esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Analysis
### Reliability  
A transmission is always successful when exactly one ZigBee device transmits without any channel error or collision. The packet error depends on the bit error rate and the packet length. Let, \(L_{Data}\) be the length of the data packet in bits, \(N\) be the number of ZigBee devices and \(T_{pkt}\) be the ZigBee packet duration. A transmission will be successful if the ZigBee device receives the acknowledgment packet corresponding to its transmitted packet. The coordinator sends the acknowledgment packet, if exactly one ZigBee device transmits without any channel error and no WiFi device starts transmission during the ongoing ZigBee transmission. Hence, the successful transmission probability for a ZigBee device is:  
\[P_{s}=\frac{N\phi(1-\phi)^{N-1}}{(1-(1-\phi)^{N})}(1-P_{b}(\zeta))^{L_{Data}}e ^{-T_{pkt}\lambda_{w}}. \tag{22}\]  
The packet is dropped mainly due to retry limits or channel access failure. The channel access failure occurs when the tagged ZigBee device does not find any channel ideal during \(maxMacBackoff\) number of backoffs. Considering the Markov chain model presented in Figure 8, the probability of channel access failure can be derived as follows.  
\[P_{cf}=\{p_{2}+(1-p_{2})\alpha\}b_{m,0}. \tag{23}\]  
In our proposed modified superframe, separate durations are allotted for collided devices, i.e., only failure devices contend during those periods. Let \(P_{s_{j}}\) be the probability of successful transmission by a ZigBee device in the \(j\)-th allocated retransmission duration of the proposed modified superframe. If the expected number of ZigBee devices that contend in the \(j\)-th retransmission period is \(N_{j}\), then:  
\[P_{s_{j}}=\frac{N_{j}\phi(1-\phi)^{N_{j}-1}}{(1-(1-\phi)^{N_{j}})}(1-P_{b}( \zeta))^{L_{Data}}e^{-T_{pkt}\lambda_{w}}. \tag{24}\]  
Let us assume that \(maxFrameRetries=r\). Hence, the probability of transmission failure during all \(r\) attempts is:  
\[P_{tf}=\prod_{j=1}^{r}(1-P_{s_{j}}) \tag{25}\]  
It is assumed that the generated data packets are first buffered by each device and then are transmitted to the coordinator. However, the ZigBee device has buffer constraints. Let the data arrival rate of the ZigBee devices be \(\lambda_{z}\), the service rate be \(\mu_{z}\) and the buffer maximum be \(l\) number of packets. Hence, the device can calculate its utilization rate, which is \(\rho_{z}=\frac{\lambda_{z}}{\mu_{z}}\). Let \(P_{bf}\) be the probability that the buffer of the ZigBee device is full, i.e., there are \(l\) number of packets in the buffer of the ZigBee device. Hence, the newly-generated packet will be discarded due to full buffer being:  
\[P_{bf}=\frac{\rho_{z}^{l}}{\sum_{l=0}^{l}\rho_{z}^{i}} \tag{26}\]  
Furthermore, the reliability can be deduced as follows.  
\[P_{R}=(1-P_{cf})(1-P_{tf})(1-P_{bf}) \tag{27}\]
## 4 Experiments
### Hyperparameters  
#### 4.4.1 Rigid Threshold  
Our MA-NMS, along with well-known NMS algorithms, such as Greedy NMS, Soft NMS [16], and Adaptive NMS [17], incorporates the hyperparameter \(N_{t}\) as the rigid threshold. In order to achieve their optimal performances and sensitivities, different values of \(N_{t}\) are employed in the experiments. The evaluation metric \(AP\) is used to measure the average precision of the pedestrian detector. To ensure a fair comparison, all the NMS algorithms are implemented using the same programming language and separately applied to the baseline with the same settings. Figure 4 illustrates the results of NMS algorithms with varying values of \(N_{t}\). Obviously, MA-NMS achieves the best performance across all values of \(N_{t}\), and the optimal results for all the NMS algorithms in the experiments are obtained, when \(N_{t}\) is set to 0.5. Furthermore, the results shows that Greedy NMS remains consistent between 0.55 and 0.85, Adaptive NMS is stable between 0.80 and 0.87, while MA-NMS remains stable between 0.85 and 0.90, with only 16.67% and 71.42% of their \(AP\) values floating, which indicates that MA-NMS exhibits lower sensitivity to the hyperparameters \(N_{t}\) compared to these well-known NMS algorithms.
