esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Microwave Deicing of Carbon-Fiber-Modified Concrete under Multi-Factor Coupling Effect
## 6 Conclusions  
In this paper, a comprehensive study was conducted on the law of concrete wave absorption heat generation and deicing under the coupling effect of multiple factors, including carbon fiber dosage and length, microwave height, ice cover and initial temperature. The conclusions are as follows:  
1. The incorporation of carbon fiber significantly improves the absorption and heating efficiency of concrete. After microwave irradiation, the overall heating area increases, and the temperature increase rate and range increase. The optimal fiber length is 0.6 cm, with 0.2% heating efficiency.
2. When the height of the microwave source trumpet is 40 mm, the deicing area and the temperature after deicing reach their maximum values, corresponding with the optimal height. The initial temperature and ice layer only affect the actual deicing effect but have little influence on absorption heating.
3. The simulation calculation results match the test results to a high degree, and the formation of the absorbing heating area is consistent with the actual heating area. The main factors affecting the absorption heating performance of carbon fiber concrete are the overall conductivity and microwave reflectance of the concrete. In this experimental study, we only considered a case of with single microwave source; for cases with multiple wave sources, further research is needed to establish a more widely applicable temperature increase analysis formula.  
**Author Contributions:** Conceptualization, A.Y., W.X. and H.H.; methodology, A.Y., W.X. and H.H.; software, A.Y., W.X. and H.H.; validation, A.Y., W.X. and H.H.; formal analysis, A.Y., W.X. and H.H.; investigation, A.Y., W.X. and H.H.; resources, A.Y., W.X. and H.H.; data curation, A.Y., W.X. and H.H.; writing--original draft preparation, A.Y., W.X. and H.H.; writing--review and editing, A.Y., W.X. and H.H.; visualization, A.Y., W.X. and H.H.; project administration, J.X., E.B. and Y.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by the Natural Science Foundation of China (51908548) and the Youth Talent Promotion Program of Shaanxi University Association for Science and Technology (20200415).  
**Institutional Review Board Statement:** The study did not involve humans or animals.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** The authors express their thanks to all members of the laboratory team for their technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Advanced Technology in ATSC3.0: Boosting Data Signal Rates Using Polarization Properties and FSS +
## 3 Proposed System
\(\hat{n_{1}}\), \(\hat{n_{2}}\) considers the signals from different polarization antennas as residual noise, taking into account the attenuation due to XPD. Therefore, by reducing only the received power from different polarization antenna, it is possible to receive the desired data by switching between vertical and horizontal receiving antennas. As shown in Figure 4b, the receiver removes the GI, performs S/P conversion, and applies fast Fourier Transform (FFT). Lastly, this frequency domain signal is detected and demodulated. When receiving additional data, the converted signals are despread by spreading code in the frequency domain. When despreading additional data, spread gain is obtained. Therefore, additional data can be received with the same performance as broadcast. However, multipath fading disrupts the orthogonality among spread symbols. To restore it, we utilize a minimum mean square error combining MMSEC-based frequency equalization for detection. The MMSEC weight is defined as follows:  
\[\Omega_{\text{MMSEC}(m)}=\frac{h^{*}(m)}{|h(m)|^{2}+2\sigma^{2}}, \tag{28}\]  
where \(h(m)\) denotes the channel estimate of the \(m\)-th OFDM subcarrier, and \(\sigma\) represents the Additive White Gaussian Noise (AWGN) and residual noise per subcarrier [16]. Figure 5 illustrates the overall mechanism of FSS. Within the FSS block, each subcarrier symbol is spread in the frequency domain using an orthogonal spreading code. The FSS block refers to a group of subcarriers to which the spreading code is applied, and the resulting spread data symbols are combined across these subcarriers. At the receiver side, the original data can be retrieved through a despreading operation. This helps mitigate the effects of frequency-selective fading by effectively distributing its influence across the entire FSS block [15; 17]. This approach allows FSS to provide frequency diversity, thereby suppressing the impact of frequency-selective fading. The conventional TxID spreading codes often use Gold codes with spreading code lengths of \(2^{n}-1(n=1,2,3\ldots)\). However, in this case, since spread is performed in the frequency domain by FSS, FFT must be performed after spreading. Considering that the FFT size must be a power of 2. Therefore, Hadamard codes with spreading code lengths of \(2^{m}(m=0,1,2,3\ldots)\) are used in this paper.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 1 Introduction
