esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Gene Structure and Conserved Motif Analysis  
To better understand the gene structure and evolutionary relationships, the intron-exon structure of each _RcTPS_ gene was analyzed (Figure 4). The number of exons ranged from 5 to 14, and most of the _RcTPS_ genes that clustered in the same subfamily showed a similar gene structure. _RcTPS31_ and _RcTPS49_ had the highest number of exons (14 exons), whereas _RcTPS40_ contained the fewest exons (5 exons). Except for _RcTPS07_, _RcTPS27_, _RcTPS35_, _RcTPS40_ and _RcTPS41_, all 44 remaining _RcTPS_ genes of the subfamilies TPS-a, TPS-b and TPS-g contained 7 coding exons. Moreover, the _RcTPS_ genes in the TPS-e/f subfamily contained more exons (10-14), which is similar to previously reported data in _V. vinifera_, _D. offincale_ and _E. grandis_[19; 23; 27].  
Figure 3: Phylogenetic analysis of TPSs in _R. chinensis_, _A. thaliana_ and other species. Subfamilies are highlighted with different colors. Species: _R. chinensis_ (Rc); _A. thaliana_ (At); _S. lycopersicum_ (SI); _M. domestic_ (Md).  
To further clarify the functional characteristics and specificities among the RcTPS proteins, 10 conserved protein motifs were identified using the MEME motif search tool. Each identified motif contained between 21 and 50 amino acids (Figure S1). Most RcTPS proteins in the same subfamily shared similar motif structures (Figure 5). Motif-1, motif-4, motif-6 and motif-8 were found in all RcTPS proteins. Motif-1 harbors an aspartate-rich region, DDXXD, representing the most typical conserved motif contained in the C-terminal of TPS proteins. Motif-7 was another important motif of the C-terminal domain in TPS proteins, which could be found in almost all RcTPS proteins of the subfamilies TPS-a, TPS-b, TPS-g and TPS-e/f. Previously, this motif, characterized by the consensus sequence (L,V)(V,L,A)(N,D)D(L,I,V)X(S,T)XXXE, was named NSE/DTE [62]. Both the DDXXD and NSE/DTE motifs are known to play an important role in cleaving prenyl diphosphate substrates by binding Mg\({}^{2+}\) or Mn\({}^{2+}\) at the C-terminus [1; 14; 15]. In rose TPSs, the DDXXD motif shows a higher level of conservation than the NSE/DTE motif. In addition, the arginine-tryptophan motif RRX\({}_{8}\)W (motif-9) was found in the N-terminus of all RcTPS-a and RcTPS-b proteins. This motif is known to play an essential role in the catalysis of monot  
Figure 4: Exon–intron structure analyses of RcTPS genes. The blue and grey boxes indicate CDS and UTR, respectively. Lines indicate introns.
## 5 Discussions  
### Findings and Contributions  
#### 5.1.1 Energy-Efficient IoT Monitoring System  
This paper introduces a novel low-cost IoT system designed for natural disaster monitoring. This study primarily focuses on power consumption in IoT devices and proposes methods to minimize it, both in the context of microcontroller processing and network processing. The system's architecture comprises IoT devices; all hardware components are affordable and readily available. Compared to commercial IoT solutions, the system is cost-effective. Additionally, the system utilizes a task scheduling technique that reduces power consumption, and the deep sleep delay function further reduces energy consumption. Finally, a solar cell system that powers a rechargeable battery is the power source. These features make the system an excellent low-cost solution for natural disaster monitoring.  
From a hardware perspective, using a solar cell system as a power source further reduces the system's overall cost and environmental impact. Overall, the proposed system's simplicity, affordability, and energy efficiency make it a promising solution for natural disaster monitoring in resource-limited settings. Choosing appropriate hardware is crucial in reducing energy consumption and extending the lifetime of IoT devices. Even if hardware options are limited, implementing effective task scheduling and delay techniques can still help reduce power consumption. However, these techniques are just the beginning of power-aware design.  
The choice of low-power microcontrollers, sensors, and transmission devices can also significantly impact overall power consumption. The batteries selected must be both easily accessible and cost-effective within the local market. In addition to hardware, software-level solutions such as low-power real-time operating systems, task and transmission scheduling algorithms, and power management techniques can further reduce power consumption and extend the lifetime of IoT devices.
