esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 3 Gesture Recognition System
### Feature Vector Generation  
In the feature vector generator, the image frame that is \(P\times Q\) pixels in RGB color format is converted to the feature vector \(\overset{\rightarrow}{G}\). The feature vector proposed in [3] is employed. Computation to obtain the feature vector is shown in Figure 2, which consists of a binary quantization, horizontal and vertical projection histogram calculations, and two discrete Fourier transforms (DFTs). Output is the \(D\) dimensional feature vector \(\overset{\rightarrow}{G}\).  
Figure 1: Dynamic gesture recognition system.  
Figure 2: Feature vector generation.  
Firstly, the input color frame image is converted to a binary image \(I(x,y)\). For the system to remove the background image including the arm as well as to extract the finger segments, the user is required to wear a glove, finger portion of which is colored in red. If color of pixel is red the pixel is treated as 1, otherwise 0. Then horizontal and vertical histograms \(P_{H}(y)\) and \(P_{V}(x)\) of \(I(x,y)\) are calculated as follows:  
\[P_{H}(y) = \sum\limits_{x=0}^{P-1}I(x,y) \tag{1}\] \[P_{V}(x) = \sum\limits_{y=0}^{Q-1}I(x,y) \tag{2}\]  
After the histogram calculations, DFTs are carried out on the histograms.  
\[A_{H}(k) = \sum\limits_{y=0}^{Q-1}P_{H}(y)\cdot\cos\Bigl{(}\tfrac{2\pi\eta k }{Q}\Bigr{)} \tag{3}\] \[B_{H}(k) = \sum\limits_{y=0}^{Q-1}P_{H}(y)\cdot\sin\Bigl{(}\tfrac{2\pi\eta k }{Q}\Bigr{)}\] (4) \[A_{V}(k) = \sum\limits_{x=0}^{P-1}P_{V}(x)\cdot\cos\Bigl{(}\tfrac{2\pi\eta k }{P}\Bigr{)}\] (5) \[B_{V}(k) = \sum\limits_{x=0}^{P-1}P_{V}(x)\cdot\sin\Bigl{(}\tfrac{2\pi\eta k }{P}\Bigr{)} \tag{6}\]  
Here, \(A_{H}(k)\), \(A_{V}(k)\) and \(B_{H}(k)\), \(B_{V}(k)\) are real and imaginary parts of the frequency components of the histograms. Then, \(F_{H}(n)\) and \(F_{V}(n)\), i.e., the magnitude spectra of \(P_{H}(y)\) and \(P_{V}(x)\) are computed as  
\[F_{H}(k) = \sqrt{A_{H}^{2}(k)+B_{H}^{2}(k)} \tag{7}\] \[F_{V}(k) = \sqrt{A_{V}^{2}(k)+B_{V}^{2}(k)} \tag{8}\]  
The \(F_{H}(k)\) and \(F_{V}(k)\) of the same hand posture images placed in different positions are identical because they are the magnitude spectra lacking the phase information related to the hand posture position. Since most of the image's feature information is concentrated in the lower frequency components, they are used as the feature vector. The \(D\)-dimensional feature vector, \(\overrightarrow{G}\) is formed from \(F_{H}(k)\) and \(F_{V}(k)\) as;  
\[\overrightarrow{G} = \{\lambda_{0},\lambda_{1},\cdots,\lambda_{D-1}\}\in\Re^{D} \tag{9}\] \[\lambda_{i} = \left\{\begin{array}{ll}F_{H}(i)&\Bigl{(}0\leq i<\tfrac{D}{2} \Bigr{)}\\ F_{V}\Bigl{(}i-\tfrac{D}{2}\Bigr{)}&\Bigl{(}\tfrac{D}{2}\leq i<D\Bigr{)}\end{array}\right.\]  
This feature vector \(\overrightarrow{G}\) is fed to the sequence vector generator.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 5 Experiments
### Datasets  
Two electromagnetic signal datasets are considered in this paper, i.e., _Mobile_ dataset and _Radio_ dataset.  
The _mobile_ dataset contained electromagnetic signals from 10 mobile phones, including three Glory-10 mobile phones, one Huawei-P9 mobile phone, three Apple-6s mobile phones, one Meizu-X8 mobile phone, and two OPPO-R11 mobile phones. The _Radio_ dataset was taken from the electromagnetic signals collected from 10 radio stations, including three MESHs_1 UE radio stations, three MESHs_2 UE large radio stations, three round small radio stations, and one round large radio station. After implementing the filter, energy detection, slice, and STFT operations on the original signals, a series of \(224\times 224\) labeled complex matrices were generated. By separating the real and imaginary parts, the time-frequency matrix samples of the dimension of \(2\times 224\times 224\) could be established. Finally, the dataset with the signal samples of the \(3\times 224\times 224\) dimension could be constructed with some splicing operations (the corresponding mean values of the first and second dimensions were set to be the third dimension). In order to reflect universality, we conducted five experiments for each algorithm. Each experiment randomly selected six out of the ten targets as the known electromagnetic signals, such as targets (labeled 0-5), and the remaining four targets as the unknown electromagnetic signals, such as targets (labeled 6).