The second is the target task performance (TTP) criterion [17; 18]; it was established to solve the single frequency constraining the Johnson criterion, and replaced the Johnson criterion with the TTP criterion. It takes into account the effects of all spatial frequency information below the threshold frequency on the task requirements. The third is the magnitude [19; 20], which is a measure of the luminosity of a celestial body. In order to measure the detection performance of astronomical telescopes, the limit magnitude is used, which is the faintest magnitude that a telescope can see. These criteria and methods give qualitative and quantitative evaluation criteria for the detection and identification capabilities of the optoelectric imaging system. Generally, the volume of the optoelectric tracking measurement device is large, and it is not easy to track the fast-moving target. The above performance evaluation criteria are mainly from the image signal results, without analyzing the influencing factors in the imaging process. For the stable tracking of fast-moving targets, this paper mainly designs the orbital optoelectric tracking system combining with a rotating mirror to reduce the tracking movement load, and establishes the limit magnitude detection calculation model with the target photometric signal characteristics, background noise, and CCD camera noise based on the magnitude evaluation method, which provides the basis of the research and development of the high-speed and dynamic target tracking system for the shooting range test. This paper is organized as follows: the orbital optoelectric tracking system is designed in Section 2; then, the limit magnitude detection capability model of it is constructed in Section 3; computational and experimental analyses are carried out in Section 4; and finally, the conclusion is given in Section 5.
## 2 Six-Hardpoint Positioning Mechanism and Its Mathematical Model  
The six-hardpoint positioning mechanism is used to adjust and maintain the pose of the large aperture primary mirror. It forms a primary mirror positioning system with the primary mirror and mirror cell, and is a subsystem of the pneumatic active support system for the primary mirror of the large aperture ground-based telescope. As shown in Figure 1, the six-hardpoint positioning mechanism consists of six identical hardpoints, one end of the hardpoint is connected to the primary mirror back plate at A\({}_{1}\) to A\({}_{6}\) through a flexible hinge, and the other end is connected to the mirror cell at B\({}_{1}\) to B\({}_{6}\) through a flexible hinge. The hardpoint can be considered as a displacement actuator with high axial stiffness, so the kinematic principle of the mechanism is similar to that of a Stewart platform. Adjusting the lengths of the hardpoints can realize the adjustment of the pose of the primary mirror relative to the mirror cell. When the lengths of all hardpoints are maintained, the six-hardpoint positioning mechanism completely limits the degrees of freedom of the primary mirror to maintain the pose of the primary mirror.  
The mathematical model includes kinematic models and an error model. Kinematic models include forward and inverse kinematic models. The forward kinematic model refers to obtaining the pose of the primary mirror through the length of the hardpoint, and the inverse model refers to obtaining the lengths through the pose. Both the forward and inverse model need to be used in calibration. The error model provides the relationship between kinematic parameter errors of the mechanism and pose errors of the primary mirror, which is one of the contents of kinematic calibration.
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 4 Results and Analysis
### The BERs of the Seq2 seq Decoder  
To evaluate the performance of seq2 seq decoder, we compare it with the traditional table look-up method. We also plotted the MAP decoding of 4B6B codes as the benchmark. That is, the received noisy signal \(\mathbf{y}\) is decoded to the codeword \(\mathbf{c}\) that minimizes the squared Euclidean distance \(d_{E}^{2}(\mathbf{y},\mathbf{c})\) and naturally obtain the corresponding source word in terms of the 4B6B codebook.  
**(1) Frame-by-frame transmission**: Figure 5a shows the performance of the seq2 seq decoder under the frame-by-frame transmission case. We can observe that the table look-up decoding method leads to the worst BERs performance, since it is a hard decision-based decoding method and is not able to make full use of channel information. Moreover, the seq2 seq decoder had a notable performance gain of up to \(\sim\)2.5 dB at \(\text{BER}=10^{-2}\) compared to the conventional table look-up decoding. In addition, our seq2 seq decoder can achieve a low BER performance that satisfies the MAP criterion.  
