esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Analysis of Cyclone Separator for Separation of Cenospheres
## 3 Results and Discussion
### Effect of Different Incidence Velocities on Separation Performance  
Under the selected conditions of an upper discharge opening radius of 90 mm and sloping wall height of 400 mm, this study analyzes the specific effect of incidence velocity on the performance of the cyclone separator in separating nanospheres by varying the incidence velocity. This analysis contributes to an in-depth understanding of the effect of varying the incident velocity on the separation efficiency and other related performance metrics. Incidence velocities of 2.5 m/s, 5 m/s, 7.5 m/s, 10 m/s, and 12.5 m/s were selected. Figure 8 shows the velocity vector diagrams derived from simulations with incident velocities v of 2.5 m/s, 5.0 m/s, 7.5 m/s, 10.0 m/s, and 12.5 m/s, respectively. Figure 9 shows the particle trajectory diagrams for different incident velocities \(\
u\).  
As shown in Figure 9, the number of escaping particles tends to decrease with decreasing incident velocity. The data in Table 5 show that the [PERSON]'s efficiency decreases gradually with the decrease in the incident velocity, and when the incident velocity is 2.5 m/s, the [PERSON]'s efficiency reaches 0.55. On the contrary, when the incident velocity  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sloping Wall Height** & \begin{tabular}{c} **Separation Efficiency** \\ **of Cencospheres** \\ \(\gamma_{A}\) **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Fly Ash Separation** \\ **Efficiency** \\ \(\gamma_{B}\) **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Newton’s Efficiency** \\ \(\gamma_{N}\) **(\%)** \\ \end{tabular} \\ \hline
400 & 93.75 & 45 & 0.488 \\
450 & 92.86 & 47.02 & 0.458 \\
500 & 77.5 & 36.25 & 0.412 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency of different sloping wall heights.  
Figure 8: Velocity vector plots for different incident velocities.  
is increased to 12.5 m/s, the separation efficiency of the cenospheres decreases to 6.3%, and the Newton's efficiency decreases to \(-\)0.406. The reason for this phenomenon is that the increase in the incident velocity results in the enhancement of the centrifugal force, which makes the cenospheres continue to rotate inside the cyclone without being discharged from the upper discharge opening. From the results, it is concluded that the separation of cenospheres is significantly affected by the incident velocity. When the velocity is too large, the Newtonian efficiency decreases to a negative number.
## 5 Simulation Results and Discussions
For example, when \(K=12\) UEs, the proposed matching scheme can attain approximately 6.55%, 25.3%, 33.53%, 39.9%, and 52% EE improvement compared to CMFAS with 50% and 75% of the selected antennas, CMDAS, DSAS, and FCPSs. We also observe  
Figure 4: Total power consumption versus number \(N_{r}\) of antennas, where \(M=80\) APs, \(N_{Q}=8\), \(\rho=23\) dBm, and \(K=8\) UEs. Figure 8 presents clear gains in the total power consumption obtained by the extended and proposed schemes in this paper. Figure 8 also demonstrates that when \(K\) increases, so should the total power consumption. For example, the proposed matching scheme uses 1615.2 W, while CMFAS with 50% of the selected antennas fixed uses 1539.38 W with \(K=12\), \(N_{r}=48\), \(N_{Q}=8\), \(M=80\), and \(\rho=23\) dBm. In addition, the proposed matching scheme achieves lower total power consumption compared to DSAS, CMDAS, CMFAS with 75% of the selected antennas fixed, and FCPSs by 40.53%, 31.62%, 21.01% and 41.046%, respectively.