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
## 4 Experimental Set Up
### Location of the Generated Sources and Sensors  
In the PLB tests, the used pencil leads were 0.5 mm and 2 H in diameter and hardness, respectively. Moreover, the locations of the PLB tests are shown in Figure 8. In Figure 8, white dots imply the sources generated by PLB tests located with intervals of 100 mm. Thus, the number of total PLB points is 49, and it is expected that 49 sources are localized. The localized sources are shown in the xy orthogonal coordinate system, and the original point is defined as S1. In the orthogonal coordinate system, the locations of sensors are listed in Table 1. The analytic model is defined as the rectangle consisting of the vertexes, which are S1, S4, S9, and S12. Although the number of sensors should be increased for greater accuracy of the source localizations, the computational costs of the SOM are increased with a large number of input vectors for the classification of elastic waves. Owing to the conservation of computational costs, the selection of sensors is required. Furthermore, if the number of classes of input vectors has a bias, it is expected that having several input vectors does not contribute to the visualization of classes since the map is updated on the basis of input vectors computed from waveforms. If diffraction waves are measured in the limited sensors, since a defect is local in a specimen, the number of diffraction waves is particularly smaller in comparison with all of the measured waves. It is challenging that a small number of diffraction waves forms the class wherein diffraction waves belong. Therefore, the measured waves should be selected to be applied to the SOM. In this model test, the SOM is conducted with artificial AE signals measured at the sensors located in the vertexes of the rectangle shown in Figure 8. Although the locations of diffraction waves arrivals are unknown because identifying locations of failures generated inside of structures is challenging, it is expected that the sensors located in the vertexes can measure diffraction waves if the model is defined as a rectangle. Furthermore, the appropriate number of straight propagation waves are expected to be applied to the input vectors because the number of straight propagation waves is decreased in waves used in the SOM. Hence, measured waves at all of the vertexes have potential to form the class wherein diffraction waves belong.  
Figure 7: Illustration of measurement trigger and detected arrival time by AR-AIC.
# Using Optimization Techniques in Grammatical Evolution
## 2 The Proposed Method
### The Modified Simulated Annealing Algorithm  
The current work utilizes a modified version of the Simulated Annealing algorithm as a local search procedure. Simulated Annealing was chosen as a local search method, since it offers the possibility of representing the considered solutions in an integer form, which is critical for the Grammatical Evolution representation of chromosomes. In addition, this method has been distinguished for its easy adaptation to a multitude of problems but also for its ability to find the total minimum of functions through the point acceptance mechanism at high temperatures. In the present work, the Simulated Annealing will initiate from the current representation of a chromosome and gradually try to find other nearby representations that might lead to lower values of the fitness value. The main steps of this procedure are shown in Algorithm 1.  
```
procedure\(\text{siman}(x_{0})\)
1. Set \(k=0\), \(T_{0}>0\), \(e>0\), \(r_{T}>0\), \(r_{T}<1\)..
2. Set \(N_{eps}>0\), a positive integer indicated the number of samples drawn in every iteration.
3. Set \(g\) and \(R\), positive integer values.
4. Set \(x_{b}=x_{0},f_{b}=f(x_{b})\).
5. For \(i=1\ldots N\text{eps}\) 1. Set \(y=x_{k}\) 2. For \(j=1\ldots g\) 3. Set \(p=\text{rand}(1,\text{size}(x_{0}))\). The variable \(p\) indicates a randomly selected position in \(y\). 3. Set \(y_{p}=y_{p}+\text{rand}(-R,R)\) 4. EndFor 5. If \(f(y)\leq f(x_{k})\) then \(x_{k+1}=y\) 6. Else Set \(x_{k+1}=y\) with probability \(\text{min}\Big{\{}1,\exp\Big{(}-\frac{f(y)-f(x_{k})}{T_{k}}\Big{)}\Big{\}}\) 7. If \(f(y)<f_{b}\) then \(x_{b}=y,f_{b}=f(y)\). EndFor 8. Set \(T_{k+1}=T_{k}r_{T}\) 9. Set \(k=k+1\). If \(T_{k}\leq\epsilon\) terminate 10. Goto step 5. 11. Return \(x_{b}\) endsim
```  
**Algorithm 1** The modified version of the Simulated Annealing algorithm  
The method accepts the chromosome \(x_{0}\) as a starting point and, in each iteration, randomly generates chromosomes around it. The parameter \(g\) controls the number of changes that will be made to the chromosome and the parameter \(R\) controls the radius of these changes. The parameter \(T\) represents the temperature of the algorithm. The temperature starts at extremely high values and progressively decreases linearly. In the early stages and at high temperatures, the algorithm may accept points that may have higher values of the fitness function, but as the temperature decreases, this probability also decreases. The used Simulated Annealing variant is also shown graphically as a flow chart in Figure 2.
