esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Results
### Emergency Braking Conditions
From Figure 20a, the stress of the front and rear axles increases first and then decreases from both ends of the axle to the middle, and increases rapidly when approaching the axle support, which means that the maximum stresses on the midline of the upper surface of the front and rear axles are located at \(l=297\) mm and \(l=971\) mm, and the maximum stresses are \(57.25\) MPa and \(58.64\) MPa, respectively. As can be seen from Figure 20b, the maximum stress position of the front and rear crossbeams occurs at the connection position between the longitudinal beams and the crossbeams, and the maximum stress of the rear crossbeam is slightly larger than the maximum stress of the front crossbeam; the maximum stress of the front crossbeam is \(4.06\) MPa and the maximum stress of the rear crossbeam is \(4.42\) MPa. Figure 19: Stress cloud diagram under braking condition. Figure 20: Stress curves of main components under braking condition. (**a**) Axle displacement curve, (**b**) crossbeam displacement curve, and (**c**) longitudinal beam displacement curve. From Figure 20c, it can be seen that the stress change trend under the braking condition is similar to the stress change trend under the bending condition. From the front end of the longitudinal beam to the rear end of the longitudinal beam, the overall trend of stress change in the longitudinal beam first increases and then decreases. The maximum stress of the left longitudinal beam is 18.06 MPa and the maximum stress of the right longitudinal beam is 18.16 MPa. In the three intervals from \(l=633\) mm to \(l=799\) mm, \(l=1357\) mm to \(l=1536\) mm, and \(l=2237\) mm to \(l=2650\) mm, the stresses in the longitudinal beams appear to significantly decrease and then rebound. The stresses in all of the parts of the frame under the braking condition are less than 100 MPa, which is much less than the yield strength limit of each material, and so the strength of the frame under the braking condition meets the design requirements. Based on the above analysis, it can be concluded that the maximum stress and maximum displacement generated by the mobile pump truck under four typical working conditions can meet the requirements of frame strength design. Among them, the maximum stress and maximum displacement under the full load bending condition are 71.76 MPa and 2.11 mm; the maximum stress and maximum displacement under the full load torsion condition are 352.68 MPa and 18.18 mm; the maximum stress and maximum displacement under emergency turning conditions are 79.718 MPa and 2.68 mm; and the maximum stress and maximum displacement under emergency braking conditions are 74.907 MPa and 2.81 mm. It can be analyzed that the safety factor of the frame under four typical working conditions is greater than 2.0, which provides a reference for the design of the subsequent mobile pump truck frame. The safety factors under four typical working conditions are shown in Table 5.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 3 Methods
### SmartSamplingAugment  
The number of sampled augmentations in SmartSamplingAugment, a tuning-free and computationally efficient algorithm, is fixed to two augmentation operations, and the magnitude is sampled randomly from the interval [5; 30] (see Figure 2b and Algorithm 2). These design choices are based on our preliminary experiments and seem to generalize well to unseen datasets. SmartSamplingAugment samples augmentations with a probability derived from the weights, which we set based on an ablation study for image classification on CIFAR10 from RandAugment [12]. In this study [12], the average improvement in performance is computed when a particular augmentation operation is added to a random subset of augmentations. We selected the augmentations with a positive average improvement and transformed this value into probabilities, by which we define the weights.  
In SmartSamplingAugment, we linearly anneal the parameter \(P\), that determines the probability of applying data augmentations, from 0 to 1, increasing the percentage of applying augmentation over the whole training epochs. That way, the model first sees the original data in the early epochs and encounters more variations as the training progresses.  
Figure 2: Comparison of TrivialAugment and SmartSamplingAugment.  