## 3 Results and Discussion
### Damage Pattern Recognition
#### 3.1.3 Damage Pattern Recognition of crumb Rubber Concrete by the Density Peak Clustering Method
Among them, the peak frequency 40 kHz of cluster 1 corresponds to a larger amplitude, and the peak frequency 100 kHz of cluster 3 corresponds to a larger amplitude (Figure 13). This indicates that the failure mode reflected by the cluster 1 signal and the cluster 3 signal corresponds to these two main peaks, which are mentioned above: the damage of the concrete matrix and the new failure mode caused by the incorporation of rubber. Meanwhile, the cluster 1 signal and cluster 3 signal are based on the failure mode of the concrete matrix (40 kHz) and the new failure mode (100 kHz) caused by rubber incorporation, respectively. Therefore, for different damage modes, the FFT method can be used to make their corresponding peak frequencies more accurate. Through FFT calculation, it is found that for rubber concrete, the cluster 1 signal is mainly the destruction of the concrete matrix, which also includes the new failure mode caused by rubber fracture. Meanwhile, the cluster 3 signal is mainly caused by the rubber fracture but also includes the damage of the concrete matrix. According to Table 3, the average value of the rise time, count, energy, duration, and amplitude of the cluster 3 signal (mainly caused by a new failure mode caused by rubber) is higher than the cluster 1 signal (mainly caused by the destruction of the concrete matrix), respectively. Thus, the intensity of the cluster 3 signal is slightly greater than the cluster 1 signal. Figure 12: Cumulative energy of three clusters. Figure 13: FFT spectrum characteristic maps: (**a**) cluster 1; (**b**) cluster 3. The average frequency (AF) refers to the ratio of the ringing counts to the duration time. Many scholars have found that the average frequency of acoustic emission signals corresponding to different damage modes is different. For example, the average frequency of acoustic emission signals produced by tensile damage is higher, while the average frequency of acoustic emission signals produced by shear damage is lower [52]. The calculation Equation of the AF follows:  
\[\text{AF}=\text{AE}\,\text{ringdown}\,\text{counts}\,\text{/the duration}\,\text{time} \tag{18}\]  
According to Table 3, the average frequency of cluster 1 and 3 signals are quite different. By comparing the average frequencies of these two clusters of signals, the different damage modes in crumb rubber concrete are further characterized. Figure 14 shows that the average frequency of cluster 1 (mainly caused by the destruction of the concrete matrix) is 1000 kHz, and the cluster 3 signal caused by the new failure mode has an average frequency band of 0 to 100 kHz. Hence, the main failure mode of the cluster 1 signal is the tensile failure mode, and the main failure mode of the cluster 3 signal is the shear failure mode.
## 4 Results and Discussions
### Data Collection and Analysis
#### 4.1.4 Post-Processing and Cleaning of Data  
It was also declared that the simulation speed was to be scaled up to \(\times 20\) of the normal speed to avoid lengthy simulation times. To account for this, the researchers scaled the timestamps to the appropriate time using the equation:  
\[D_{f}=D_{i}-10L+RL \tag{1}\]  
where  
* \(D_{f}\) is the new dwell time;
* \(D_{i}\) is the current dwell time;
* \(L\) is the length of the list;
* \(R\) is the scale factor in seconds.  
The equation works by removing the decision-making time set in the simulation, which was set to 10 for each item in the list. A scale factor, denoted by R, was added for each item in the list to account for the actual speed of the simulation. This scale factor was a random value generated from 110 s to 130 s, resulting in the new dwell time averaging at a 2 min increase from the previous dwell time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Algorithm** \\ \hline NS & No sorting algorithm \\ \hline DProSA-AG & Agglomerative clustering, dynamic sorting \\ \hline DProSA-AP & Affinity propagation clustering, dynamic sorting \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the simulation setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Config** & **Item Pool** & **List Size** & **Unique Lists** & **Runs** \\ \hline
1a & 60 items & 5–7 & 100 & 4 \\ \hline
1b & 60 items & 8–14 & 100 & 4 \\ \hline
1c & 60 items & 15–21 & 100 & 4 \\ \hline
2 & 60 items & 5–21 & 100 & 1 \\ \hline
3 & 555 items & 5–21 & 100 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the test configurations.
# Current Status of HbA1c Biosensors
## 4 Conclusions  
With the number of diabetic patients increasing each year, it has caused a huge burden on public medical resources. A HbA1c POC device with good analytical performance can help patients with diabetes to monitor their long-term glycemic status. More importantly, the characteristics of immediate feedback can avoid delaying the diagnosis and treatment of diabetes. The boronate affinity-based method and the immunoassay method are the two methodologies currently used in commercially available HbA1c POC devices. Given the advantages of being relatively simple to operate, easy to miniaturize as well as its high sensitivity, the electrochemical HbA1c biosensors can be potentially translated for clinical use.  
