esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Method
### Post-Processing  
#### 3.3.1 Needle Original Length  
In the experiment, we employ the YOLOv7 algorithm to train the needle recognition model and utilize the diagonal length of the recognition frame to estimate the length of the needle. The green line represents the length of the injection needle in Figure 13.  
To ensure the precision of the original length detection, it is imperative to verify the operator's injection process at this stage. Thus, four experimental parameters were defined to obtain precise information at this point, namely:  
\[\begin{split} Ts&:Start\;time,\\ Tn&:Needle\;puncture\;the\;skin\;time,\\ Tr&:Reverse\;blood\;generated\;time,\\ Te&:End\;time.\end{split} \tag{1}\]  
By utilizing these parameters, the operator's injection training can be effectively categorized into three distinct phases, preparation time, operation time, and processing time.  
Figure 12: Definition of the needle length—from the needle site of the syringe to the front of the blue-winged handle.  
Figure 13: Calculation of the length of the needle.  
\[\begin{split}\text{Preparation time}&=Tn-Ts,\\ \text{Operation time}&=Tr-Tn,\\ \text{Processing time}&=Te-Tr.\end{split} \tag{2}\]  
After establishing the preparation time, our approach is to determine the original length of the needle by utilizing the bounding box information extracted by YOLOv7 during the preparation time. In order to establish the minimum required operating time for medical students with limited clinical experience, we relied on data obtained from 17 experts, each with at least three years of clinical injection experience. Ultimately, we were able to gather reliable results from 40 injections. The data collected from these experts was analyzed and the findings are presented in Figure 14.  
As illustrated in Figure 14, optimizing the system's practicality entails utilizing the operator's minimal operation time and ensuring it remains within the preparation time stage, thereby ensuring precise calculation of the original needle length. Failing to enforce a minimum operation time could result in some operators initiating skin puncture and moving into the second stage (operation time), leading to inaccurate length calculations. To address this issue, a minimum operation time of 0.694 s was implemented in this study. Moreover, considering that the camera used in this experiment recorded at 30 frames per second (FPS), we chose the bounding box at the 20 th frame to compute the original needle length.
###### Abstract  
For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time coding scheme in multiple-antenna systems was considered. This scheme allowed spreading, multiplexing, and allocating information symbols associated with multiple transmitted data streams. We showed that the received signal was formulated as a third-order tensor satisfying a Tucker-2 model, and then a robust semi-blind receiver was developed based on the optimized Levenberg-Marquardt (LM) algorithm. Under the assumption that the instantaneous channel state information (CSI) is unknown at the receiving end, the proposed semi-blind receiver jointly estimates the information symbol and channel parameters efficiently. The proposed receiver had a better estimation performance compared with existing semi-blind receivers, and still performed well when the channel became strongly correlated. Moreover, the proposed semi-blind receiver could be extended to the multi-user massive multiple-input multiple-output (MIMO) system for joint symbol and channel estimation. Computer simulation results were shown to demonstrate the effectiveness of the proposed receiver.  
multiple-antenna systems; third-order tensor; Tucker-2 model; semi-blind receiver; optimized LM algorithm +
Footnote †: journal: Electronics Letters
# Smart Patch for Structural Health Monitoring of Composite Repair
###### Abstract  
The bonding integrity of a repair patch to the parent composite laminate is considered the most important factor in the repair design. A smart repair patch is proposed here to allow for real-time ultrasonic guided wave monitoring of repaired composites. A diagnostic film with lead zirconate titanate (PZT) transducers and inkjet-printed wires is embedded into the repair patch using a cut-out method. The electro-mechanical impedance (EMI) method is used to verify the integrity of the embedded PZT transducers. The performance of the smart repair patch is assessed on the external panel with artificial bondline delamination and surface-mounted artificial damage. The damage index correlation coefficient and delay-and-sum (DAS) algorithm are used for damage detection and localization. The results show that the developed repair patch can successfully detect and locate damages.  
