esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Anti-Interference Performance of Substrate-Free PEDOT:PSS ECG Electrodes
## 3 Results and Discussion
### Effect of Static Factors on Impedance and ECG
#### 3.1.2 Effect of Electrode Area  
Here, electrodes of different sizes are attached to the skin as full conformal contact electrodes. As shown in the equivalent circuit model shown in Figure 4, the electrode size mainly affects the interface resistance \(R_{cs}\), the interface capacitance \(C_{cs}\), the electrode resistance \(R_{el}\), and the electrode capacitance \(C_{el}\). The smaller the area, the larger the resistance \(R_{cs}\), the smaller the \(C_{cs}\), the larger the electrode resistance \(R_{el}\), and the smaller the electrode capacitance \(C_{el}\). Therefore, the smaller the electrode area is, the larger the skin-electrode impedance is.  
Figure 6 shows the impedance and ECG signal results for different sizes of electrodes. From Figure 6a, it could be seen that the skin-electrode impedance decreased as the electrode area increased, consistent with the results analyzed by the equivalent circuit model. The spectrogram in Figure 6b reveals that the ECG amplitudes for different electrode sizes were similar, with no significant differences. The original ECG waveforms in Figure 6c also demonstrate that ECG waveforms from different electrode sizes exhibited complete waveform characteristics, and the waveform features were consistent across different sizes.  
The SNRs were 4.0447 \(\pm\) 0.4616 dB, 3.9115 \(\pm\) 0.5885 dB, and 4.1556 \(\pm\) 0.5557 dB for round electrodes with diameters of 30 mm, 20 mm, and 10 mm, respectively. Analyzing these SNR results in comparison to previous studies offers valuable insights. Previous research by [PERSON] [23] and [PERSON] [31] investigated the impact of electrode size on ECG signal quality, with varying conclusions. [PERSON]'s microneedle electrode study suggested that electrode size had a minimal effect on ECG signal quality, while [PERSON]'s fabric electrode study found that decreasing electrode size led to decreased ECG signal quality. In this  
Figure 5: Results of different contact states (**a**) Bode plot of skin-electrode impedance; (**b**) Spectrogram of different contact states; (**c**) ECG of different contact states.  
study, a similar trend to [PERSON]'s microneedle electrode findings was observed. Despite the differences in electrode size, the SNR values remained relatively consistent among the electrodes. This suggests that for the full conformal contact substrate-free PEDOT:PSS electrode, electrode size has little effect on ECG signal quality. Even with smaller electrode sizes, high-quality ECG signals were maintained, indicating the robust performance of the electrode design. This consistency in SNRs across varying electrode sizes underscores the importance of achieving full conformal contact and stable skin-electrode impedance, highlighting the effectiveness of the substrate-free PEDOT:PSS electrodes in capturing reliable ECG signals regardless of electrode size.
# Modeling of Fatigue-Strength Development
## 3 Results and Discussions
### Prediction of Fatigue-Strength Development in Emulsion Asphalt.
#### 3.2.2 Rate Constants for Modified Fatigue-Age Relationships  
The rate constants, k, together with the modified limiting fatigue-strength values, F\({}_{\text{u}}\), were determined for each cure temperature by fitting modified fatigue data in Tables 2-4 to modified fatigue-age functions, Equations (10) and (11), respectively. The parameters, k and F\({}_{\text{u}}\), were computed by non-linear least squares curve fitting technique using \"Solver\", a data analysis tool embedded in Microsoft Office Excel spreadsheets application. For illustration purposes, computation of k and F\({}_{\text{u}}\) for cold-emulsion asphalt specimens cured at 5 \({}^{\circ}\)C and tested at 125 \(\upmu\)m/m are presented in Table A1 of Appendix A. Values of k and F\({}_{\text{u}}\) obtained for all the strain levels and cure temperatures are recorded in Tables 5 and 6 for linear hyperbolic and parabolic hyperbolic models, respectively.  