Other noninvasive technologies have emerged to supplement current blood glucose and HbA1c measurements for diabetes diagnosis and management. For example, [PERSON] developed a QCM sensor by an electronic breath-analysis system to detect the amount of acetone in exhaled breath for indirectly determining HbA1c levels and blood glucose for 30 patients' samples [76]. The average accuracy rates for HbA1c level and blood glucose predictions are approximate 83% and 75%, respectively. With an expected global market of up to US $12.2 billion by the end of 2017, blood glucose monitoring occupies the top share in the POC market [77]. Raman scattering has also become a promising tool for noninvasive, continuous tracking of blood glucose level [78; 79]. By exploiting an improved concentration independent calibration approach, [PERSON] and coworkers performed a longitudinal tracking of blood glucose which exhibited a 35% reduction in error compared to a conventional calibration method [79].  
The integrations of affinity- and immunoassay-based HbA1c biosensors with emerging cell phone-based technology and LOC platforms, supplemented by noninvasive blood glucose monitoring provide a highly promising interface for the design of new generation of personalized HbA1c POC devices [77].
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 3 Proposed Architecture
### Interface Structure  
The data port of the PCCNoC interfaces can transfer two types of data. The first type is the control packet. There are four kinds of control packet: (1) route packet which is used to establish and lock routing path, (2) broadcast packet which used for global reconfiguration, (3) multicast pacekt, (4) short packet for small data transmission. The control packet transmission mechanism is similar with the conventional packet switching network, which routes the packet to the routing target by node. The second type is payload, which packs all messages to be transmitted and transmits them quickly on the locked path through circuit switching. The two types of data are transmitted use the same physical resource. If the link is locked, the data is directly forwarded by the circuit. If it is not locked, the router controller will parse the data according to its contents.  
The interface is composed by four groups of ports. Each group contains a bandwidth configurable (N is configurable) data port and seven control ports. The control ports are: enable (EN), unlink (UL), overtime (OT), CRC error (CRC), transmission grant (TG), transmission refuse (TR) and broadcast indicator (BC). The detailed information for each part of interface are listed in Table 1. When constructing transmission circuit, the control bits belong to the same group are connected in different directions as shown in Figure 6.  
Figure 5: Schematic for datapath locker. The connected example shows that local-import is connected to west output and south inport is connected to north output.  
Figure 6: The port connection when router (0,0) establish a path with router (1,0). The green wires belong to a group and the black wires belong to another group. The green wires are the connected path. The bit width of wires are labeled on wire. The interface structure of the other three directions are the same.  
As shown in Figure 7, there are four different types of packets distinguished by the 2 bits at header: (1) a route packet, which is used to establish a transmission path; (2) a short packet, which is used for short message transmission by packet switching without constructing transmission path on the way; (3) multicast packet which is used to constructed multiple transmission circuits; (4) broadcast packet that is used to transmit broadcast information. The \"m\" is related to NoC size \"M\". \"M\" is maximum between mesh row number and mesh column number.  
\[m=log_{2}/M \tag{1}\]  
In this design, the router could quickly transmit control information merely by increasing 7 bits of interface bit width, instead of adding a separate set of interfaces [31; 32]. The design of this work not only increases the flexibility of the interface, it reduces the complexity of the router design.
## 6 Results and Discussions
### Scenario 3: Rayleigh-Weibull Fading  
The outage probability vs. \(\hat{\gamma}\) for scenario 3 (Rayleigh-Weibull fading) for \((\gamma_{th})=1\) dB, 2 dB, 3 dB, 5 dB, and 7 dB is shown in Figure 4. System performance was evaluated for Weibull parameter \(c=1\), which represents multipath fading worse than Rayleigh fading.  
It was observed that the general system behavior as the threshold parameter \((\gamma_{th})\) varied was similar to that observed in Rayleigh-generalized Gamma fading and Nakagami-m-Weibull fading; thus, the performance worsened as \((\gamma_{th})\) increased. It was also observed that generally parallel behavior with different \((\gamma_{th})\) values but with the same \(c\) parameter  
Figure 4: Outage probability vs. average SNR for Rayleigh-Weibull fading.  
Figure 3: Outage probability vs. average SNR for Nakagami-m-Weibull fading.  
was also exhibited in this scenario.In this scenario, one link was the Rayleigh-faded link and, by setting \(c<2\), the other link was made worse than Rayleigh fading. We observed that the performance of the system in this scenario, at \((\gamma_{th})=5\) dB, was similar to that of the Rayleigh-generalized gamma fading for \(m=v=1\), which was effectively the Rayleigh-Rayleigh fading scenario. We therefore observed that at lower values of \((\gamma_{th})\) fading in the system could be modeled as a Rayleigh-Rayleigh fading scenario.