```
Input: Data \(D\), List of augmentations \(A:=[a_{1},a_{2},\dots,a_{-1}]\), Weights \(W:=[w_{a_{1}},w_{a_{2}},\dots,w_{a_{-1}}]\)
1 for each epochdo
2 Update \(P\) ; // \(P\) is linearly annealed
3 for each image \(I\) in \(D\)do
4 Sample var from \([0,1]\)
5 if\(var>P\)then
6 use \(I\) ; // do not augment
7
8 else
9\(A_{W}:=\) random sample 2 ops \(a_{i},a_{j}\in A\) with weights \(w_{i},w_{j}\in W\)
10\(M:=\) random sample magnitude from [5; 28]
11\(I_{AUG}:=\) apply \(A_{W}\) with \(M\) to \(I\)
12 use \(I_{AUG}\)
```  
**Algorithm 2**Pseudocode for SmartSamplingAugment.
## 2 Materials and Methods
### Analysis  
The proportions of CH\({}_{4}\), CO\({}_{2}\), and N\({}_{2}\) in the biogas were determined using a gas chromatograph Model GC-8A (Shimadzu, Japan) equipped with a thermal conductivity detector. The stainless-steel column in the gas chromatograph was packed with Shincarbon ST (Shinwa Chemical Industries, Kyoto, Japan). The pH was determined as soon as the sample was collected, using a pH meter Model HM-25R (TOA-DKK, Tokyo, Japan) equipped with a GST-5721C electrode (TOA-DKK, Tokyo, Japan). Total suspended solids (TSS) was determined according to the sewage test methods of Japan Sewage Works Association. Free LCFAs such as caprylic acid (C8:0), capric acid (C10:0), lauric acid (C12:0), tridecanoic acid (C13:0), myristic acid (C14:0), palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), arachidonic acid (C20:0), cis-11-eicosenoic acid (C20:1), behenic acid (C22:0), and erucic acid (C22:1) were measured as fatty acid methyl esters using an Agilent 6890 gas chromatograph (Agilent J&W, Santa Clara, CA, USA) with a flame ionization detector. A DB-WAX capillary column (Agilent J&W, Santa Clara, CA, USA) was installed in the gas chromatograph. Fatty acid methyl esters for GC analysis were prepared by the HCI/methanol/toluene method optimized for free fatty acids [22]. First 1 mL digestate samples were lyophilized by a freeze dryer Model DC401 (Yamato, Tokyo, Japan) for at least 6 h to completely remove water from the samples. Then the samples were dissolved in 0.2 mL toluene and mixed with 1.8 mL HCl/methanol reagent in a glass vials. The methylation was performed by heating the mixtures in a metal block for 20 min according to the literature [22] and methyl esters were extracted in the hexane phase after cooling at room temperature and addition of 1 mL hexane and 0.2 mL reverse osmosis-purified water. Acid-base titrations and end point (EP) determination were performed usingan automatic titrator 848 Titrino plus (Metrohm, Herisau, Switzerland) equipped with a pH electrode (Ecotrode Plus, Metrohm, Herisau, Switzerland). The titration was carried out by adding 0.1 mol/L NaOH into the mixed solution of 10 mmol/L LCFAs and HNO\({}_{3}\). The blank solution contained only 10 mmol/L HNO\({}_{3}\).
## 3 Numerical Simulation and Its Verification
### CFD Model Validation  
Table 3 shows the CFD simulation data compared with the experimental data [1] under the same working conditions. The performance curves and wall pressure distributions of tests 1, 2, and 3 are shown in Figures 5 and 6. The numerical simulation data were close to the experimental data, except for test 3. The errors between the experimental and  
Figure 4: Structural mesh results of the computational domain.  
Figure 3: Comparison of Mach numbers.  
Table 4 illustrates that there was a good consistency between CFD results in this study and those in [1].
## 4 Simulation Results
### Case I  
In the first case, bus load change pattern is considered. The load at bus #30 is gradually increased by 15% of its initial load level from the time instant 1 min to 2.5 min of the simulation.  
In this test, the voltage magnitude \(|V_{30}|=0.9388\) p.u. of pilot bus #30 drops below the constraint of 0.95 p.u. at \(t=2.5\) min. This low voltage violation activates the proposed ASVC immediately, and Figure 6a illustrates the voltage trace at pilot bus #30 during the simulation test of case I. When the proposed scheme is used to carry out the secondary voltage control, voltage violation will be eliminated effectively. In addition, the value of VSM is increased from 0.51 to 0.72 p.u., as shown in Figure 6b. This demonstrates that larger VSM can be achieved simultaneously with improvement of overall network voltage.