## 3 New Approach for the Slowing Down Detection and Comparison with On-Line Estimator at AUG
### Comparison Between the New Established Estimators and Criterion for Real Time Application  
The first immediate and practical use of the estimator deals with the detection of a drop of the rotation of a mode so steep to lead to locking. To perform this task, the following criterion has been set:  
\[Q\;=\;\left\{\begin{array}{l l l}\mbox{\it true}&\quad\mbox{\it if}&\quad f (t_{i})<mean\big{(}f(t\in[t_{0},t_{i-1}])\big{)}-Z\cdot std(f(t\in[t_{0},t_{i- 1}]))\\ false&\quad\mbox{\it if}&\quad f(t_{i})\geq mean\big{(}f(t\in[t_{0},t_{i-1}]) \big{)}-Z\cdot std(f(t\in[t_{0},t_{i-1}]))\end{array}\right. \tag{8}\]  
Equation (8) represents the actual implementation of the estimator (6) for the detection of the slowing down of the rotating modes with \(n=1\). Evaluating (6) at each time instance, if the value \(t_{i}\) falls below a threshold that is equal to the average value of the frequencies measured in the time interval from \(t_{0}\) to \(t_{i}\), minus Z times the standard deviation of the values in the same time interval described, then the mode is considered as locked. The criterion in (8) is robust enough to follow the dynamics of the mode, without suffering of sporadic fluctuations, but at the same time is also enough sensible to the occurrence of significant changes due to an actual increase or decrease of the rotation of the mode.  
This rule has been compared with the threshold based indicators used at AUG. An illustrative example of the tests performed has been reported in Figure 7.  
Figure 7 reports the estimator in (6) in blue. Considering the vertical lines, standing for the time instances at which the different estimators considered have triggered an alarm and in red the time instance at which the disruption occurred, it can be observed how the described estimator increases the possibility of detecting earlier in time the presence of a locked mode with respect to the ones actually used in CR. The parameter Z has been set to \"\(-4\)\" to increase the confidence value of the detection.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 6 Results and Evaluation
### Parameter Analysis
#### 6.4.3 Incidence Angle of the Signal  
Many studies have shown that, when the incident direction of the antenna is perpendicular to the plane of the tag, the effective aperture of the tag is the largest and the tag receives the most energy. When the incident direction is parallel to the tag plane, the energy is at its least. We place the antenna in the middle of the symmetrical tags array to explore the difference between tags. The experimental results show that the localization error of the tags placed in the middle is smaller than that at the side. During the rotation of the antenna, the angles of the signals received by the tags at the side are larger, and the energy absorption is more sufficient. Therefore, as shown in Figure 8, the peak of the RSSI curve is flat and it is difficult to distinguish the maximum value. However, the RSSI curve of the tags in the middle immediately drops after reaching the highest point. The judgment is more accurate and ultimately leads to the difference in localization error.  
Figure 19: Distance between antenna and tags array vs. Error.  
Figure 18: Distance between tags vs. Error.
###### Abstract  
We propose a stacking method for ensemble learning to distinguish micro-Doppler signals generated by human walking from background noises using radar sensors. We collected micro-Doppler signals caused by four types of background noise (line of sight (LoS), fan, snow and rain) and additionally considered micro-Doppler signals caused by human walking combined with these four types of background noise. We firstly verified the effectiveness of a fully connected deep neural network (DNN) to classify 8 types of signals. The average accuracy was 88.79% for the test set. Then, we propose a stacking method to combine two base classifiers of different structures. The average accuracy of the stacking method on the test set was 91.43%. Lastly, we designed a modified stacking method to reuse feature information stored at the previous stage and the average test accuracy increased to 95.62%. This result shows that the proposed stacking methods can be an effective approach to improve classifier's accuracy in recognizing human walking using micro-Doppler signals with background noise.  
