esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model  
In this study, a single relay structure is taken into account. In our system model, there is a base station as a source, a traffic sign as a relay and a moving vehicle as a destination as seen in Figure 1. The direction of the moving vehicle is perpendicular to the source. Thissystem model is very practical, because today it is frequently encountered base stations and traffic signs on the roads within the scope of 4G/5G cellular communication systems. In this scenario, only a base station and a traffic sign with a receiver/transmitter feature are sufficient. Therefore, this model is simply considered.  
The focus is on the performance improvement of the data (internet) connection when a moving vehicle communicates with the base station. When the bit error rate (BER) in the transmissions increase, interruptions or outages in the data (internet) connection also increase. This system model is proposed in order to minimize BER.  
In this system model, transmissions are performed in two time slots in a downlink scenario. These time slots are expressed as transmission phases. Different protocols can be used when the relay processes the signal. In this proposed method, an SDF protocol or scheme is used, and this SDF protocol runs in two phases. In the first phase, the source broadcasts the symbol and the symbol is received by the destination and the relay. In the second phase, only if the code (symbol) transmitted by the source is correctly decoded in the relay does the relay then retransmit to the receiver/destination.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 6 Conclusions  
To jointly achieve multiple objectives in tree routing, we revisit the classic problem of finding an optimal parent node in a complex deployment scenario. Our key idea is to find the best parent node by utilizing empirical data about the target network via Q-learning. Specifically, our contributions are: (1) We propose three types of cognitive metrics to cope with various network scenarios. (2) We present a system model for applying RL in tree routing and specify concrete algorithms regarding the tree construction, loop detection, and parent update. (3) We define a state space, action set, and reward function to find the best parent node via Q-learning. Through comprehensive simulations, we demonstrate that the proposed parent selection scheme can strike a balance between the performance metrics regarding end-to-end delay, packet delivery ratio, and energy consumption.  
Conceptualization, B.-S.K., B.S. and I.J.S.; methodology, B.-S.K.; writing--original draft preparation, B.-S.K.; writing--review and editing, H.B.L., J.S.G.; supervision, modelling, K.-I.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Korea Electric Power Corporation (KEPCO) Research Institute under the R&D project entitled by Development & Demonstration of On-line Diagnosis Technology for URD facilities (R20 DA30).
## 2 Experimental Data and Evaluation Index
### Data Preprocessing  
The data used are the battery data within 1020 h sampled under the dynamic current. There are 127,372 sets of original data with 25 dimensions in each group. For the convenience of the model training, the data were extracted every 1 h, and the data of 7 dimensions, namely stack voltage, current, inlet temperature of hydrogen and air, outlet pressure of hydrogen, flow rate of cooling water and inlet hygrometry, were used based on our previously proposed multi-parameter optimization theory [30].  
After the preliminary screening of the data, it is necessary to carry out some simple outlier elimination and noise processing, which is mainly completed by two steps.  
* Elimination of outliers The elimination of outliers is based on 3-\(\sigma\) theory, and the formula is as follows: \[y=\overline{x}\pm 3\sqrt{\sum_{i=1}^{\overline{m}}\left(x_{i}-\overline{x} \right)^{2}}\] (1) where \(\overline{x}\) is the average of the data, \(x_{i}\) is the \(i\)-th actual measured value and \(y\) is the boundary of normal data.
* Noise removal This paper uses moving average filter (MAF) to remove the invalid noise in the data and reduce the disturbance; MAF takes \(N\) consecutive original data as a queue. Moreover, the corresponding original data are replaced with the average value of the data in the queue. When new data are inserted at the end of the queue, the data at the head of the queue are removed. That is, if the length of the queue is \(N\) (generally set to an odd number), then the processed \(k\)-th data can be obtained from Formula (2): \[{x_{k}}^{\prime}=\frac{1}{N}\sum_{i=k-\frac{N}{2}}^{k+\frac{N}{2}+1}x_{i}\] (2) where \(x_{i}\) is the \(i\)th original value, and \({x_{k}}^{\prime}\) is the smoothed value.  
In this paper, \(N\) is set to 11. The original heap voltage data and the smoothed voltage data are shown in Figure 2.  