**(2) Multi-frame transmission**: Figure 5b demonstrates the BERs of the seq2 seq decoder under the case of processing multiple RLL-coded frames, where \(N\) is a multiple of six. It can be seen that the proposed decoder is fully capable of more than one frame of parallel decoding, and can reach eight frames of simultaneous decoding. The BERs almost achieved MAP-based decoding performance, which is able to largely enhance the throughput of the VLC systems. Note that such multi-frame decoding is achieved by a single decoding operation due to the parallel computing capability of the NN, whereas look-up table decoding requires multiple decoding operations to achieve multi-frame decoding. This, in a way, shows our method's superiority over the traditional table look-up decoding method. However, with a further increase in the number of parallel frames, the performance of the seq2 seq decoder is degraded. Therefore, we suggest that the number of frames for parallel decoding needs to be set in terms of the BER tolerance of the actual VLC application.  
Figure 4: (a) MEMAP versus training \(E_{b}/N_{0}\) over 4B6B RLL codes for a seq2 seq decoder trained with 40 training epochs. (b) BERs of the seq2 seq detector for each epoch during the training process at training \(E_{b}/N_{0}=1\) dB.
## 7 Research Directions
### Memory-Aware Security Solutions  
As mentioned in the early part of this paper, memory efficiency is crucial in resource-limited IoT devices because these devices often have constraints in terms of processing power, storage, and energy. Optimizing security schemes to reduce memory consumption is essential to ensure that security measures do not compromise the functionality and performance of the IoT devices. Here are some controls and strategies to address memory efficiency in resource-limited IoT devices:  
**Cryptographic Algorithm Selection:** We can choose lightweight cryptographic algorithms that require less memory and processing power. For example, consider using elliptic curve cryptography (ECC) instead of RSA for public key cryptography, as ECC generally requires smaller key sizes and less computational resources [184].  
**Optimized Libraries and Code Size:** It would be advantageous to utilize memory optimization techniques such as code and data compression to reduce the overall memory footprint [185; 186]. This may involve using tools and compilers that can optimize code for size. We can use lightweight cryptographic libraries that are specifically designed for resource-constrained environments [127]. These libraries are often tailored to minimize memory usage while still providing essential security functions. It would be useful to employ code size optimization techniques during the development and compilation process. This includes removing unnecessary code, using compiler optimizations, and employing techniques such as function inlining.  
**Dynamic Memory Allocation:** We need to minimize or avoid dynamic memory allocation (e.g., using malloc in C) [187], as it can lead to fragmentation and increased memory overhead. Instead, prefer static memory allocation when feasible.  
**Secure Key Storage:** It is important to implement secure key storage mechanisms that use minimal space while ensuring the confidentiality and integrity of cryptographic keys. We also need to consider hardware-based security solutions or trusted execution environments when available. It is recommended to optimize the storage of cryptographic materials and program codes in flash memory. This can involve efficient data structures and storage formats to minimize the space required for storing keys, certificates, and other cryptographic parameters [188].  
**Hardware Acceleration:** We can exploit hardware-based cryptographic accelerators if available on the IoT device. Offloading cryptographic operations to dedicated hardware can significantly reduce the burden on the main processor and conserve memory [189; 190].  
**Firmware Over-the-Air (OTA) Updates:** We can also suggest implementing efficient firmware update mechanisms [191; 192] that only transmit and store the necessary changes, which reduces the overall data transfer and storage requirements.
## 3 Simulated Result and Analysis
### Parameters Analysis  
This section mainly investigates the optimization of the parameters of the proposed miniature eight-element MIMO antenna. The effects of the clearing ground distance (L1) on the mainboard and the length of the Wang-shaped feed element (L2) on the sideboard are used to optimize reflection coefficients. Since the proposed miniature eight-port MIMO antenna is symmetrical in structure, antenna elements with the same situation are ignored. In Figure 4a, the reflection coefficient varies as a function of the clear ground distance, L1. To cover both the lower and higher frequency bands, the optimal value of L1 is 1 mm. In Figure 4b, the reflection coefficient varies as a function of the Wang-shaped microstrip line width, L2. When the value of L2 is 1.2 mm, both the lower and higher frequency bands meet the requirement.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 4 Performance Analysis of the Proposed SCbSC
### Cryptanalytic Analysis
#### 4.2.1 Key Size and Sensitivity Analysis  
For a secure image encryption system, the key space should be large enough to resist a brute-force attack [38]. The secret key is produced here by Xorshif generator [30] and its size is given by:  
\[\begin{array}{l}|K|=|XL0|+|XT0|+|Q0|+|XLC1|+|XSC1|+|XT1C1|+|P_{\mathrm{s}}|\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \qquad\qquad\qquad\qquad\qquad\qquad\qquad\  
A robust cryptosystem should also be sensitive to the secret key; that is, changing a one bit in the secret key must produce a completely different encrypted image. This sensitivity is conventionally measured by two parameters which are the NPCR (number of pixel change rate) and the UACI (unified average changing intensity) [39]. Besides, instead of those two parameters which operate on the bytes, we use the Hamming distance \(H_{D}\) which operates on the bits (in our opinion \(H_{D}\) is more precise than NPCR and UACI parameters). The expressions of these parameters are given below, with \(C_{1}\) and \(C_{2}\) being the two ciphered images of the same plain image P.  