Keywords:human detection; radar sensor; radar machine learning; stacking learning; micro-doppler signal +
Footnote †: journal: Applied
## 5 Experimental Results
### Experimental Configuration  
In all experiments, we embedded a 256-bit watermark \(b\) in the second-to-last layer of the target model, following the approach used in DeepSigns. This watermark could represent, for example, the hash of the model owner's identifier generated using the SHA-256 hash function. We used the bit error rate (BER) to measure the discrepancy between the original and extracted watermarks:  
\[\text{BER}(b^{\text{ext}},b)=\frac{1}{n}\sum_{i=1}^{l}I(b_{t}^{\text{ext}}\
eq b _{i}) \tag{25}\]  
where \(b^{\text{ext}}\) is the extracted watermark, \(I\) is the indicator function returning 1 if the condition is true and 0 otherwise, and \(l\) is the watermark size (\(l=256\)). A BER close to 0 indicates identical watermarks, while a BER close to 0.5 indicates uncorrelated watermarks.  
**DICTION configuration:** We used a simple two-layer fully connected neural network as the architecture for the DICTION projection function \(\text{Proj}_{\theta}\) in our experiments. The normal distribution \(N(\mu,\sigma)=N(0,1)\) serves as a latent space to embed the watermark, and the selection matrix \(Z\) serves as the identity. We used the Adam optimizer with a learning rate of \(10^{-3}\), a batch size of 100, and a weight decay of \(10^{-4}\) to train the DICTION projection function. We set \(\lambda=1\) in Equation (18). We used 10 epochs for each benchmark to embed the watermark.  
**Baseline method configurations:** Regarding the implementation of DeepSigns, we used the second-to-last layer for embedding, as in its original version. \(\lambda_{1}\) and \(\lambda_{2}\) were set to 0.01 to obtain a BER equal to zero. The number of watermarked classes \(s\) equals 2, \(N\) was 128 (i.e., a watermark of 256 bits), and the watermark projection matrix \(A\) was generated based on the standard normal distribution \(N(0,1)\). The trigger set corresponds to 1% of the training data.  
For ResEncrypt, we used two-layer, fully connected neural networks as the architecture for MappingNet. We recall that the role of MappingNet is to expand the size of the selected weights to the watermark. In our implementation, we used an expansion factor equal to 2 for LeNet and 1 for the other benchmarks.
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 5 Simulation Experiment Results and Analysis
### CFVI-CFAR Detection Performance  
VI-type detectors can effectively improve the adaptability of detectors by selecting the appropriate detector based on the environment type, thereby improving detection performance. For example, In VIHECMOS-CFAR [13], the CA-CFAR detector is selected in a homogeneous environment, the modified order statistics CFAR detector is selected in a multiple-target environment, and the heterogeneous clutter estimate CFAR detector is selected in a clutter edge environment, thus demonstrating excellent overall detection performance. To verify the detection performance of the CFVI-CFAR, Monte Carlo simulation is used for comparative analysis with CA-CFAR [5], OS-CFAR [8], KLQ-CFAR [9] and VIHECMOS-CFAR [13] in a homogeneous environment, a multiple-target environment,  
Figure 3: The detection probability of each detector:when (**a**) there is one interference in the leading reference window, (**b**) there are three interferences in the leading reference window, (**c**) there is one interference in both the leading and lagging reference windows.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Environment** & **MCA** & **MAX-MCA** & **MIN-MCA** & **PRODUCTS-MCA** & **SUM-MCA** & **CFKLQ** \\ \hline One interference in the leading window & 53.44\% & 54.50\% & 54.43\% & 54.67\% & 55.43\% & 56.78\% \\ Three interferences in the leading window & 49.70\% & 53.64\% & 53.55\% & 53.81\% & 54.04\% & 55.04\% \\ One interference in both windows & 50.29\% & 53.85\% & 54.74\% & 54.08\% & 54.74\% & 56.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: The average detection probability of each detector in a multiple-target environment.  
and a clutter edge environment. The \(r_{1}\) and \(r_{2}\) of KLQ-CFAR are set to 6 and 3, the \(k\) of OS-CFAR is set to 24, \(K_{VI}\) = 4.76, \(K_{MR}\) = 1.806.
## 4 Conclusions  
The design and testing of a high-speed linear TIA targeting quasi-coherent receivers have been presented and discussed in detail. Circuit measurements have shown a 3 dB response in the range of 5 to 50 GHz, with a differential transimpedance of 68 dB\(\Omega\) and a maximum differential output swing of 896 \(mV_{pp}\) at 1 dB compression. A quasi-coherent receiver based on a discrete vertical PIN photodiode and featuring the designed TIA has been introduced, achieving a receiver sensitivity of \(-30.5\) dBm and \(-26\) dBm (BER = 1 \(\times\) 10\({}^{-3}\)) at 10 and at 25 Gbps, respectively. The proposed TIA has thus proven an enabling block towards highly integrated quasi-coherent receivers operating at high speeds.  