## 2 Materials and Methods
### Validation of the Analytical Method  
The SB-MSPE-HPLC-PDA method was validated in terms of linearity, precision, accuracy, sensitivity and selectivity.  
Linearity studies were performed by analyzing each concentration three times, covering the whole working range. The construction of the calibration curves of the two BZs was accomplished by plotting the peak area versus the concentration. For the calculation of the slopes, intercept and coefficients of determination, least square linear regression analysis was used. The limit of quantification (LOQ) was equal to the concentration of the first starting point of the calibration curve, while the limit of detection was calculated via the equation of LOQ = 3.3 LOD.  
Accuracy and precision were studied in three different concentrations, 25, 50 and 100 ng/mL, in spiked water samples. Relative recoveries (R\({}_{\text{rel}}\)%) were calculated by the formula of (mean concentration found/theoretical concentration spiked * 100). The precision was stated as the standard deviation (SD) between nominal and measured concentrations. Intraday or within-day experiments consisted of the triplicate analysis (\(n\) = 3) of each level of concentration in the duration of one day, while interday or between-day experiments were conducted by analyzing each level twice for four consecutive days.
# Security of IoT Application Layer Protocols: Challenges and Findings
## 4 Messaging Protocols
### Dds  
Data Distribution Service (DDS) is a data-centric standard protocol defined by the Object Management Group. The protocol is generally used to manage data exchanges between lightweight devices and large high-performance sensor networks as well as the cloud. While not being a typical IoT solution, DDS finds its application in some industrial deployments, such as air-traffic control, smart grid management, autonomous vehicles, transportation systems and healthcare services.  
Concerning security, the DDS protocol offers a rich variety of mechanisms. As other messaging protocols, DDS supports both TLS and DTLS. Moreover, for ensuring confidentiality, integrity and authenticity of the exchanges, the newest OMG DDS security specification defines an architecture based on a set of built-in plugins. For example, plugins offer mechanisms for authentication and authorization of DataWriters and DataReaders, thus avoiding unauthorized publication and subscription. Nevertheless, both specification and plugins are affected by vulnerabilities. In particular, the handshake protocol used for permission attestation sends clear text information about participant capabilities, thus allowing attackers to discover potentially sensitive reachability information on a DDS network (CVE-2019-15135). As [PERSON] et al. (2019) reported, this vulnerability breaches the confidentiality of the connection and allows attackers to collect information that could be used for malicious purposes.  
It is also important to point out that plugins per se do not ensure security of DDS environments. In particular, the two vulnerabilities discovered for the Access Control plugin could lead to unauthorized or unintended connections between participants (CVE-2019-15136, CVE-2019-15137).  
Finally, it is worth mentioning that not every DDS product and service are compliant to the security specification and even compliant implementations might be affected by vulnerabilities. In fact, as shown in [58], node misconfiguration can be abused to perform malicious activities inside a DDS environment.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 4 Discussion
### Accuracy  
First, we extracted the wavelet features (energy and entropy) for the sparse representation of EEG data. The wavelet transform enables the signal decomposition into subsignals consisting of different frequencies (high and low) with distinct spatial and directional characteristics [45]. Therefore, the sparse representation of wavelet features (energy and entropy) provides the discriminable features necessary for EEG classification in the sparse domain [10]. Then, a sparse dictionary was constructed based on the wavelet features. The overcomplete dictionary D consists of four sub-dictionaries corresponding to each class of the data (refer to Equation (1)). Hence, there is a high variance between the sub-dictionaries, and each test signal can be represented through the sub-dictionary corresponding to the class. Therefore, there is no chance of misrepresentation of the class of the test signal, and the classification accuracy is significantly improved.  
Furthermore, we have used different epoch lengths to investigate which epoch length yields better classification accuracy in two scenarios: without selecting channels and with selecting channels. The dataset contains 64-channel EEG data and MI tasks only activate the sensorimotor areas for the MI task. Hence, we selected the EEG channels corresponding to motor cortex region, and there was a significant improvement in training time and testing time improved with selected channels along with classification accuracy.  
We have compared our methodology with state-of-the-art methods. The proposed methodology that outperformed other studies has two major drawbacks. Firstly, selection of features for sparse representation as the sparse features should be able to carry enough information for classification [40]. Second, the proposed classifiers were based on EEGNet architecture, which was initially proposed to classify P300 data [33].