## 11 Privacy and Security
### Data Privacy and Security in Smart Homes  
In general, security in smart homes is related to data privacy and the safety of smart home residents. According to the research conducted by Mr. [PERSON] in reference [113], the safety of smart homes presents three critical issues:  
1. Unauthorized entities are not allowed to access data. Therefore, impenetrability and decryption are essential in security issues related to smart homes;
2. Introducing entities that can access users' data and privacy;
3. Creating a safe environment for smart home users. To protect users' privacy, symmetric and asymmetric essential encryption methods are usually used. The result of the encryption process is called cipher text. Also, the message that needs to be encrypted is called plaintext, which is converted into cipher text by a particular function called the key. The symmetric method uses data decoding, which is not the case in asymmetric. Also, all classical cryptography is symmetric, for example: DES, 3 DES, and AES. Unlike symmetric encryption, in asymmetric encryption, a pair of public keys are used to encrypt and maintain confidentiality, and a private key is used for decryption. There is a mathematical relationship between them, for example, RSA, DH, and DSA [114]. In general, a smart home is vulnerable to two types of internal and external threats [115]. In both types of existing threats, hackers intend to compromise the smart home infrastructure [116; 117]. Table 2 shows the threats and possible security damages in smart homes. As mentioned, all smart home systems work through home automation protocols. In addition, protocols are similar to languages through which smart home systems communicate with each other and execute commands. Table 3 describes the features and disadvantages of each of the protocols reviewed in this article. Figure 13: The 6 LoWPAN protocol stack.
## 3 Dataset
### Study Area  
This study was conducted on the roads of the Kingdom of Saudi Arabia, and since there is no convenient dataset available, it was collected and compiled by the authors. Road images for the proposed study were selected from the municipality of the Rabigh region in Saudi Arabia. Rabigh is located on the eastern coast of the Red Sea in the Tihama region of western Saudi Arabia. It is one of the governorates of Makkah Al-Mukarramah. It has an area of about 14,000 km\({}^{2}\) and an inhabited population of almost 93 thousand [42] and is considered one of the most important historical, religious, and economic cities. Rabigh city has 13 major neighborhoods; in our work, we focus on five major neighborhoods, which are: Al Murjaniyah District, Al-Safa District, Al-Marwa District, Al-Nozha District,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Research** & **Method** & **Dataset** & **Country** & **Classes** & **Results** \\ \hline
[30] & CNN, AlexNet, VGG16,19, GoogleNet & 4663 images & Undefined & Vertical, horizontal, and diagonal cracks & VGG-16 = 87.83\%, AlexNet = 85.14\% \\ \hline
[4] & CNN & 9053 images & Japan & Cracks, rutting, bump, pothole, separation, and blur & Recalls 71\% and precisions 77\% \\ \hline
[12] & ANN & 3923 images & Czech, Slovak & Cracks, potholes, rutting, bump, separation, and deteriorated markings & F1 score 67 \\ \hline
[32] & Mask-RCNN & RDD2018 (9053 images) & Japan & Cracks, rutting, bump, pothole, separation, and blur & F1 score 0.528 \\ \hline
[33] & Faster R-CNN & RDD2020 (26,336 images) & Japan, Czech, India & Longitudinal cracks, transverse cracks, alligator cracks and potholes & F1 score 0.542–0.536 \\ \hline
[34] & FRCNN & RDD2020 (26,336 images) & Japan, Czech, India & Longitudinal cracks, transverse cracks, alligator cracks and potholes & F1 score of 0.487 \\ \hline
[35] & Faster R-CNN & RDD2018 (9053 images) & Japan & Cracks, rutting, bump, pothole, separation, and blur & Mean F1 score 0.6255 \\ \hline
[41] & YOLOv4 & Udemy website, Lebanon images + Internet images & Lebanon, undefined & Potholes/Non potholes & Recall 81\% Precision 85\% \\ \hline
[36] & YOLOv4 & RDD2020 (26,336 images) & India, Czech, and Japan & Longitudinal cracks, transverse cracks, alligator cracks and potholes & F1 scores 0.628 and 0.6358. \\ \hline
[37] & YOLO & RDD2018 (9053 images) & Japan & Cracks, rutting, bump, pothole, separation, and blur & F1 score 0.62 \\ \hline
[39] & u-YOLO & RDD2020 (26,336 images) & India, Czech, and Japan & Longitudinal cracks, transverse cracks, alligator cracks and potholes & F1 score of up to 0.67 \\ \hline
[40] & YOLO & 4000 images & USA & Longitudinal, lateral, and alligator crack, pothole, blurred steed line, blurred crosswalk & mAP 97.98\% \\ \hline
[38] & YOLOv5 & RDD2020 (26,336 images) & India, Czech, and Japan & Longitudinal cracks, transverse cracks, alligator cracks and potholes & F1 score of 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._and AlNaseem District. Road selection and data collection time were made based on road accessibility, weather conditions, and traffic volume.