It can be observed in Tables 5 and 6 that, at each test strain level, the rate constants for the cold-emulsion reclaimed asphalt pavement mixture generally increase with an increase in cure temperature. The modified limiting fatigue strength values, on the other hand, decrease with an increase in cure temperature. The trend displayed by the rate constants implies that, at early curing periods, specimens cured at higher temperatures experience a more rapid drop in their fatigue strength than their counterparts cured at lower temperatures. This behavior is attributed to a more rapid moisture loss by specimens cured at higher temperatures than those cured at lower temperatures. The modified limiting fatigue values indicate that specimens cured at higher temperatures eventually attain higher fatigue strength values than those cured at lower temperatures. This behavior is attributed to enhanced adhesion between aggregates and the binder brought about by increased curing temperatures.
# In-Field Quantum-Protected Control-Based Key Distribution with a Lossy Urban Fiber Link
## 4 System Configuration
Figure 3 shows the calculated critical \(r_{\text{E}}\) as a function of \(\mu_{0}\) and \(\mu_{1}\) for the effective BER \(\tilde{p}_{\text{err}}=5\%\). Figure 3a covers a wider range of intensities, while Figure 3b focuses on a narrower range with the critical \(r_{\text{E}}\) near the experimentally observed \(r_{E}\) with some overhead. The marked point in Figure 3b corresponds to our experimental choice of intensities \(\mu_{0}=2370\) and \(\mu_{1}=2520\). The particular choice of \(\tilde{p}_{\text{err}}=5\%\) is motivated by the constraints of the LDPC-based error correction realization utilized in our scheme, which is optimized for BER below 5%. Under the experimental conditions--with the chosen \(\mu_{0}=2370\) and \(\mu_{1}=2520\) and using empirically picked postselection parameters--we observe a raw BER of \(p_{\text{err}}=18\%\). After advantage distillation with the block size \(M=2\), the effective BER \(\tilde{p}_{\text{err}}\) is close to 5%, meeting the requirements of the LDPC procedure. An extended table of intensities and critical \(r_{\text{E}}\) is provided in Section 7.3 in Methods. Although in the experiment we perform advantage distillation with \(M=2\), choosing other values of \(M\) can also yield positive secret key rate \(R\). The numerically calculated \(R\) as a function of \(r_{\text{E}}\) for \(M=\{2,\,3,\,4\}\) is plotted in Figure 4. Within the highlighted experimentally observed range of \(r_{\text{E}}\), \(M=2\) ensures the best secret key rates, while \(M=3\) and \(M=4\) allow for less optimal results. Other choices of \(M\geq 5\), not represented in the plot result in substantially worse \(R\). Note that, without advantage distillation (\(M=1\)), the amount of information disclosed with error correction is so high that the users lose informational edge over Eve, and \(R=0\).
## 4 Statistical Models
### Drift Model  
In the process of designing the Su-Tong Bridge in China, drift model was developed to calculate the probability of collisions between ships and the bridge pier [14]. The movement of an out-of-control ship is illustrated in Figure 4, where V is ship instantaneous speed, U is current speed, and V\({}_{\text{S}}\) is the ship speed over ground. When a ship is out of control, the ship's motion can be divided into two steps. The first step is from Point \(G_{0}\) (initial position when a ship is out of control) to \(G_{1}\), where the ship is moving with inertia under the forces from wind and currents. From Point \(G_{1}\) to point \(G_{2}\) (position of a bridge pier), only wind and currents are the major factors for the movements. Whether a collision happens or not depends on the fairway condition, currents, wind, and the positions of the piers, etc. There are formulas to calculate the lateral distances (\(B_{1}+B_{1}^{\prime}+B_{2}+B_{2}^{\prime}\)) in Figure 4, in which \(B_{1}\) is the drift on axis x caused by the current within the stopping period, \(B_{1}^{\prime}\) is the drift on axis x caused by wind within the stopping period, \(B_{2}\) is the drift on axis x caused by the current without inertia, and \(B_{2}^{\prime}\) is the drift on axis x caused by the wind without inertia. There are empirical formulas to calculate the lateral drifts. The stopping distance and stopping time are constant, and these can be found in the ship particulars. After numerous calculations of drift tracks from different ships with different initial speeds and positions in the waterway, the dangerous collision areas can be determined (shadowed areas in Figure 5).  
Figure 4: Drifting stages of the ship that is out of control [14].  