## 5 Evaluation of Reliability Cost
### Background on Reliability Theory
#### 5.1.3 Series and parallel systems  
A series system consists of \(n\) components connected in a series fashion as shown in Figure 10(a). Such a system is only functional if all of its components are functional. The reliability function \(r(p)\) of a series system is  
\[r(p)=\prod_{i\text{-}1}^{n}p_{i}. \tag{5}\]  
A parallel system, on the other hand, has components connected in a parallel fashion (Figure 10 (b)). In this case, the system is functional if at least one of its components is functional. Therefore, the reliability function of a parallel system is  
\[r(p)=1-\prod_{i\text{-}1}^{n}(1-p_{i}). \tag{6}\]  
Figure 10: A series and a parallel system.
# Processing of Single-Walled Carbon Nanotubes with Femtosecond Laser Pulses
###### Abstract  
There are continued efforts to process and join single wall carbon nanotubes (SWCNTs) in order to exploit their exceptional functional properties for real-world applications. In this work, we report experimental observations of femtosecond laser irradiation on SWCNTs, in order to process and join them through an efficient and cost-effective technique. The nanotubes were deagglomerated in ethanol by an ultrasonicator and thin slurries of SWCNTs were spread evenly on glass substrates. A laser micromachining workstation for laboratory FemtoLAB (workshop of photonics) has been employed to irradiate the different SWCNTs film samples. The effect of laser parameters, such as pulse wavelength, laser power, etc., were systematically tuned to see the possibility of joining the SWCNTs ropes. Several experiments have been performed to optimize the parameters on different samples of SWCNTs. In general, the nanotubes were mostly damaged by the infrared (1 st harmonics femtosecond laser) irradiation on the focal plane. However, the less damaging effect was observed for second harmonics (green wavelength) irradiation. The results suggest some joining of nanotubes along the sides of the focus plane, as well as on the center at the brink of nanotubes. The joining is considered to be established within the region of the high field intensity of the exposed femtosecond laser beam.  
femtosecond laser; SWCNTs; interconnect; joining Article
# Optimal Attitude Determination for the CR200 Underwater Walking Robot
## 4 Flow Physics at the Optimal Attitude
### Optimal Seabed Distance  
With the optimal pitch angle determined in the previous subsection, the effect of the optimal seafloor clearance is investigated. To exclude the influence of pitch angle, the relationship between distance and downforce is examined by analyzing the downforce at a constant pitch angle. Figure 15 illustrates the variation in downforce magnitude as a function of pitch angle for three different seafloor clearances. The data represented by the solid line shown in Figure 15 are shown in Table 5.  
Figure 14: Streamline on the CR200 surfaces for 3 different cases of \(\alpha\) when \(\hat{\mu}=0.12\) (m).  
Figure 15: Variation of downforce magnitude concerning seafloor clearance \(\hat{\mu}\) at different fixed pitch angles.  
Figure 16 shows the velocity distribution in the x-direction, aligned with the incoming flow. The result demonstrates the clearance effect for the predetermined pitch angle. By only changing the seafloor clearance, the flow acceleration effect is quantitatively presented. Interestingly, the flow acceleration does not vary in a monotonic manner; rather, an optimal clearance exists, as shown in Figure 16b. If the clearance is too small, as represented in Figure 16a, the blockage effect dominates over the Venturi effect. As the clearance increases, the flow generally accelerates. However, this trend does not persist. For even wider clearances, the flow decelerates again, as shown in Figure 16c.  
Figure 17 shows the pressure contour in the direction of the uniform inflow along the longitudinal plane. It can be observed that, in all cases, a narrow clearance from the seabed causes a pressure drop, leading to faster flow velocities. Comparing Figure 17a,b, as the distance from the seabed increases, the region where the flow accelerates and the pressure drops expands. On the other hand, as shown in Figure 17c, the pressure reduction in the space between the lower surface and the seabed weakens, resulting in a diminished adverse pressure gradient upstream, which leads to a reduction in downforce.  