s mart repair patch; structural health monitoring (SHM); embedded PZT transducers; damage detection and localization; delay-and-sum (DAS) algorithm
# EXPRESS: Exploiting Energy-Accuracy Tradeoffs in 3D NAND Flash Memory for Energy-Efficient Storage
## 4 Experimental Evaluation
### Data Retention Effects  
Data retention is an essential consideration for nonvolatile flash memories. The charge stored on the FG/CT of the flash cells tends to leak out through the tunnel oxides at room temperature, lowering the cell threshold voltage over a period of time [31; 32]. Hence, flash memory manufacturers keep wider voltage margins between the program \(V_{t}\) and the read reference voltage in order to guarantee long-term data retention (\(\sim\)10 years for many products). Since EXPRESS trades off the voltage margin for improved energy efficiency, it is important to characterize the data retention time.  
Figure 13 summarizes the results of an experiment that explores the effects of EXPRESS on the data retention for both the SLC and MLC modes of operation on PE-cycled blocks. It shows the bit error rate of the data written by EXPRESS (red bars), and the data written by the nominal program operation (blue bars). To accelerate the retention loss, we bake the chip at a higher temperature (120 \({}^{\circ}\)C) for 1, 2, or 3 h. Using the acceleration-factor-based  
Figure 12: (**a**) Cumulative distributions of page program times for SLC pages in fresh (solid blue line) and 10K-PE-cycle (dashed blue line) conditions. (**b**) Cumulative distribution of page program time for LSB pages in fresh (solid blue line) and 5K-PE-cycle (blue dashed line) conditions, and MSB pages in fresh (solid red line) and 5K-PE-cycle (dashed red line) conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Storage Mode** & **Page Type** & \(t_{prog}\)(\(\mu\)s) & \(n\) & \(t_{pp}\)(\(\mu\)s) & \(n_{skip}\) & **Average BER** & **Average Energy** \\  & & & & & & [\%] & **Saved [\%]** \\ \hline \multirow{3}{*}{**SLC-10K**} & Slow & 467 & 4 & 300 & 1 & \(9\times 10^{-4}\) & 35.78 \\ \cline{2-7}  & Nominal & 320 & 3 & 220 & 1 & \(5\times 10^{-5}\) & 31.29 \\ \hline \multirow{3}{*}{**LSB-5K**} & Slow & 1001 & 9 & 760 & 2 & \(2\times 10^{-1}\) & 24.12 \\ \cline{2-7}  & Nominal & 561 & 5 & 300 & 2 & \(1\times 10^{-2}\) & 46.56 \\ \hline
**MSB-5K** & Nominal & 1600 & 12 & 1330 & 2 & \(5\times 10^{-1}\) & 16.87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: EXPRESS characterization for NAND flash block after PE cycles.  
calculation, we find that the 3 h of baking time corresponds to 5 years at room temperature, assuming the activation energy for the charge loss in the 3D NAND as \(E_{A}=1\) eV [33]. The results in Figure 13 show that the BERs for the EXPRESS write increase relative to the traditional programming, after the accelerated retention test. The temporary read error is a new reliability issue in 3D NAND Flash [34; 35]. It is not considered in this case, as the BERs are <1% for all the types of pages and, hence, can be corrected using standard error-correction techniques [36; 37; 38].
## 3 Results  
### Scatter Plots and Linear Regression Analysis Results  
Table 4 shows the sum of the measured FSR sensor values (ASUM) as a scatter plot where each experimenter (A-E) wearing smart safety shoes is not holding a heavy object (body weight+0 kg) and when each is holding a different weight of heavy objects (body weight +5 kg, +10 kg, +15 kg, +20 kg). As shown in Table 4, when the weight of the heavy object increases for each experimenter, the sensor values tend to increase slightly, even though it cannot be said to appear clearly because of the wide distribution of sensing data values. The wide distribution of the sum of the sensor values (ASUM) measured by both smart safety shoes for each weight occurs due to the characteristics of sensing values  
Figure 2: An analysis model using Machine Learning (ML). LSUM: Conversion value of SumRawL to voltage; RSUM: conversion value of SumRawR to voltage; ASUM: sum of LSUM and RSUM.  
having the potential to react sensitively, depending on the state of how the experimenter is wearing the smart safety shoes or the degree of grounding on the floor.  