After noise processing, the new data are used for model training.
## 3 Results and Discussion  
In this study, ten repeated operations for each test condition were designed. The transverse (X-direction and Y-direction) and the vertical (Z-direction) acceleration vibration waves are collected by the sensor, which is illustrated in Figure 4 as an example. In Figure 4, the horizontal axis represents time (s) and the vertical axis represents acceleration (m/s\({}^{2}\)). The peak acceleration value of each wave is recorded, and the average value of ten acceleration peaks is calculated as the representative value to conduct the analysis of the vibration isolation effect. The results of the indoor impact test under different test conditions (different thickness of XPS plates and different falling heights of the drop hammer) are listed in Table 4. It should be mentioned here that the direction of the traffic load was not included in this study, so the acceleration values were supposed to be no different in the X- and Y-direction. However, there was a certain difference between the acceleration values of Table 4, which may have resulted from the fact that the load was applied and the sensor was not completely in the center of the X-Y plane.  
Figure 4: Measured acceleration wave curves. (**a**) Acceleration of the X-direction; (**b**) Acceleration of the Y-direction; (**c**) Acceleration of the Z-direction.
## 3 Proposed Method
### Object-Level Data Augmentation  
Firstly, the pre-processed source image containing the source obstacle and the image with obstacle instance segmentation mask are cropped to the size of the obstacle's bounding box. The cropped source obstacle is then sharpened by applying a sharpening filter if the obstacle scaling factor (OSF) is larger than 1, to retain the sharp object edges, even in the case that the obstacle shall be upscaled for the purpose of so-called true-to-scale insertion into the target image. In parallel, the cropped obstacle segmentation mask is processed by applying appropriate morphological operations with the goal to smoothen the edges of the binary segmentation mask. The processed, cropped source image and cropped segmentation masks are placed, respectively, into two images of the sizes of the target image, whose pixels all have zero values. These two images are denoted as \(I_{\text{obst}}\) and \(I_{\text{mask}}\). The points of insertion of the cropped images into the images \(I_{\text{obst}}\) and \(I_{\text{mask}}\) are referred to as points of intrusion (PoI). The PoI is defined as the image coordinates of the point in the target image where the bottom right corner of the source obstacle will be upon its insertion into the target image. The PoI and the OSF are calculated in the processing pipeline, named context- and scale aware augmentation, that consists of several processing steps as explained in the following.
# Low-Frequency Communication Based on Rydberg-Atom Receiver
## 4 Results and Discussion
The process involves six primary steps: (1) decomposing the original image into its constituent red (R), green (G), and blue (B) color spaces; (2) mapping each pixel’s intensity value to an 8-bit sequence; (3) encoding these sequences into baseband signals represented by high and low voltage levels; (4) modulating these baseband signals onto a carrier using BPSK; (5) transmitting the modulated signal; (6) demodulating the received signals to retrieve the original 8-bit sequences and reconstructing the pixel data to recover the final color image. color images can be recovered after being received by the Rydberg atom. As shown in Figure 10, black-and-white images are fully recoverable, whereas the restored color images exhibit some degree of blurriness. To evaluate the quality and reliability of color image transmission, we primarily assess the image fidelity using the PSNR. The PSNR is calculated using the following formula:  
\[PSNR=10\times\log_{10}\left(\frac{MAX^{2}}{MSE}\right)=20\log_{10}\left(\frac{ MAX}{\sqrt{MSE}}\right) \tag{3}\]  
where \(MAX\) represents the maximum possible pixel value in the image, and \(MSE\) denotes the mean squared error. Typically, a \(PSNR\) value exceeding 40 dB is considered excellent. The calculated \(PSNR\) for the color image received by the Rydberg atom was 70 dB.