The given optimal pitch angle, which produces maximum downforce, can be further tuned to find the optimal distance from the seabed. Similar to the variation in pitch angle, an optimal seabed clearance can also be determined. With respect to seabed distance, maintaining an optimal level of clearance and avoiding both excess and deficiency, as illustrated in Figure 17b, maximizes ground suction. By adjusting the seabed distance configuration, maximum suction on the lower body surface can be generated, leading to the highest downforce, which, in turn, results in the optimal pitch angle and optimal seabed clearance.  
Figure 17: The pressure contour around the CR200 at three different seabed distances \(\hat{\varkappa}\) with \(\alpha=12^{\circ}\).
## 3 Results
### Classification Potential  
We propose the concept of classification potential (CP), which is calculated as a ratio of inter-person variability and intra-person variability. The formula is expressed as:  
\[CP=\frac{\text{Inter-person variability}}{\text{Intra-person variability}}\]  
The CP metric is considered favorable when the numerator (inter-person variability) is as high as possible, indicating that the handwriting of different individuals is easily distinguishable. On the other hand, the denominator (intra-person variability) should be as low as possible, showing that each individual's handwriting is highly consistent across trials. Because the proposed apparatus does not represent a handwriting classification solution, the trade-off between system accuracy and implementation cost cannot be directly analyzed. However, the obtained values of the CP metric can be useful in this context. Specifically, a combination of high sensor cost (whether from purchase price or the effort needed for experimental integration) and a low CP value suggests that the sensor could be a candidate for exclusion from the fusion. In other words, if a researcher intends to remove certain sensors or groups of sensors from the fusion for any reason, CP, along with the cost, serves as a relevant factor in making this decision.  
Tables 14 and 15 compare intra-person and inter-person variability, along with classification potential (CP), for various sensors and measurements. Data were collected during stylus and finger writing tasks where participants wrote the phrase '_We trust you_' five times. In comparison to other sensors or measurements, the accelerometer shows a higher CP, especially in the finger-writing task (CP = 26.53). Similarly, the magnetometer seems to be a valuable part of the apparatus in both tasks with CP values of 7.71 for stylus writing and 6.73 for finger writing. Stylus orientation values, available only for stylus writing, also show a higher CP of 9.86 when compared to other readings. On the other hand, the piezoelectric sensor shows lower CP values with 1.42 and 1.37 for stylus and finger writing, respectively.  
Figure 17: Visual depiction of vibration and pressure changes derived from a piezoelectric sensor below the tablet. Three different users employed the stylus in an attempt to compose an identical phrase 3 consecutive times.  
Similarly, velocity readings for both input modalities exhibit the lowest CP (0.97) and show poor classification potential.
## 4 Experiments and Results
### Models of Process of the Capacities of Self-Optimization and Self-Configuration
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline AODV Parameters & Standard & Small & Medium & Large \\ \hline
**route\_request\_retries** & 1 & 8.2171 & 3.2413 & 2.9104 \\ \hline route\_request\_rate & 10 & 10 & 10 & 10 \\ \hline grat\_route\_reply\_flag & False & True & False & False \\ \hline dest\_only\_flag & False & True & False & True \\ \hline ack\_required & False & True & True & False \\ \hline
**hello\_interval** & 1.0 & 3.2906 & 5.9951 & 9.0137 \\ \hline
**active\_route\_timeout** & 3.0 & 2.7784 & 4.3656 & 5.8208 \\ \hline
**allowed\_hello\_loss** & 2 & 7.0288 & 2.5524 & 1.3097 \\ \hline
**net\_diameter** & 35 & 8 & 3 & 1 \\ \hline node\_traversal\_time & 0,04 & 0,04 & 0,04 & 0.04 \\ \hline route\_error\_rate & 10 & 5 & 10 & 15 \\ \hline timeout\_buffer & 2 & 2 & 2 & 2 \\ \hline pk\_queue\_size & -1 & -1 & -1 & -1 \\ \hline local\_repair & True & True & True & false \\ \hline ttl\_start & 1 & 1 & 1 & 1 \\ \hline ttl\_increment & 2 & 2 & 2 & 2 \\ \hline ttl\_threshold & 7 & 7 & 7 & 7 \\ \hline local\_add\_ttl & 2 & 2 & 2 & 2 \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters description of the protocol AODV after the Self-Optimization process. It is valid to stand out that in the Tables 2 and 3, the self-optimization is made in way _off-line_, in other words, the data are externally optimized by MATLAB and later coupled inside the self-optimization module that if found implemented inside the simulator OPNET.