To analyze their correlation in more detail, a univariate multiple linear regression was performed as shown in Table 5. Herein, the independent variables were designated as the sum of sensing data values collected from both the left and right smart safety boots and the sum of all sensing data values (LSUM, RSUM, ASUM); the dependent variable was designated as the differential weights (body weight +0 kg, +5 kg, +10 kg, +15 kg, +20 kg). As a result, it was found that the R2 values for each experimenter were at the lowest level of 25%, the highest 69%, and the average level of 46%, indicating that the degree of explaining the correlation is somewhat low. The statistical analysis results imply that the accuracy is not very high in classifying how much weight is carried by construction workers wearing smart safety shoes.
## 3 Reliability Models of WSNs
### Basic Definitions  
Prior to present the reliability models of a WSN, it is necessary to characterize the elements that we are considering in the modeling process. Figure 5 shows these elements and their relationships. Every WSN is composed by simple sensor nodes, sink nodes and communication links as introduced in Section 2.1. In addition to these elements, we adopt the notion of \"WSN region\", which consists of a set of WSN nodes that sense the same physical phenomenon, and \"WSN path\" that represents a logical way (a set of WSN nodes and links) from a WSN node belonging to a particular region and the sink node. At this point, it is worth observing a key aspect in our approach: the WSN path depends on the routing algorithm being used. Hence, several WSN paths may exist between a particular node and the sink node and their reliability may be different.  
By using these elements, we defined a strategy for modeling them in RBD. Firstly, each RBD model has a source (starting point) and a target (ending point); in WSNs, the starting point is any region in the network, whilst the ending point is always the sink node. Secondly, for reliability purpose, the WSN is divided into regions and its reliability is computed individually for each region. This division is justified because the reliability of a region is affected by four factors: positions of the WSN nodes that make up the region, routing algorithm used, reliability of the WSN nodes, and reliability of WSN links. Finally, each aforementioned element has associated a reliability model,  
Figure 5: WSN Elements considered in the modeling process.  
which leads to the definition of Basic reliability blocks (used to model WSN nodes and links), Region model (to express the reliability of a WSN region) and a Path model (to specify the reliability of a WSN path). Figure 6 presents a step-by-step diagram of the proposed strategy.  
Starting from a WSN configuration (topology and adopted routing algorithm), it is necessary to manually define a WSN region to be evaluated. Next steps are performed automatically (by the developed tooling) and consist of create WSN paths for each sensor nodes belonging to the region being evaluated. With the paths already defined, next step generates reliability models for each of them. Last step consists of composing the path models to produce the reliability model of the WSN region. Again, it is worth observing that the nodes that compose a path depend on the routing algorithm being utilized in the WSN. The following subsections present details of Basic block, Path Models and Region Models.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Fabric-Based Graphene ECG Electrodes
#### 2.2.1 Cotton-Textiles-Based Graphene ECG Electrode  
At present, the most commonly used ECG electrodes are made of silver/silver chloride (Ag/AgCl) with a hydrogel layer. Although this kind of electrode is inexpensive and has small electrode-skin resistance, its gel layer may cause skin allergies. Further, during long-term ECG monitoring, the water content of the gel decreases, increasing electrode-skin contact resistivity. To overcome the skin allergy and gel-drying problems in long-term ECG monitoring, [PERSON] et al. proposed a shuttle cotton textile-based ECG electrode [45]. Lightweight (130 g/m\({}^{2}\)) commercial cotton textiles with