Figure 3: Ship drifts towards the bridge line (based on [28]).  
The dangerous areas will be formed via various ship tracks, where out-of-control ships have a chance to collide with the bridge. Then the collision probability is:  
\[P_{collision}=\frac{A_{danger}}{A_{0}} \tag{4}\]  
where, \(A_{0}\) is the area in which the ship navigates and \(A_{danger}\) is the dimension of the dangerous area in which the ship may collide with the bridge. This area is determined by factors including wind, currents, and ship speed.
## 3 Insights into Algal-Bacterial Biomass
### Formation Processes of the microalgae-Bacteria Consortium (MABA)  
The aggregates have been successfully cultivated in various configurations across different systems. [PERSON] et al. (2018) [53] operated a sequential batch reactor and noted an initial aggregation of filamentous microalgae on the granules' surface, followed by settling inside, resulting in a dark green coloration. [PERSON] et al. (2017) [51] developed photogranular biofilm in a continuous regime and reported that filamentous microalgae can act as nuclei for the aggregates. In general, the presence of cyanobacteria and/or other filamentous microalgae is considered essential to the formation of MABAs [39].  
Generally, a photogranular biofilm has either the appearance of filamentous aggregate (Figure 2a), due to the prevalence of filamentous microalgae, or more a compact structure (Figure 2b), which exhibits better sedimentability properties [24]. The compact form represents a mature state of filamentous aggregates, and the presence of microalgae improves the granulation process, decreasing the formation time and improving the biomass structure [48; 49]. The microalgae's species play an essential role in biofilm formation, which was reported by [PERSON] and [PERSON] (2016) [19]. The authors documented the physical characteristics of a bacteria-algae aggregate cultivated in a high-rate pond as a central aggregate with a predominance of diatoms with filamentous microalgae attached to them. In addition, later it was observed that an increase in this diatom nucleus led to fewer filamentous bacteria cells and enhanced sedimentation [54].  
While most investigations into microalgae-bacteria consortia (MABAs) have been conducted in closed reactors operating in sequential batches [25; 55; 56], studies have also been undertaken in single-batch reactors [5; 13], continuous flow systems [57; 58], and high-rate ponds [24; 59]. Furthermore, aggregate formation has been observed in high-rate ponds operating in a sequential batch regime [50]. Moreover, a crucial factor in MABA formation is the secretion of extracellular polymeric substances by microorganisms, primarily bacteria [47]. EPS, characterized by a permeable hydrogel primarily composed of carbohydrates and proteins, facilitates microalgae and bacteria aggregation while also acting as a carbon source and providing protection for microorganisms [52]. EPS plays a role in the formation of MABA nuclei [19].  
Figure 2: Filamentous and compacted aggregate morphology: (**a**) filamentous structure and (**b**) compacted structure.
## 2 Operational Principle and Mathematical Model
### Operational Principle  
Figure 1 illustrates the structure of a typical EPS system; the EPS consists of a steering wheel, torque sensor, vehicle velocity sensor, electronic control unit (ECU), electrical motor and rack and pinion assembly [3]. The driver torque and vehicle velocity are sent to the ECU to calculate the appropriate value of the assist torque using the desired assist torque curve. The assist torque is provided by the electric motor and transmission mechanism.  
The assist torque is needed to overcome the steering resistance and complete the turning action while reducing the driver applied torque. The main objective of the controller is to ensure that the curve of the desired driver torque always tracks the actual driver torque at different vehicle speeds.
## 3 Case Study
### Analysis of Influenced OD Passenger Flow
#### 3.4.1 OD Passenger Flow at the Station with the Disruption  
Bajiao Amusement Park station, which was the disrupted station, is an important station for residents in western Beijing. To analyze the influence of the disruption on the OD passenger flow at Bajiao Amusement Park station, taking this station as the origin, destination stations were ranked according to the OD passenger flow, and the top ten destination stations were selected. The OD passenger flow corresponding to the top ten destination stations is illustrated in Figure 8. It was found that the travel destinations of passengers at Bajiao Amusement Park station were mainly the business district and office area. Among the top ten destination stations, the distance from Bajiao Amusement Park station to Wanshoulu (WSL) station and Wukesong (WKS) station is short, and the travel time is within 15 min, while the distance from Bajiao Amusement Park station to other stations is relatively long.  