Conceptualization, G.S.V., J.B.J., and T.K.J.; methodology, G.S.V., J.B.J., and T.K.J.; resources, J.B.J. and T.K.J.; measurements and experimental results, G.S.V., M.K., and T.K.J.; writing--original draft preparation, G.S.V., J.A.A.; writing--review and editing, G.S.V., J.A.A., and T.K.J.; supervision, J.B.J. and T.K.J.; project administration, J.B.J. and T.K.J.; funding acquisition, J.B.J. and T.K.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the INCOM project (ID: 8057-00059B), partly funded by Innovation Fund Denmark.  
The authors would like to acknowledge [PERSON] for PCB mounting and additional support with the measurements.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data, and in the writing of the manuscript or in the decision to publish the results.
## 3 Results
### Efficiency of Object Detection
#### 3.3.3 Performance of vePOD YOLO  
As a single-stage privacy-preserving object-detection network, vePOD YOLO processes secret shares through a series of secure convolutional, secure max pooling, and secure fully-connected layers without generating region proposals. We also evaluate our implementation of vePOD YOLO on the COCO dataset.  
Figure 8 compares the performance of vePOD YOLO and the original YOLO using two and three edge servers. The dimension of input images to YOLO is the same as the dimension of image-level secret shares to vePOD YOLO. In the figure, we can see that the major performance difference between YOLO and vePOD YOLO is from the secure max pooling. Secure max pooling for two shares introduces 72.6 s computation latency and 4.1 s data-transmission latency, which takes 99.8% of total performance overhead.  
The execution time of secure max pooling, as shown in Figure 9, increases linearly with the size of the input feature map. The size of a feature map equals the product of the width, height, and the number of channels of the feature map. For example, vePOD YOLO's execution time with two edge nodes is 15.17 s with input size 56 \(\times\) 56 \(\times\) 512 (width, height, channel), and execution time for a feature map of input size 224 \(\times\) 224 \(\times\) 64 is 32.4 s. The execution time ratio equals the ratio of the total number of features in the input feature map because the secure function is executed on every pooling region iteratively with identical time complexity. VePOD Darknet CNN consists of four secure max-pooling layers, and their execution time varies. The ratio of the execution time between the four layers matches the ratio of the corresponding size of their input feature maps. The ratio of the four layers' execution time and feature map size is 4:3:2:1. The execution time of vePOD YOLO with three nodes is longer than two nodes with the same input size. For example, with an input feature map size of 56 \(\times\) 56 \(\times\) 512, the execution time for two and three nodes is 15.6 s and 19.2 s. Compared to two nodes, the secure function for three nodes needs to combine an extra piece of data to retrieve the difference of feature value in every location, which causes an additional delay in performance.  
Figure 8: Performance comparison of YOLO and vePOD YOLO run on two and three edge servers.
## 4 Experimental Results and Analysis
### Evaluation Indicators  
To quantitatively compare the performance of the models, three evaluation metrics, namely precision, recall, and mean average precision (mAP), are utilized. In this experiment, all motor vehicles, including cars and buses, are considered as positive samples, while non-motor vehicles are considered as negative samples. If the real target is a positive sample, the detection result is also a positive sample as \(N_{TP}\), the detection result is a negative sample as \(N_{FP}\); if the real target is a negative sample, the detection result is also a negative sample as \(N_{FN}\), whereas the detection result is a positive sample as \(N_{TN}\).  
\[\text{Pr}=\frac{N_{TP}}{N_{TP}+N_{FP}} \tag{17}\]  
\[\text{Re}=\frac{N_{TP}}{N_{TP}+N_{FN}} \tag{18}\]  
\[\text{m}AP=\frac{\sum\limits_{j=1}^{m}\sum\limits_{i=1}^{n-1}(r_{i+1}-\text{r} _{i})p(r_{i+1})}{\text{m}} \tag{19}\]  
where \(\text{r}_{i}\) represents the recall corresponding to the ith precision in ascending order, \(m\) represents the number of categories of samples in the dataset, which is set to 2 in this experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Input Image Size & 640 \(\times\) 640 & Momentum & 0.937 \\ Optimizer & SGD & Weight decay & 0.0005 \\ lr0 & 0.01 & Batch size & 16 \\ lrf & 0.2 & Epochs & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network training parameters configuration.