###### Abstract  
Computing-in-Memory (CIM) is a novel computing architecture that enormously improves energy efficiency and reduces computing latency by avoiding frequent data movement between the computation and memory units. Currently, digital CIM is regarded as more suitable for high-precision operations represented in floating-point arithmetic, as it is not limited by the bit width of ADC/DAC in analog CIM. However, the development of DCIM still faces two problems: On the one hand, mainstream SRAM-based DCIM memory cells introduce large area overheads, which contain at least six transistors per cell. On the other hand, existing DCIM solutions can only support the computing precision up to FP32, failing to meet the demands of high-accuracy application scenarios. To overcome these problems, this work designs a novel SOT-MRAM-based digital CIM macro (MDCIM) with higher area/energy efficiency and achieves double-precision floating-point (FP64) computation with a modified fused multiply-accumulate (FMA) module. The proposed design is synthesized with a 55 nm CMOS technology node, achieving 0.62 mW power consumption, 26.9 GOPS/W, and 0.332 GOPS/mm\({}^{2}\) energy efficiency at 150 MHz with 1.08 V supply. Circuit level simulation results show that the **MDCIM** can achieve higher area utilization compared to previous SRAM-based CIM designs.  
digital computing-in-memory; SOT-MRAM; double-precision floating-point format; fused multiply-accumulate +
Footnote †: journal:
## 3 Results and Discussion
#### 3.1.1 Effect of Reaction Temperature  
The effect of the reaction temperature on the preparation of the modified DPNR particles was studied. The experiment was performed by using 10 phr of monomer content and 1 phr of the initiator. Figure 2 presents a plot of the monomer conversion as a function of polymerization time at different reaction temperatures (40, 50, and 60 \({}^{\circ}\)C). It can be seen that the conversion increased with increasing polymerization time for all temperatures used. The polymerization rate increased rapidly at the initial polymerization stage and remained constant after 360 min. When the reaction temperature increased from 40 to  
Figure 1: Schematic representation of preparing the (PAA-_co_-PAM)-_grafted_ DPNR by graft copolymerization.  
50 \({}^{\circ}\)C, the conversion increased from 73.6 to 91.9%. The increase in temperature can cause a higher collision frequency between monomer molecules and the free radicals in the system. This resulted in an acceleration of the polymerization rate and an increase in monomer conversion [36]. However, after increasing the temperature to 60 \({}^{\circ}\)C, the monomer conversion slightly increased at the early stage of polymerization, and then the conversion seemed to decrease when compared to 50 \({}^{\circ}\)C. The monomer conversion was reached at 87.4%. Thus, the reaction temperature at 50 \({}^{\circ}\)C, which exhibited the highest conversion, was selected for further study.
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
###### Abstract
The LSTM method can deal with the problem of gradient explosion, but it has limitations for the processing of long sequence data. In the subway vehicle overhaul process, failure rate [15] is an important indicator in the reliability assessment, which cannot be ignored, and is often combined with time series in the prediction process. Time series [16] analysis is a statistical method used to analyze and process dynamic data for predicting future trends by analyzing and mining the previous development of specific data, which, after being combined with the failure rate, will lead to a relatively small amount of data and relatively short time sequences, making it difficult to make accurate predictions using the above model. This paper proposes a combination model based on Random Forest (RF), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to predict the failure rate of the key components of subway vehicles. Based on the actual subway failure rate data, Random Forest is applied to check for missing values in the data to ensure the quality of the prediction data, then CNN is used to extract the data features, and finally LSTM is used to predict the failure rate of key subway components. The ARIMA model, multilayerperceptron neural network model, LSTM model and the proposed RF-CNN-LSTM model are compared, and the R-squared accuracy and adjusted R-squared accuracy are introduced to evaluate the model's prediction accuracy. Taking the door controller of the door system, a key component of the subway, as an example, the RF-CNN-LSTM model is used to predict failure rate and provide support for the preventive maintenance of EDCUs.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 4 Services for Final Users  
Feedback programs are crucial for stimulating the customers to change their usual habits [33]. Real-time direct feedback is particularly important compared to the indirect solution, where measurements are available many hours later due to validating processes performed by DSOs for billing purposes. According to [33], real-time feedback programs can ensure an energy savings of up to 8.6%, while indirect measurements affect just 2% of the total energy consumption. Such information will be acquired by IHDs located in customers' premises with the goal of supplying them a set of new services for consumption awareness, customized contracts and, if located in a smart context (e.g., a smart home), home automation. Moreover, the same services have positive effects on other stakeholders of the electricity value chain. For a SM, we defined \"sampling time\" as the periodic interval of time passing from the acquisition of a measurement to the following acquisition of the same measurement, and \"latency\" as the interval of time passing from the generation of the information in the source field to the complete delivery to the final entity, identified according to the use case considered. Consequently, we define \"real-time service\" as any service characterized by a sampling time requirement finer than 15 min and a maximum latency requirement tighter than 1 h. It is worth noting that we include real-time and near real-time services in the same category. The combination of the two mentioned constraints satisfies most of DSOs' and the market's needs and enables the activation of the real-time services identified in Table 2. This list of services can be grouped into four categories: Awareness, Market, Scheduling & Control, and Network Services. Figure 2: Second generation Smart Metering: Advance Metering Infrastructure (AMI) and chain 1/chain 2 explanation.