As shown in Figure 8, all OD passenger flow at Bajiao Amusement Park station decreased due to the disruption. Compared with the long-distance travel demand, the short-distance travel demand was more affected by the disruption because it was more convenient for passengers to choose ground-level transportation as an alternative to continue their travel after the disruption. However, long-distance ground-level transportation is not as efficient as urban rail transit in terms of travel time, which led to more long-distance passengers choosing to wait outside the station and continue their travel after the disruption was resolved.  
Figure 7: Variation in the influence intensity at Military Museum station (representative transfer station). (**a**) Variation in the influence intensity at Military Museum station on line 1. (**b**) Variation in the influence intensity at Military Museum station on line 9.  
To analyze the influence of the disruption on the OD passenger flow at Babaoshan station, taking this station as the origin, destination stations were ranked according to the OD passenger flow, and the top ten stations were selected. The OD passenger flow corresponding to the top ten destination stations is illustrated in Figure 9. Passengers who left the rail transit system and chose other travel modes mainly traveled short distances, such as to the FKM station or NLSL station. However, passengers traveling long distances chose to wait outside the station because ground-level transportation could not meet their travel purposes. Therefore, it was concluded that the occurrence of the disruption had a great influence on passenger delay for long-distance travel at the ordinary station.
# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 2 Task Factorisation
### Audio Tagging (WHO)  
The second intermediate task of our approach is the WHO network that performs audio tagging using the provided weak labels of a low-resource dataset. This task follows supervised training since the weak labels provided are exactly the ones that the network will try to learn how to predict. Hence, our training techniques that we use for the WHO network follow standard approaches.  
Neural Network Architecture  
A similar network architecture to the one proposed for WHEN (see Table 1) is used for the first few layers of WHO in order to implement our proposed training approaches that we introduce in Section 3. Table 2 describes the structure of each individual layer used in the WHO network.  
Similar to the WHEN network, the log mel-band energy feature extracted from the audio is used as input with shape \(T\times\) 40, where \(T\) is the number of time frames in a recording. The convolutional layers in the beginning of the network are in charge of learning the local shift-invariant features of this input. We use a 3 \(\times\) 3 receptive field and the padding arguments set as'same'. Max-pooling is performed along the frequency axis after every convolutional layer to reduce the dimension for the feature matrix. Global average pooling is finally applied to the output of the convolutional part of the network and the results are fed to a dense layer that has units equal to the number of labels for our tagging task with sigmoid activation that predict the probability of each class being present in a recording. The dimensions of each prediction are 1 \(\times\) #labels. Finally, we calculate the binary cross-entropy loss on this output and the ground truth extracted from the weak labels.
## 4 Modified LLB Method with Deck Charge Technique  
### Concept behind the Modified LLB Method  
The deck charge technique divides the explosives into multiple sections, with each blast hole having different detonation times. Therefore, reducing the charge per delay can simultaneously reduce the blasting vibration and increase the blasting efficiency [36; 37; 38]. Specifically, reductions in production and drilling costs have been reported to amount to 10-45% and 25%, respectively [39; 40]. Moreover, the amount of explosives was reduced by 15-35%, along with improved blasting efficiency [41; 42; 43], and better breakage of rock was obtained using the air-deck charging technique in crater blasting [44]. Based on previous studies, it is understandable that the deck charge technique has a great advantage in improving rock blasting.  
Figure 7: Results of numerical analysis of the LLB method: (**a**) 0 ms, (**b**) 0.5 ms, (**c**) 1.0 ms, (**d**) 1.5 ms, (**e**) 2.0 ms, (**f**) 2.5 ms, (**g**) 3.0 ms, (**h**) 3.5 ms.  
The newly proposed method is a modification of the LLB method that incorporates deck charge blasting to achieve efficient long hole blasting. The modified LLB method uses the same amount of explosives as the original LLB method, but the explosives are loaded in separate parts, as shown in Figure 8.  