## 3 Materials and Testing Methods
### Testing Methods
#### 3.2.3 Physical Tests  
(1) Penetration test  
Penetration is a grading index of asphalt. For example, if the penetration range is 60-80, the asphalt is defined as PEN70. As shown in Figure 6, a container filled with an asphalt sample is stored in a 25 \({}^{\circ}\)C water bath for 90 min, and then penetrated by a needle weighted 100 g; the penetration depth is measured as a penetration in the unit of 0.1 mm. For the details of penetration test, refer to ASTM D5 [16].  
Figure 5: The Rolling Thin Film Oven.  
Figure 6: The Needle Penetration Tester.  
(2) Soften point test  
The soften point reflects the high-temperature stability of asphalt. The higher the soften point, the better the anti-rutting performance of the asphalt. As shown in Figure 7, two steel balls are placed on the horizontal disks of an asphalt sample contained in vertically supported metal rings. The assembly is heated in a water bath at 5 \({}^{\circ}\)C/min. The softening point was recorded as the average temperature at which the two disks softened enough to allow each ball, enveloped in asphalt, to fall a distance of 25 mm (1.0 in). For the details of soften point test, refer to ASTM D36 [17].  
(3) Ductility test  
Ductility reflects the ductility of asphalt. The higher the ductility, the better performance the asphalt has. The ductility test is used to measure the stretching length of a standard asphalt sample before breaking under standard testing condition (1 cm/min stretching speed at 5 \({}^{\circ}\)C), as shown in Figure 8. For the details of the ductility test, refer to ASTM D113 [18].
## 3 Classification Method
### Application Domain and Field
#### 3.1.1 Medical Context  
EEG emotion recognition is medically contextual if the system is designed to provide assistance, enhancement, monitoring, assessment, and diagnosis of human psychiatric and neurological diseases.
## 3 Multi-Ship Collision Avoidance Decision Making Based on Full Communication
### Intention-Based Ship Collision Avoidance Processing
In this case, each involved ship is responsible for clearing the region ahead of the ship on its starboard side. If there is no other ship on its starboard side, the ship can keep its own speed and heading forward [2; 18]. In this case, the first ship to the starboard direction of the OS is considered to be the priority ship to give way to. For instance, ship 2 is ship 1's priority ship to give way to in Figure 4. In this way, each ship and its priority avoiding ship form a two-ship encounter situation. If there is no more new and urgent collision risk, the OS will sail towards the current path target until it encounters a new priority avoiding ship or completely leaves the encounter region. It should be noted that the choice of path target is an independent choice of each involved ship, whether in a two-ship encounter situation or a multi-ship encounter situation. In the following numerical experiments in Section 4, it can be seen that the different ways of determining path targets will affect the efficiency of the CA process, but they will not affect the safety of the OS or the inference of the CA intention of the OS by other ships. This is because the CA intention itself is vague and at a hidden level, not a specific CA maneuver. According to the CA communication flow design in Figure 1, the intention of CA between ships is the main content of BTC technology communication. Figure 4: The path target determination and two-stage collision avoidance process in a two-ship encounter situation. Figure 5: The path target determination and two stage collision avoidance process in a multi-ship encounter situation.
## 5 Performance Evaluations  
### Performance Evaluation Metrics  
We conducted multiple experiments to analyze the performance of our proposed scheme and the benchmark protocol using the following metrics.  
Figure 4: Cluster head (CH)-friendship.  
1. **CH Lifetime:** The CH lifetime is defined as the amount of time a node can act as a CH. In other words, it is the time of CH until re-clustering.
2. **Re-clustering Frequency:** Re-clustering is the process of electing new CH to avoid CH communication failure. Re-clustering mechanism is executed when the available resources of CH go down than a certain threshold (e.g., the energy of CH). Re-clustering frequency is defined as the frequency of re-electing the CHs during the entire network lifetime.