# Electrical Monitoring under Transient Conditions:
## 4 Results
### Results in Laboratory Motors
This illustrates an interesting advantage of stray flux analyses: since more evolutions can be identified in the time-frequency maps, the diagnosis will be more reliable, since it will rely on multiple harmonic evolutions rather than on evolutions of a few fault components. Moreover, note the clear differences between the healthy and faulty cases; much  
Figure 4: Experimental test bench and two considered positions of the flux sensor [28]. more harmonic evolutions are discernible in these latter cases. This is because the patterns caused by the evolutions of the fault harmonics are clearly amplified in these latter cases. More specifically, note the clear evolutions of the axial components, given by \(s\)-\(f\) and \(3\)-\(s\)-\(f\), that are amplified by the rotor damages. These components are already present in healthy condition (since they can be created by inherent eccentricities in the motor), but their respective amplitudes are clearly incremented when the fault is present. These components (that are not observed in the starting current analyses, see Figure 5) can be visualized in the graphs of Figures 6 and 7 because these two sensor positions (position A and B) are those capturing a major portion of axial flux, as commented above (see Figure 3). On the contrary, note that the evolutions of the radial components such as \(f\)-\((1-2\):s) and \(f\)-\((1+2\):s) are less observable. In conclusion, the time-frequency analyses of stray flux signals under starting yields time-frequency maps with higher harmonic content (hence with more potential for the diagnosis but also requiring a more complex interpretation) and with high sensitivity with respect to the sensor position since, according to the predominant portion of captured flux (axial/radial), the corresponding components can be better tracked. The determination of the optimum position of the flux sensor is a goal pursued in several works [28]. In this regard, position B shown in Figures 3 and 4 seems to be a preferred option, since apart from being more accessible in practical applications (in comparison with other options), it captures both axial and radial flux and, therefore, a higher number of potential evolutions can be tracked in the analyses. Very recent papers have proposed the use of triaxial sensors to separately measure different stray flux portions in a single sensor [35]. Figure 5: Time-frequency maps of the starting current for different rotor fault conditions. Figure 6: Time-frequency maps of the electromotive force (emf) signals induced by the stray flux at sensor position A for the two considered fault conditions.
## 3 Estimation of Motion Parameters Based on the CCAE Method
### Proposed CCAE Algorithm
#### 3.2.2 Estimation of Higher Order Parameters  
It is seen from Equation (17) that the estimated frequency of the cross-correlation result is determined by the difference in the target's distance between the two pulses. To estimate high-order motion parameters, the differential operation or curve fitting operation can be applied to the cross-correlation results. For example, a second-order differential operation can be applied to estimate the jerk, with which the acceleration can be obtained using the result of the first-order differential operation. Another approach for simultaneous estimation of velocity, acceleration and other higher order parameters is to employ the curve fitting method, such as the least-square method. However, high-order parameter estimation requires more pulses, and consequently, the output delay increases. It is worth noting that the objective of the proposed CCAE method is to estimate the motion parameters in real time, i.e., the estimated results should be output with minimum delay. Therefore, for estimation of high-order motion parameters, we mainly focus on acceleration estimation in this paper. Considering the acceleration, Equation (18) is rewritten as:  
\[R_{p}(t_{m})=R_{0,p}+vt_{m}+\frac{1}{2}at_{m}^{2}. \tag{21}\]  
Then, the estimated frequency \(\hat{f}_{m}\) of Equation (17) can be expressed as:  
\[\hat{f}_{m}=\frac{2\gamma T_{pr}}{c}(v+at_{m}+\frac{1}{2}aT_{pr}). \tag{22}\]  
It is seen that the estimated frequency is a slow time varied function. The acceleration can be calculated out by \(f_{m}\) and \(f_{m+1}\) (i.e., three echo signals are used).  
