it is impossible to mark fair quantitative comparison with other cross-layer designs, as all utilize PHY or network layer support to enable collision-free multihop communication. However, we performed a qualitative comparison with deference to the expedient of undertaking ad hoc tactical networks in terms of control overhead for route discovery and set collision-free simultaneous data transmissions up, described in Table 12.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Misdirection Attack  
In a misdirection attack, the malicious attacker forwards packets to incorrect paths by modifying routes or misdirecting packets to a malicious node. As illustrated in Figure 5a, the normal route should be from Node1 to Node4, but the malicious attacker (M) modified the route to Node6.  
This attack can be protected by amending the route path which consists of source-routes in each packet. Authorization, egress filtering, trust-aware routing, and monitoring of routes are feasible techniques for defending against a misdirection attack. In [37], a defense mechanism is proposed for the detection and isolation of the misdirection attack in WSNs. The proposed technique is based on node localization, in which delay per hop is counted. The node that is increasing delay maximum times would be detected as a malicious node. Unfortunately, it is hard to get nodes' precise positions and the distances between nodes in the underwater environment, which means that the mechanism is not suitable for UWSNs.
###### Abstract  
This paper presents a compact design for a four-element multiple-input multiple-output (MIMO) antenna for millimeter-wave (mmWave) communications covering the bands of n257/n258/n261. The MIMO design covers the frequency range of 24.25-29.5 GHz, with a wide bandwidth of 5.25 GHz. The element of the MIMO antenna structure uses a single circular patch with an inset feed, and, in order to improve the reflection coefficient (S\({}_{11}\)), a half-disk parasitic patch is positioned on top of the circular patch. Moreover, to fine-tune the antenna's characteristics, two vertical stubs on the extreme ends of the ground plane are introduced. For this design, a Rogers RT/Duroid 5880 substrate with ultra-thin thickness is used. After the optimization of the design, the four-port MIMO antenna attained a tiny size, with the dimensions 16.2 mm \(\times\) 16.2 mm \(\times\) 0.254 mm. In terms of the MIMO parameters, the ECC (Envelopor Correlation coefficient) is less than 0.002 and the DG (Diversity Gain) is greater than 9.99 dB in the mentioned band, which are within the tolerance limits. Also, in spite of the very small size and the four-port configuration, the achieved isolation between the neighboring MIMO elements is less than \(-\)23.5 dB.  
n257; n258; 261 band; ISM 24 GHz; mmWave; inset fed antennas; MIMO 1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Compact Ultra-Wideband Millimeter-Wave Four-Port Multiple-Input Multiple-Output Antennas for 5G Internet of Things Applications. _Sensors_**2024**, 24, 7153. [[https://doi.org/10.3390/s424227153](https://doi.org/10.3390/s424227153)]([https://doi.org/10.3390/s424227153](https://doi.org/10.3390/s424227153))
## 4 Results
Meanwhile, the state of Rio Grande do Sul is the best-positioned in the multidimensional depression ranking. This state stands out mainly in terms of the factors of the level of education, having someone's support, performing unhealthy work, and being male. The results in Table 8 suggest that public policies aimed at preventing and treating depression should be directed towards the states of the northeastern region, especially in terms of actions that promote education, as well as the prevention and treatment of chronic diseases. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**State** & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\delta\) & \(\varepsilon\) & **CI** \\ \hline Acre & 14 & 16 & 13 & 13 & 9 & 2 \\ Alagoas & 2 & 25 & 26 & 26 & 27 & 1 \\ Amapá & 23 & 7 & 1 & 24 & 21 & 13 \\ Amazonas & 21 & 14 & 14 & 7 & 8 & 20 \\ Bahia & 16 & 20 & 7 & 3 & 14 & 5 \\ Ceará & 3 & 26 & 17 & 21 & 25 & 21 \\ Distrito Federal & 27 & 1 & 11 & 20 & 17 & 15 \\ Espirito Santo & 22 & 5 & 18 & 8 & 4 & 17 \\ Goias & 5 & 12 & 15 & 11 & 11 & 9 \\ Maranhão & 9 & 27 & 6 & 23 & 18 & 26 \\ Mato Grosso & 26 & 9 & 22 & 17 & 13 & 19 \\ Mato Grosso do Sul & 18 & 11 & 12 & 2 & 16 & 4 \\ Minas Gerais & 19 & 13 & 8 & 6 & 7 & 23 \\ Pari & 15 & 18 & 19 & 18 & 22 & 25 \\ Paraiba & 4 & 22 & 16 & 25 & 23 & 10 \\ Parana & 13 & 10 & 9 & 9 & 6 & 22 \\ Pernambuco & 17 & 19 & 25 & 13 & 20 & 7 \\ Piauf & 7 & 23 & 5 & 19 & 12 & 8 \\ Rio de Janeiro & 12 & 2 & 27 & 27 & 26 & 11 \\ Rio Grande do Norte & 6 & 21 & 20 & 12 & 24 & 6 \\ Rio Grande do Sul & 11 & 6 & 3 & 5 & 1 & 27 \\ Rondonia & 10 & 17 & 23 & 1 & 2 & 18 \\ Roraima & 25 & 8 & 24 & 10 & 10 & 3 \\ Santa Catarina & 20 & 4 & 2 & 22 & 3 & 24 \\ Sao Paulo & 24 & 3 & 10 & 14 & 19 & 16 \\ Sergipe & 1 & 24 & 21 & 4 & 15 & 14 \\ Tocantins & 8 & 15 & 4 & 16 & 5 & 12 \\ \hline \hline \end{tabular} Note: \(\alpha\): Having a limiting chronic illness, \(\beta\): Education, \(\gamma\): Having someone’s support, \(\delta\): Performing unhealthy work, and \(\varepsilon\): Male gender.  
\end{table} 
Table 7: Rankings of the multidimensional depression diagnostic index (CI) and its respective determining factors per state. In general, the multidimensional depression diagnostic index provides policymakers with information to guide decisions about which states should receive investments in care and assistance networks, as well as for educational campaigns. In this context, the index is more concerned with providing information for public policies than with classifying or indicating an individual's depression diagnosis.