Sensors_**2014**, _14_, 15981-16002; doi:10.3390/s140915981  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Image-Based Environmental Monitoring Sensor Application Using an Embedded Wireless Sensor Network**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{4}\)**  
\({}^{1}\) Department of Computer Information Communication Engineering, Hongik University,  
Sejong 339-701, Korea  
\({}^{2}\) Computer Science Department, University of California, Los Angeles, CA 90095, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Biology Department, University of California, Riverside, CA 92521, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-044-860-2622; Fax: +82-044-865-0460.  
_Received: 11 July 2014; in revised form: 14 August 2014 / Accepted: 25 August 2014 / Published: 28 August 2014_  
**Abstract:** This article discusses the experiences from the development and deployment of two image-based environmental monitoring sensor applications using an embedded wireless sensor network. Our system uses low-power image sensors and the Tenet general purpose sensing system for tiered embedded wireless sensor networks. It leverages Tenet's built-in support for reliable delivery of high rate sensing data, scalability and its flexible scripting language, which enables more-side image compression and the ease of deployment. Our first deployment of a pitfall trap monitoring application at the James San Jacinto Mountain Reserve provided us with insights and lessons learned into the deployment of and compression schemes for these embedded wireless imaging systems. Our three month-long deployment of a bird nest monitoring application resulted in over 100,000 images collected from a 19-camera node network deployed over an area of 0.05 square miles, despite highly variable environmental conditions. Our biologists found the on-line, near-real-time access to images to be useful for obtaining data on answering their biological questions.  
keywords: wireless sensor networks; image sensors; sensor applications; tenet, rcrt, cyclops, environmental monitoring +
Footnote †: journal: Journal of Sensors
## 3 Case I: Passive Eavesdropper  
### Passive Eavesdropper System Model  
The wiretap channel system models with the use of FD self-jamming and a passive eavesdropper are shown in Figures 4 and 5, where user A is operated in FD transmission mode to simultaneously receive the intended information message from user B and transmit  
Figure 3: Security gap.  
the self-jamming signal to destroy the decoding ability of user E, while user E just tries to listen and decode the message from user B. The transmission strategy of the proposed scheme is as follows. User B wants to send his encoded message \(\mathbf{x}_{B}\) to the legitimate receiver user A through the main channel \(\mathbf{h}_{BA}\), while passive eavesdropper user E tries to listen and decode user B's message through the wiretap channel \(\mathbf{h}_{BE}\). The received signals in the digital domain at user A and user E are given by the following:  
\[y_{A}[n] =y_{BA}[n]+y_{AA}[n]+w_{A}[n] \tag{2}\] \[=(\sqrt{p_{B}}\mathbf{x}_{B}*\mathbf{h}_{BA})[n]+(\sqrt{p_{A}} \mathbf{x}_{A}*\mathbf{h}_{AA})[n]+w_{A}[n];\]  
\[y_{E}[n] =y_{BE}[n]+y_{AE}[n]+w_{E}[n] \tag{3}\] \[=(\sqrt{p_{B}}\mathbf{x}_{B}*\mathbf{h}_{BE})[n]+(\sqrt{p_{A}} \mathbf{x}_{A}*\mathbf{h}_{AE})[n]+w_{E}[n];\]  
where \(\mathbf{w}_{A}\) and \(\mathbf{w}_{E}\) are the complex Gaussian background noise of the receiver channel of user A and user E, with \(\mathcal{CN}(0,\sigma_{A}^{2})\) and \(\mathcal{CN}(0,\sigma_{E}^{2})\), respectively, and \((*)\) is the convolution operation.  
The legitimate receiver user A obtains the signal \(\mathbf{y}_{A}\) and performs two possible decoding strategies to eliminate the SI component and obtain the estimation of the intended signal \(\mathbf{\hat{x}}_{Sol}\). First, it may use a classical blind scheme without feedback where the DSIC and decoding processes are independent, as presented in Figure 4. Second, it can use a more efficient scheme based on joint iterative blind channel estimation and decoding through feedback, as shown in Figure 5, which we call the blind feedback scheme. At the same time, user E also tries to listen to the transmission over the wiretap channel and only performs the equalization process and the classical SPA decoding process to obtain the original signal \(\mathbf{x}_{B}\).  