\[\hat{a}=\frac{c}{2\gamma T_{pr}^{2}}(\hat{f}_{m+1}-\hat{f}_{m}). \tag{23}\]  
Then, the velocity can be obtained with \(\hat{f}_{0}\) at slow time \(t_{m}=mT_{pr}\) (\(m=0\)) and \(a\), where \(t_{m}\) (\(m=0\)) can always be set to the time of the first pulse:  
\[\hat{v}=\frac{c}{2\gamma T_{pr}}f_{0}-\frac{1}{2}T_{pr}\hat{a}. \tag{24}\]
# Cost-Effective CNNs for Real-Time Micro-Expression Recognition
## 4 Experiments  
### Dataset and Validation Protocol Presentation  
Two ME databases were used in our experiments. CASME II (Chinese Academy of Sciences Micro-Expression) [15] is a comprehensive spontaneous ME database containing 247 video samples, collected from 26 Asian participants with an average age of 22.03 years old. Compared to the first database, the Spontaneous Actions and Micro-Movements (SAMM) [36] is a more recent one consisting of 159 micro-movements (one video for each). These videos were collected spontaneously from a demographically diverse group of 32 participants with a mean age of 33.24 years old and a balanced gender split. Originally intended for investigating micro-facial movements, SAMM initially collected the seven basic emotions.  
Both the CASME II and SAMM databases were recorded at a high-speed frame rate of 200 fps. They also both contain \"objective classes,\" as provided in [37]. For this reason, the Facial MEs Grand Challenge 2018 [38] proposed to combine all samples from both databases into a single composite dataset of 253 videos with five emotion classes. It should be noted that the repartition is not very well balanced. Namely, this composite database is composed of 19.92% \"happiness\", 11.62% \"surprise\", 47.30% \"anger\", 11.20% \"disgust\" and 9.96% \"sadness\".  
Similarly to [38], we applied the Leave One Subject Out (LOSO) cross-validation protocol for ME classification, wherein one subject's data is used as a test set in each fold of the cross-validation. This is done to better reproduce realistic scenarios where the encountered subjects are not present during  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Input** & **1 CL** & **2 CL** & **3 CL** & **4 CL** \\ \hline
**Single Channel** & 82,373 & 168,997 & 333,121 & 712,933 \\
**Double Channel** & 165,541 & 348,005 & 709,477 & 1,620,197 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of learnable parameters according to the dimensionality of the input of the network.  
Figure 5: Proposed networks composed of one to four (from left to right) CLs for various representations of the optical flow as input.  
training of the model. In all experiments, recognition performance is measured by accuracy, which is the percentage of correctly classified video samples out of the total number of samples in the database.  
The Horn-Schunck method [PERSON] (1973) was selected to compute optical flow. This algorithm was widely used for optical flow estimation in many recent studies for virtue of its robustness and efficiency. Throughout all experiments, we trained the CNN models with a mini-batch size of 64 for 150 epochs using the RMSprop optimization. Feature extraction and classification were both handled by the CNN. Simple data augmentation was applied to double the training size. Specifically, for each ME video clip used for training, in addition to the optical flow between the onset and apex frame, we also included a second flow computed between the onset and apex+1 frame.
## 4 Experimental Results
### Comparison with the State-of-the-Art Methods  
The performance of the proposed algorithm was compared with the results reported in other articles that included various developed, hybrid, and improved algorithms. These algorithms included SATLBO [5], JJAYA [16], hARS-PS [47], APLO [48], CMM-DE/BBO [49], DE/BBO [50], BLPSO [51], CLPSO [52], MSSA [53], TLABC [54], GOTLBO [55], STLBO [56], MVO [57], QMVO [58], Rcr-JADE [59], and IWOA [60].