fabric diameters ranging from 10-20 um were used as the substrate for fabricating graphene-coated ECG electrodes. The surface of the textile was first coated with a polymer layer by heat transfer (180 \({}^{\circ}\)C, 10 s) to enhance the roughness of its surface and increase the adhesion between cotton fibers and graphene nanoplates. Then, screen-printing was used to tightly combine the conductive graphene patterns with the textile substrate, making the electrodes washable for long-term repeated use (Figure 2a). The prepared textile electrode shows a low resistance of \(\sim\)104 \(\Omega\)/square, and the acquired ECG signals are comparable with Ag/AgCl electrodes. Besides, the cotton textile substrate allows the electrode to continue to obtain high-quality ECG signals after washing. Moreover, as a high-throughput fabrication method, the produced cotton-textiles-based graphene ECG electrodes are low-cost, durable, and suitable for daily ECG monitoring for the consumer health market.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 3 Experiments
### Data Collection
Was the ATC accurately communicating with the RPAS pilot and responding to RPAS requests? Q12. Was the RPAS operation negatively impacted by the interaction with the ATC? Q13. Was the RPAS accurately flying the predefined contingency trajectory? The level of accuracy depends on the type of contingency, with the requirements for lost link being much stricter than those required for engine failure. Accuracy will be agreed during the pre-briefing. Q14. Was the RPAS ADS-B/transponder properly indicating its contingency state? The question is employed to keep track of potential malfunctions of that element during the simulations. Q15. Was the RPAS flight intent properly provided and accurate with the actual trajectory? The question is employed to keep track of potential malfunctions of that element during the simulations. Q16. Did the flight intent improve the overall situational awareness of the ATC with respect to the RPAS contingency operation and beyond the existing awareness available through the mission pre-planning? Q17. Was the RPAS impact on the traffic performance excessive; i.e., the surrounding traffic was impacted negatively from their desired trajectories? Q18. Was the RPAS impact on the ATC workload excessive with respect to the workload induced by similar contingencies in manned aviation?
## 3 Results and Discussion
### Antioxidant Activity Evaluation  
#### 3.3.1 Antioxidant Capacity by Chemiluminescence  
Chemiluminescence data were quantified by calculating the evolution of the chemiluminescence signal over time in the presence of 10% hydroalcoholic extractive solutions of _Medicaginis herba_, _Trifolini pratense flores_, _Gingko bilobae folium_, _Myrtill fructus_, and _Cynos-bati fructus_ and the antioxidant product (Table 6).  
From the analysis of the antioxidant activity values (AA%), it can be observed that the hydroalcoholic extract of _Myrtillis fructus_ has the most pronounced antioxidant activity (AA% = 92.1), followed by the hydroalcoholic extractive solution of _Trifolini pratense flores_ (AA% = 59.24), _Medicaginis herba_ (AA% = 63.55), and _Cynosati fructus_ (AA% = 44.86). The hydroalcoholic extractive solution of _Ginkgo bilobae folium_ has an average AA% = 43.47. The antioxidant product CILTAG shows an antioxidant activity (AA%) = 86.16, which is slightly diminished compared with the hydroalcoholic extractive solution of _Myrtillis fructus_ due to the values of the other components (Figures 2 and 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analyzed Product** & \(\mathbf{I_{p}}\) & **AA\%** & **V (5 sec)** \\ \hline _Medicaginis herba_ & 726.6 & 63.55 & 145 \\ _Trifolini pratense flores_ & 808.8 & 59.24 & 162 \\ _Gingko bilobae folium_ & 1081 & 43.47 & 216 \\ _Myrtillis fructus_ & 140.6 & 92.1 & 28.1 \\ _Cynosbait fructus_ & 1029 & 44.86 & 206 \\ _Antioxidant product CILTAG_ & 227.5 & 86.16 & 45.5 \\ \hline \hline \end{tabular} \(\mathrm{I_{p}}=\mathrm{chemiluminescence}\) signal intensity in the presence of the studied solutions at t = 5 s; AA% = antioxidant activity; V (5 sec) = speed of the chemiluminescent signal after the first 5 s.  