3. **Number of Control Packets:** The control packets considered as overheads and are defined as the packets used for route establishment from a source to a destination e.g., CH announcement, Join CH and TDMA etc.
4. **Packet Drop Ratio:** Packet drop ratio is defined as the ratio of the number of packets lost (not received at receiving node e.g., CH or Sink node) to the total number of sent packets.
5. **Total Energy Consumption:** The energy consumption of a node mainly depends on two main factors (e.g., packet processing and transmissions or receptions) [37]. The cumulative energy consumption of a node is presented in Equation (13) and is defined as follows. \[E_{node}=E_{proc}+E_{rx}+E_{tx}\] (13) where \(E_{proc}\) is the energy consumed in processing, and \(E_{rx}\) and \(E_{tx}\) represents the energy consumed for receiving and transmitting a data packet, respectively. The total energy consumption of the network is directly proportional to the number of packets transmitted in the network and is defined as follows. \[E_{network}=\sum_{i=1}^{n}\left(E_{Proc_{i}}+E_{rx_{i}}+E_{tx_{i}}\right)\] (14) where \"\(n\)\" represents the total number of packets.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 4 Poly(3,4-ethylenedioxythiophene)-Based Electrodes
### 3D Nanostructures
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Nanostructure** & **Recepur** & **Analyte** & **Mechanism** & **LOD 1** & **Response** & **Working** & **Linearity** & **Cycle** & **Stability** & **Reference** \\ \hline PJFEDOT nanosphere & - & H\({}_{2}\)O\({}_{2}\) & Electrochemical & 2.84 uM & \(<\)10 s & 25 °C & 2.5 uM-mM & 3 weeks & [117] \\ \hline PJFEDOT microsphere & GOK & Glucose & Electrochemical & 1.15 uM & \(<\)10 s & 25 °C & 0.1-10 mM & 12 days & [118] \\ \hline PEDOT/MnO\({}_{2}\) & - & catechol amine & Electrical & 0.25 mM & \(<\)20 s & 25 °C & 0.25-25 mM & - & [119] \\ \hline BPK-CJFEDT5 PS8 & \multirow{2}{*}{GOK} & Glucose & Electrochemical & 2.4 mM & \(<\)20 s & 25 °C & 0.5-5 mM & - & [120] \\ \cline{2-7} EmbeddedPSPSPSPSPS & \multirow{2}{*}{HRP} & H\({}_{2}\)O\({}_{2}\) & Electrochemical & 30 pM & - & 25 °C & 0.1 mM-10 mM & 35 days & [121] \\ \cline{2-7} PEDOT NT & & hDRD1 3 & \multirow{2}{*}{Dopamine} & FET * & 10 mM & \(<\)1 s & 25 °C & 10 pM-10 mM & - & [122] \\ \cline{2-7} FDTD-S+all flowline & & & & & & & & & \\ \cline{2-7} core-sheash wires & & & & & & & & & \\ \hline PJFEDT5 PS8 & \multirow{2}{*}{PLL 1,9-GC} & \multirow{2}{*}{Streptavidin} & \multirow{2}{*}{Electrochemical} & 1.14 uM & \(<\)20 s & 25 °C & 1.14-800 nM & 4 weeks & [123] \\ \cline{2-7} PEDOT NT & & & & & & & & & \\ \cline{2-7} PEDOT NT & & & & & & & & & \\ \hline PEDOT narrowband & Au-HB 7 & H\({}_{2}\)O\({}_{2}\) & Electrochemical & 0.6 uM & \(<\)20 s & 25 °C & 1.9 uM-1.1 mM & 60 days & [124] \\ \hline PEDOT/Bus-based & \multirow{2}{*}{GOK} & \multirow{2}{*}{Choose} & \multirow{2}{*}{Electrochemical} & 41 pM & \(<\)20 s & 25 °C & 0.1-14 mM & - & [127] \\ \cline{2-7} EtherDT & & & & & & & & & \\ \hline PEDOT/Au & Antibody & VEGF * & Electrochemical & 0.5 pg mL-1 & - & 25 °C & 1-20 pg mL-1 & - & [128] \\ \hline Au-PEEDT-Graphene & \multirow{2}{*}{Artibody} & \multirow{2}{*}{PSA *} & \multirow{2}{*}{Electrochemical} & 0.03 pg mL-1 & - & 25 °C & 0.1 pg mL-15-50 mg mL-1 & - & [131] \\ \cline{2-7} Appe & & & & & & & & & \\ \hline
3D PEDOT NT network & \multirow{2}{*}{LDH 10} & \multirow{2}{*}{Latute} & \multirow{2}{*}{Electrochemical} & 0.05 mM & \(<\)10 s & 25 °C & 0.05-1.8 mM & - & [133] \\ \cline{2-7} membrane & & & & & & & & \\ \hline \hline \end{tabular}  
* \({}^{1}\) Limit of detection, 2 polyethylenimine binding with ferrocene, 3 human dopamine receptor nanowoside, 4 field effect transistor, 5 biotin-derivatized poly(L-lysine), 6 grafted oligo-ethylene glycol, 7 hemoglobin, 8 vascular endothelial growth factor, 9 prostate specific antigen, 10 lactate dehydrogenase.  