Figure 9 shows the predicted blasting process for the modified LLB method. When explosives #1 located near the excavation face are first detonated in stage 1, the crushed rocks are moved toward the free face (excavation face). This is important for creating additional free faces to facilitate the next round of blasting (stage 2). When explosives #2 located at the end of the blast holes are detonated in stage 3, the fragmented rocks are moved in stage 4 through the formed free face.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 4 Results and Discussion
### Optimization Analysis--RSM Model
Moreover, the \(R^{2}\) and \(R_{adj}^{2}\) coefficients for the steady-state moment were found to be 0.99654 and 0.99614, with a percentage difference of 0.04%. These two deviations were good indicators of accuracy and thus verified that two responses fulfilled the criterion of accuracy. The final step before obtaining the optimal design solution was the application of the LSGRG algorithm (large-scale generalized reduced gradient), which was able to control equality and inequality restraints [18]. The design parameter intervals of \(\theta\) = [45, 90] and \(t\) = [0.05, 0.15] were added in the algorithm, as well as the primary point of x = [0.1, 65.0] T. The maximum \(\sigma_{\mathrm{v}}\) was kept lower than the depicted allowable \(\sigma_{\mathrm{v}}\) of 1.9 \(\times\) 10\({}^{3}\) N/mm\({}^{2}\) to assure stability, durability, and safety of the DLTS hinge in the stowed state. The LSGRG algorithm ran multiple simulations to acquire the optimal design parameters, and thus, 27 iterations were required. Table 4 shows the modifications of the critical design parameters, max \(\sigma_{\mathrm{v}}\) and steady-state moment from the original to the optimal design of the DLTS hinge. From Table 4, it is observed that the \(\theta\) variable was reduced by 97.92%, whereas the \(t\) variable was increased by 94.83%. The max \(\sigma_{\mathrm{v}}\) and steady-state moment values were decreased and elevated by 11.58% and 13.34%, respectively.
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### Link Discovery Protocol
The mandatory fields contain the information that the SDN-Switch wants to advertise to its neighbor which is: Chassis-ID (which is a unique switch identifier), Port ID (which is its egress port), and time to live. Therefore, to illustrate the OFDP function, we explain the cooperation between the ODFP sections based on each other, as illustrated in Figure 6. The SDN-Controller sends every specified period (i.e., 10 s) an LLDP packet encapsulated with a Packet-Out message to each active port in each SDN-Switch [38]. From Figure 6, the SDN-Switch (s1) has three active ports, which means three LLDP packets and each of these packets has Port-ID TLVs and a Chassis-ID configured accordingly. In OpenFlow protocol, if the SDN-Controller wants to send a packet to the OpenFlow SDN-Switch it will encapsulate it in a Packet-Out message. The Packet_Out message structure contains a field called the instruction field. This field is responsible for deciding what the SDN-Switch should do in that packet. Therefore, the LLDP packet is obtained from all ports in SDN-Switch except for the one connected to the SDN-Controller. This port communicates with SDN-Controller via an OpenFlow Packet-In message. Moreover, it is used to collect and aggregate all the port information in each SDN-Switch into a single \"Packet-In\" message and sends it to the SDN-Controller according to a proactive rule installed in the flow tables of all SDN-Switches. In the Figure 6 scenario, the LLDP packet on port-ID 1 is sent from SDN-Switch (s1) and received by SDN-Switch (s2) through port 1 as well. Moreover, the Packet_Out message has a field called instruction which is configured to forward the encapsulated LLDP out of the corresponding port on the SDN-Switch [39; 42]. Next, the SDN-Switch (s1) will, in turn, receive Packet-Out messages, de-decompile the LLDP packet from the Packet-Out message, and forward only the LLDP packet that has exited from each matching port (based on the port's MAC address in LLDPDU). When SDN-Switch (s2) receives an LLDP packet, it will parse the LLDP packet, write its SDN-Switch Chassis-ID, and add Port-ID (i.e., the port through which the SDN-Switch received the LLDP packet). Then, SDN-Switch (s2) will encapsulate the LLDP packet in a Packet_In message and send it to the SDN-Controller. The SDN-Controller will in turn parse the Packet_In message and discover the new links represented by the mapping between s1 and s2 [65]. However, the same method is repeated to discover the remaining links in the network [39]. Figure 5: LLDP frame format used in OFDP [39].