## 5 Brillouin Optical Correlation Domain Reflectometry
### BOCDR Systems with Performance Improvement Schemes
Brillouin Dynamic Grating and Its Application to Discriminative and Distributed Measurements of Strain and Temperature  
In the stimulated Brillouin scattering process, the propagating refractive index grating due to the acoustic wave is enhanced. Then, under the condition that the grating is excited by one eigen polarization mode in a polarization-maintaining fiber, the orthogonal polarization mode might also be reflected in the Bragg reflection condition. We have confirmed this Bragg reflection in the PM fiber in 2008 through collaborative work with Chung Ang University, Korea [19; 20]. This phenomenon is named \"Brillouin dynamic grating (BDG)\". The distributed measurement of BDG was performed in the correlation-domain technology [90] and the time-domain technology [91]. Additionally, the BDG is confirmed for use in the discriminative measurement of the strain and temperature of the distributed measurement situation. In our laboratory, another dynamic grating had also been realized by utilizing the gain saturation phenomenon in erbium-doped optical fiber before the demonstration of BDG [92]. Additionally, with the EDFA dynamic grating, strain-distributed measurement had also been demonstrated [93; 94].
# Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
## 2 Numerical Simulation
### Wind Load  
Previous wind tunnel tests have shown that lateral connectors can successfully suppress the strong WIV of a structure [30]. An equivalent static wind load was adopted for the structural design of the CSPS. As the most unfavorable loading condition is crosswind (Case 0\({}^{\circ}\) and Case 180\({}^{\circ}\)), in the present study, the equivalent static wind load of the PV modules under the two controlling cases were calculated according to [32; 41]. Case 0\({}^{\circ}\) indicates that the wind is normal to the span of the PV array and hits the upper surface of the PV modules, and case 180\({}^{\circ}\) indicates that the wind hits the PV modules in the opposite direction, as shown in Figure 4. It is noteworthy that only the self-weight, prestressed load, and wind load were considered to simplify the design process.  
According to the Chinese codes [32; 41], the equivalent static wind load, \(w_{\rm k}\), on ground-mounted PV modules is calculated as  
\[w_{\rm k}=\beta_{\rm z}\mu_{\rm s1}\mu_{\rm z}w_{\rm o} \tag{1}\]  
where \(\beta_{\rm z}\) is the wind vibration coefficient at height \(z\), \(\mu_{\rm s1}\) is the shape coefficient of the PV module, \(\mu_{\rm z}\) is the height variation factor of the wind pressure, \(w_{\rm o}\) is the reference wind pressure with a 25-year return period, and \(\rho=1.29\) kg/m\({}^{3}\) is the air density. In the present study, \(\beta_{\rm z}\) is measured as 1.6 trough wind tunnel testing; \(\mu_{\rm s1}\) is equal to 0.8 or \(-0.95\) in Case 0\({}^{\circ}\) and 180\({}^{\circ}\), respectively; and \(\mu_{\rm z}\) is set as 1.0, because the prototype of the CSPS structure is lower than 10 m. The reference wind pressure of the prototype \(w_{\rm o}\) is 0.5 kN/m\({}^{2}\).
# An Analysis of Visibility Requirements and Reaction Times of Near-Field Projections
###### Abstract  
Successful communication between highly automated vehicles and vulnerable road users will be crucial in the future. In addition to the technical requirements of the communication system, the projected content is also essential to ensure successful communication. For this purpose, previous studies have investigated the necessary technical requirements for near-field projections. However, the impact of the presentation content, whether symbol- or text-based, on the technological domain, has yet to be investigated. Therefore, a psychophysical subject study investigated the necessary detection probability for symbol- and text-based projection in the near-field of a vehicle. The visibility of symbol- and text-based projections were analyzed by the subject's detection rate of the tested projection in an ambient lighting scenario of 20 lx at two different distances. Additionally, the corresponding reaction time of the subjects was measured. The results of the subject study showed that, contrarily, an arbitrary increase does not reduce the reaction time and thus saturates at a level of 650 ms before the 90% detection threshold for both projection contents. The observed detection contrast indicates that symbol-based projections need approximately 25% less contrast level than text-based projections to reach a 90% detection rate.  
near-field projections; visibility requirements; contrast; reaction times; adaption luminance +
Footnote †: journal: Medical Imaging  
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