\end{table} 
Table 3: Comparative sensing performance and limit of detection of PEDOT nanostructure-based biosensors.
# Dynamic Pressure Analysis of Hemispherical Shell Vibrating in Unbounded Compressible Fluid
## 2 Control Equation and Solution
### The Analysis of the Velocity  
Due to the physical boundary conditions, the vibration at the boundary can be expressed as:  
\[u_{s}(t)=D\cdot\sin(\omega t), \tag{17}\]  
where \(D\) represents the amplitude of vibration. So, the first term of the boundary condition can be expressed as Equation (18):  
\[C\cdot[\cos(\omega t+\varphi)\cdot j_{1}(s_{r})+\sin(\omega t+\varphi)\cdot y _{1}(s_{r})]=u_{s}(t), \tag{18}\]  
Afterward, it may be simplified as follows:  
\[C\sqrt{j_{1}^{2}(s_{r})+y_{1}^{2}(s_{r})}\sin(\omega t+\varphi+\phi)=D\cdot \sin(\omega t), \tag{19}\]  
where, \(s_{r}=\frac{\omega}{c}r_{r}\), and \(\tan(\phi)=\frac{j_{1}(s_{r})}{y_{1}(s_{r})}\). Furthermore, it may be obtained that:  
\[C=\frac{D}{\sqrt{j_{1}^{2}(s_{r})+y_{1}^{2}(s_{r})}},\varphi=-\phi=-\arctan[ \frac{j_{1}(s_{r})}{y_{1}(s_{r})}]. \tag{20}\]By the nature of the Bessel function [23; 24], if \(s\) is very large, then there are:  
\[\left\{\begin{array}{l}j_{1}(s)=\frac{\cos(s-\pi)}{s}\\ y_{1}(s)=\frac{\sin(s-\pi)}{s}\end{array}\right., \tag{21}\]  
Finally, the solution can be derived as Equation (22):  
\[u=\frac{c}{\omega}\frac{D}{\sqrt{\hat{f}_{1}^{2}(s_{r})+y_{1}^{2}(s_{r})}} \frac{\cos(\omega t-\frac{\omega}{c}r+\pi-\varphi)}{r}. \tag{22}\]  
Accordingly, it can be concluded that the characteristic of vibration at time \(t_{0}\) will transfer to the position \(r=ct\) with the same frequency between time \(t\), and the amplitude will be decreased with the rate of \(\frac{1}{r}\).
## 4 Mutation-Based Multivariate Time-Series Anomaly Generation in ICS
### Anomaly Generation  
As previously mentioned, we generate anomalies by altering the latent space rather than the actual data space. More specifically, we use AVRAE to map ICS time-series data into the latent space. In particular, the encoder of AVRAE produces hidden states at each timestep while processing the time-series. Additionally, since these hidden states follow a well-known probability distribution (in this study, the standard normal distribution), it is easy for us to predict or control the values of the hidden states.  