Next, we will briefly describe the joint iterative blind channel estimation and decoding processes, which were studied in [27].  
Figure 4: Blind scheme without feedback at user A in case of a passive eavesdropper.  
Figure 5: Blind feedback scheme at user A in case of a passive eavesdropper.
## 3 Results and Discussion  
### Apparent Appearance and SEM Analysis  
A comparison of the unaged and coupled aging specimens is shown in Figure 3. It illustrates that the surface of the unaged specimen was smooth and flat, while with increased aging time, the surface became progressively rougher and glossier, with localized yellowing and asphalt oxidation spots. Additionally, the specimen's profile morphology became more rounded due to thermal expansion as the aging time increased.  
Figure 4 shows SEM photographs of unaged samples and those aged for 5, 14, and 28 days. As seen in Figure 4a, the surface texture of the unaged self-adhesive membrane was uniformly distributed and densely structured, with almost no impurities. After coupled aging, the surface of the samples exhibited noticeable folds, increased agglomerates, and uneven distribution, primarily due to the gradual conversion of lighter substances, such as aromatics and other asphalt components, into asphaltene. As the aging time increased, the asphalt became progressively harder and more brittle, leading to the formation of microcracks due to internal stress.  
Figure 3: Apparent comparison between unaged and aged specimens.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 3 Results and Discussion
### FTIR Test Results
#### 3.2.1 Analysis of FTIR Test Results
The \(A\) value has a good linear relation with the SBS content, and the correlation coefficient is very close to 1, it is consistent with the results of existing studies [16; 17]. Both the \(A_{1}\) and \(A_{2}\) values can be used as the basis for determining the SBS content in modified asphalt with the correlation not being affected by the type of matrix asphalt. However, the standard equations of the two kinds of base asphalt are quite different, so the original matrix asphalt must be obtained when using this method to determine SBS content, and it cannot be replaced by matrix asphalt of different sources.
## 3 Proposed Methods
### Cloud-Based Detection Results Verification Mechanism  
Although the edge detection we proposed greatly improves the real-time detection, it inevitably brings the decrease of detection accuracy at the same time. However, through experimental analysis, we found that the decrease of detection accuracy is not for the whole video, but is more obvious in some busy segments. Based on this observation, we consider using a small amount of cloud computing power to detect these fragments, improving the overall detection accuracy with little impact on detection speed. Figure 9 shows the cloud calibration mechanism to improve the reliability of detection results.  
For each group edge detection results [\(EDR_{i}\), \(EDR_{i+T}\)] (\(EDR\): Edge Detection Result), the cloud detection model \(DC\) (Cloud Detector) re-detects its intermediate frame \(F_{center}\) (\(F_{i+T/2}\)). \(CDR_{center}\) (\(CDR\): Cloud Detection Result) denotes the result of re-detection. Since \(F_{center}\) is inferred from \(EDR_{i}\), \(EDR_{i+T}\), and motion layer, \(F_{center}\) could show the correctness of the edge detection model and the motion layer. Thus, if Fcenter is consistent with CDRcenter, the set of detection results could be inferred as correct. Otherwise, it means the result of DE (Edge Detector) or DC is incorrect. Then we let \(DC\) re-detect the first frame \(F_{i}\) and last frame \(F_{i+T}\) of the group to obtain \(CDR_{i}\), \(CDR_{i+T}\) and let the motion inference strategy proposed in 3.2 re-generate detection result [\(DR_{i}\), \(DR_{i+T}\)] (\(DR\): Detection Result)using \(CDR_{i}\), \(CDR_{i+T}\), and motion layer. The set of detection results is inferred as correct if the generated intermediate frame detection results \(DR_{center}\) is consistent with \(CDR_{center}\). Otherwise, the extraction of the motion layer is inferred as incorrect, since the motion of the objects in the segment is not linear. In this case, let \(DC\) detects this group of video frames one by one, and use the obtained result [\(CDR_{i}\), \(CDR_{i+T}\)] as the final detection result of this video segment. Figure 10 shows the number of frames detected in the cloud for a segment of a T frame in different situations.  
Figure 8: Example of object motion matching based on T frame dynamic and static separation (T = 4 s).  
Figure 9: Cloud-based detection results verification and correct mechanism.