\[NPCR=\frac{1}{M\times N}\sum_{i,j}D(i,j)\times 100\% \tag{25}\]  
\[D(i,j)=\left\{\begin{array}{ll}1&if\ C_{1}(i,j)\
eq C_{2}(i,j)\\ 0&if\ C_{1}(i,j)=C_{2}(i,j)\end{array}\right. \tag{26}\]  
where \(M\) and \(N\) are the width and height of \(C_{1}\) and \(C_{2}\). The NPCR measures the percentage of different pixel numbers between two ciphered images.  
\[UACI=\frac{1}{M\times N\times 255}\sum_{i,j}\lvert C_{1}(i,j)-C_{2}(i,j) \rvert\times 100\% \tag{27}\]  
which measures the average intensity of differences between the two images.  
\[H_{D}(C_{1},C_{2})=\frac{1}{Nb}\sum_{i=1}^{Nb}(C_{1}(i)\oplus C_{2}(i)) \tag{28}\]  
with \(Nb\) being the number of bits in an encrypted image.  
For a random image, the expected values of NPCR, UACI, and \(H_{D}\) are 99.609%, 33.4635%, and 50% respectively. Table 6 shows the results obtained of NPCR, UACI, and \(H_{D}\) for the plain images Lena, Pepper, Baboon, Barbara, and Boats of the same size--256 \(\times\) 256 grayscale images. As we can see from these results, the NPCR, UACI, and \(H_{D}\) values obtained are very close to the optimal values. These values indicate that the proposed SCbSC system is very sensitive to slight modifications of the secret key.
# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
## 4 Experiment and Results
### Results
#### 4.4.2 Alignment Mark Detection  
The prediction performance is calculated by a loss function that classifies the input data points in the data set. The smaller the loss value, the better the classifier models the relationship between the input data and the output target. The gradual decrease in loss values after each epoch shown in Figure 10 represents the gradual learning process of YOLOv4, YOLO-csp-swish, and YOLOv4-tiny. The curves obtained by the loss functions of YOLOv4, YOLO-csp-swish, and YOLOv4-tiny are very stable after 2000 epochs.  
To experiment with various methods, the existing image was resized and tested. Experiments were conducted by changing the image resolution to \(687\times 512,512\times 384\), and \(256\times 192\), including the basic image (\(1024\times 768\)). Figures 11-13 are the detection output, showing the alignment obtained from the YOLOv4, YOLO-csp-swish, and YOLOv4-tiny models for each size of the image.  
Figure 10: _Cont._  
Figure 11: YOLOv4 model.  
Figure 10: Training process with the models.  
Figure 12: YOLOv4-csp-swish model.  
Figure 13: YOLOv4-tiny model.  
As a result of the test, YOLOv4 performed better than the YOLOv4-csp-swish and YOLOv4-tiny models. Overall, all models detected the alignment marks more thoroughly without omission and with large reliability values. However, during the experiment, there was a decrease in the detection accuracy of the alignment mark depending on the resolution of the camera, and improvement was needed. As shown in Table 3, the performance results of the 512 \(\times\) 384 size and 256 \(\times\) 192 size are clearly different. In particular, the alignment detection performance of the 256 \(\times\) 192 image was poor. Overall Precision, Recall, _F1-Score_, _IOU_, and mAp values were significantly lower than other sizes of data. Therefore, an experiment was conducted to improve the image resolution of the 256 \(\times\) 192 size.