We apply mutations to the hidden states produced by the encoder of AVRAE. This approach has two major advantages. First, the hidden states (the latent vectors) effectively encapsulate the correlations among the features of the observations. Therefore, changing only a part of the latent vector affects all the features of the observations. Second, the hidden states from the encoder are used by the decoder's attention layer (cross-attention) for time-series generation. The attention layer learns the relevance between the elements of the hidden state sequences produced by the encoder and the decoder. Thus, if a segment of the hidden state sequence from the encoder is altered, the subsequent data generated by the decoder are also influenced.  
Algorithm 4 outlines the process for generating anomalous ICS time-series proposed in this study. This algorithm is very straightforward. First, the encoder of the trained AVRAE processes \(\mathbf{x}_{1:T}\) to produce the hidden state sequence \(\mathbf{h}^{\epsilon}_{1:T}\). As mentioned in Section 4.1, we assume the standard normal distribution as the true distribution, so each \(\mathbf{h}^{\epsilon}_{i}\) that makes up \(\mathbf{h}^{\epsilon}_{1:T}\) follows the standard normal distribution. Then, one of the three mutation operators \(\{\mathcal{M}_{p},\mathcal{M}_{ctx},\mathcal{M}_{col}\}\) presented in Section 4.2 is randomly selected as \(\mathcal{M}\). Although \(\mathcal{M}\) is chosen randomly in this study, if there is a need to control the type of anomaly to be generated, specifying a particular mutator is allowed. Using \(\mathcal{M}\), we generate the mutated sequence \(\hat{\mathbf{R}}^{\epsilon}_{1:T}\) from \(\mathbf{h}^{\epsilon}_{1:T}\). Finally, the decoder of AVRAE generates the ICS time-series \(\hat{\mathbf{x}}_{1:T}\) from \(\hat{\mathbf{R}}^{\epsilon}_{1:T}\). Since \(\hat{\mathbf{x}}_{1:T}\) is generated from the mutated sequence \(\hat{\mathbf{R}}^{\epsilon}_{1:T^{\prime}}\), it contains anomalies. This generated anomalous ICS time-series not only considers the correlations among the features of the actual data but also reflects the temporal dependencies through the attention layers of AVRAE.
## 3 Multi-Scope State Area (MSSA)
### State Programming
#### 3.3.2 State-Based Packet Processing  
Realizing state-based packet processing necessitates the following steps: get state, compare state value, choose packet processing decision based on comparison result, and update state. According to the preceding content, the state can be obtained by using {type, offset, length}, indicating the position of the state, and the state can be stored or updated by the switch's assignment instruction. Furthermore, there are two ways to compare the state value: (1) use the state as a matching field and (2) compare the state value by using the compare jump instruction.  
The match-action table, as shown in Figure 8, contains two matching fields: the packet destination IPv4 address and the global state \(s_{1}\). When the state \(s_{1}\) is 1, perform the operation of updating \(s_{1}\) to 2 and forwarding the packet from port 0; when the state \(s_{1}\) is 2, perform the operation of updating \(s_{1}\) to 3 and forwarding the packet from port 1. This method implements state programming with simple logic and stable performance, but it takes up too much entry space when the number of states to be compared is large.  
Figure 8: Implement state transitions through match–action table. (**a**) Compare state with matching field; (**b**) Compare state with instructions.  
Figure 7: After the packet matches the entry, the state will only be in the three state areas if it is a matching field, and the state will only be in the four state areas if it is an instruction parameter.  
Figure 8b performs the same function as Figure 8a's match-action table. Figure 8b, on the other hand, compares the state by using the compare jump instruction. The table's matching field only contains the packet's destination IPv4 address, and the entry compares the state and branches based on the state value. This method avoids taking up extra entry space due to state comparison. However, there is a limit to the number of executable instructions in the switch's pipeline. Exercising too many instructions will result in an increase in time overhead. We compared and tested the two state comparison methods in terms of space occupied and impact on forwarding performance in our experiment.