From the analysis of the antioxidant activity values (AA%), it can be observed that the hydroalcoholic extract of _Myrtillis fructus_ has the most pronounced antioxidant activity (AA% = 92.1), followed by the hydroalcoholic extractive solution of _Trifolini pratense flores_ (AA% = 59.24), _Medicaginis herba_ (AA% = 63.55), and _Cynosati fructus_ (AA% = 44.86). The hydroalcoholic extractive solution of _Ginkgo bilobae folium_ has an average AA% = 43.47. The antioxidant product CILTAG shows an antioxidant activity (AA%) = 86.16, which is slightly diminished compared with the hydroalcoholic extractive solution of _Myrtillis fructus_ due to the values of the other components (Figures 2 and 3).  
\end{table}
Table 6: Values of the chemiluminescent signal as a function of time and of the antioxidant activity in the presence of hydroalcoholic solutions.  
Figure 2: Comparative histogram of the antioxidant activity values of the studied solutions.  
The reaction rate is consistent with the variation in the antioxidant activity. Thus, the most active compound _Myrtillis fruct_ has the fastest reaction rate.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 6 Conclusions  
In this paper, we propose a millimeter wave near-field SAR imaging algorithm called mmSight. The original data are optimized using Blackman window, which facilitates the mapping of multiple targets at different distances. The optimized data are processed using Analytic Fourier Transform algorithm, which can effectively avoid ghost image. The imaging results are processed using mean filtering to remove spatial noise. The penetration of millimeter wave is used to achieve the detection of hidden targets. In the IE of point target imaging results, mmSight obtained a value of 4.6157, which is 0.2267 lower than the Analytic Fourier Transform, 0.3919 lower than the Back Projection, and 0.3932 lower than the Matched Filter. For PSF, mmSight's sidelobe and noise are smaller than those of other algorithms. The experimental results show that mmSight can achieve robust, high-quality imaging under near-field conditions compared to other common SAR imaging algorithms. In the future, we hope to further promote the miniaturization of RF imaging devices, and further study real-time, high-resolution, no outside device-dependent, material-independent imaging, and further study the effect of irregular sampling on imaging, and try to further expand the application scope of millimeter wave imaging technology.  
Conceptualization, [PERSON] and Z.H.; methodology, R.W.; software, R.W.; validation, R.W. and J.P.; formal analysis, R.W.; investigation, R.W. and H.Y.; resources, Z.H. and X.D.; data curation, R.W. and J.P.; writing--original draft preparation, R.W.; writing--review and editing, R.W. and Z.H. and H.Y.; visualization, R.W.; supervision, Z.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant 62262061, Grant 62162056), Key Science and Technology Support Program of Gansu Province (Grant 20 YFBGA048), 2019 Chinese Academy of Sciences \"Light of the West\" Talent Program, Science and Technology Innovation Project of Gansu Province (Grant CX2 JA037, 17 CX2 JA039), 2019 Lanzhou City Science and Technology Plan Project (2019-4-44), 2020 Lanzhou City Talent Innovation and Entrepreneurship Project (2020-RC-116, 2021-RC-81), and Gansu Provincial Department of Education: Industry Support Program Project (2022 CYZC-12).  
Not applicable.  
The authors would like to thank the reviewers for their thorough reviews.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Device-Portable** & **Real-Time** & **High-Resolution** & **Out-Device Independent** & **Material-Independent** \\ \hline
[4] & Yes & Yes & No & Yes & No \\ \hline
[33] & Yes & Yes & Yes & No & No \\ \hline
[34] & Yes & Yes & Yes & No & No \\ \hline \end{tabular}
\end{table}
Table 5: Future trend from related work.