## 1 Introduction
### Bibliography Summary and Project Positioning
\begin{table}
\begin{tabular}{l l l c c c} \hline \hline
**Reference** & **Sensors** & **DP** & **P/R** & **P/F/S** & **HIL** & **Cost** \\ \hline [7] & Microphone & CNN & R & S & No & Low \\ [13] & Microphone & CNN & R & S & No & Low \\ [8] & Accelerometer & CNN & R & S & No & Med \\ [14] & Vibration & RNN+CNN & R & S & N/A & Low \\ [15] & Vibration & RNN+CNN & R & S & N/A & Low \\ [18] & 20 different sensors & RNN+CNN & R & S & No & High \\ [19] & 395 fps camera & CNN & R & S & No & Med \\ [20] & 2000 fps camera & Gabor Filters & R & S & No & Med \\ [21] & Modified camera + ad hoc mirrors & CNN & R & S & No & High \\ [22] & Vibration & CNN & P & F & No & Low \\ [23] & Accelerometer & RegLog & P & F & No & Low \\ [24] & AR + force sensors + cameras & N/A & R & S & Yes & High \\ [25] & Wearable vibration + force sensors & ANN & R & S & Yes & High \\ [27] & Camera & Hopfield RNN & R & S & No & Med \\ [28] & RaspberryPi and camera & Similarity Index & R & S & No & Low \\ [29] & Grey scale and limit switches & ANN + Rules & R & S & No & Low \\ [16] & X-Ray Camera & CNN & R & S & No & Low \\ [30] & Camera & DNN and MDL & R & S & Yes & Med \\ [9] & Vibration & FNN & R & F & No & Med \\ [10] & Raspberry Pi and general hardware & Heterogeneous & R & N/A & Yes & Med \\ [11] & Existing + retrofitted & DB & R & S & Yes & Med \\ [12] & N/A & Decision tree & P & P & No & Med \\ [17] & N/A & CAD/CAM & R & P & Yes & Low \\ [26] & AR & N/A & P & S & Yes & High \\ [31] & IIoT & Agents & P & F & No & High \\ Our approach & Camera & CNN & P & P & Yes & Med \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing Poka Yoke approaches comparison, exploiting machine learning and computer vision in the industrial domain. DP stands for Data Processing technique, P/R for Proactive or Reactive, P/F/S for Physical, Functional and Symbolic, and HIL stands for Human-In-the-Loop.
## 2 Materials and Methods
### RNA Isolation and Real-Time Reverse Transcription Polymerase Chain Reaction (RT-PCR)  
The RNA isolation and RT-PCR were performed according to the previously used protocol [47]. Briefly, RAW 264.7 cells were grown in 6-well culture plates at a density of 2 \(\times\) 10\({}^{5}\) cells/mL. After 24 h of incubation, cells were treated with different dilutions (0, 50, 100, 200, or 400 \(\upmu\)g/mL) of three algal extracts (SH, UP, and SF) separately for 24 h. After treatment, the total RNA from the different treatment groups was extracted using an RNA Extraction Kit (iNtRON Biotechnology, Seongnam, Republic of Korea) following the manufacturer's instructions. The isolated RNA was quantified through Nanodrop to check the quality of the isolated RNA. A total of 50 ng of the RNA was used to form complementary DNA (cDNA) together with 4 \(\upmu\)L of random primers and 2 \(\upmu\)L of enzyme mix (TaKaRa Bio, Otsu, Japan). A total of 5.5 \(\upmu\)L of synthesized cDNA was subjected to mRNA expression analysis using an ABI QuantStudio3 (Applied Biosystems, Foster City, CA, USA) and SYBR Green Master Mix (TaKaRa Bio, Otsu, Japan) using the selected gene primer pairs (forward and reverse) as shown in Table 1. PCR thermocycling consisted of three stages, namely, denaturation (94 \({}^{\circ}\)C, 30 s), annealing (55 \({}^{\circ}\)C, 30 s), and the synthesis of the complementary strand (72 \({}^{\circ}\)C, 1 min), and these three stages continued for 35 cycles. The target gene expression levels were adjusted to that of \(\beta\)-actin mRNA. For each sample, the relative abundance of the target mRNA was calculated from the \(-\Delta\)cycle threshold (\(\Delta\)Ct) values of the target and endogenous \(\beta\)-actin reference genes using the 2\({}^{-\Delta\Delta}\) Ct method.