# Blind Source Separation of Intermittent Frequency Hopping Sources over LOS and NLOS Channels +
## 3 Method
### DOA Estimation  
To formulate the problem of estimating the DOA of sources at each time instant \(0\leq t\leq T\) as a sparse representation problem, we again define a dictionary matrix and a matrix of unknowns. A grid comprising \(G_{d}\) possible DOAs, \(\Theta=[\theta_{1},\theta_{2},\ldots,\theta_{G_{d}}]\), is used to define the \(J\times G_{d}\) DOA modal dictionary  
\[\mathbf{H}_{d}=[\mathbf{a}(\theta_{1}),\mathbf{a}(\theta_{2}),\ldots,\mathbf{ a}(\theta_{G_{d}})], \tag{5}\]  
where \(\mathbf{a}(\theta_{i})\) is the steering vector associated with DOA \(\theta_{i}\) defined as \(\mathbf{a}(\theta_{i})=[1,a(\theta_{i}),\ldots,a^{l-1}(\theta_{i})]^{\prime}\) (see Equation (1) for other definitions).  
We let \(\mathbf{x}_{d}(t)\) be the \(G_{d}\)-length vector of unknown complex amplitudes associated with grid DOAs at time \(t\). Across all \(T\) sampling instants, the \(G_{d}\times T\) matrix of complex amplitudes is defined as \(\mathbf{X}_{d}=[\mathbf{x}_{d}(1),\mathbf{x}_{d}(2),\ldots,\mathbf{x}_{d}(T)]\). Given observations \(\mathbf{Y}\), a sparse estimate of the DOA pattern \(\mathbf{X}_{d}\) (or, equivalently, an estimate of \(\mathbf{x}_{d}(t)\) for all \(t\)) is denoted as \(\hat{\mathbf{X}}_{d}\) (or \(\hat{\mathbf{x}}_{d}(t)\)), and it is found by solving the following optimization problem:  
\[\hat{\mathbf{X}}_{d}=\underset{\mathbf{x}_{d}}{\text{arg min}}\|\mathbf{Y}- \mathbf{H}_{d}\mathbf{X}_{d}\|_{2}^{2}+\lambda_{d}\sum_{t=1}^{T}\left\|\mathbf{ x}_{d}(t)\right\|_{1}. \tag{6}\]  
Similar to the optimization problem for estimating the frequency hops, the formulation includes hyperparameter \(\lambda_{d}\) that controls sparsity.
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
## 5 Conclusions  
In this paper, a fault diagnosis model based on an adaptive sparse attention network is proposed. Through the soft threshold filtering of attention network weight, the diagnostic weight of the signals is adaptive and sparsely represented, and the signals of different representation domains are used as the model input for comparative analysis. The results show that the envelope spectrum of the bearing vibration signal has a more effective representation ability for fault information, and the proposed sparse attention model can automatically screen out the characteristic information that is more sensitive to faults. In addition, the proposed adaptive sparse network model achieves 97.6% accuracy and 196.54 s convergence time, which is 21.2% more accurate and 113.47 s shorter than the local method, and 193.24 s shorter than the global method, although the accuracy decreases by 0.4%. Therefore, the proposed model has better comprehensive performance, and the visualization has a better interpretation.  
In the future, the knowledge screening network model can be used to further explore the differences in weights of sensitive features corresponding to different faults and their variation patterns with working conditions, etc., in order to provide directional guidance for an in-depth explanation of the fault mechanism and achieve the purpose of feedback maintenance. The problem of collaborative updating of the model and real-time data will be studied later and validated in real industrial fields.  
Conceptualization, Q.J. and B.B.; methodology, Q.J. and X.H.; software, A.H. and J.J.; validation, Z.M. and Q.J.; formal analysis, Q.J. and X.H.; data curation, A.H. and J.J.; writing--original draft preparation, Q.J. and Z.M.; funding acquisition, Z.M. 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 No. 52201351).  
Not applicable.  
The authors are grateful to all commenters for their valuable and constructive comments.  
The authors declare no conflict of interest.
## 4 Experiments
### The Impact of Resolution on Pose Estimation
#### 4.1.2 Implementation Details  
This experiment aims to explore the impact of resolution on pose optimization. The experiment adopts a joint pose optimization framework similar to BARF and uses single-resolution hash encoding to extract features from positions. The resolution size of the single-resolution hash encoding is set to 16, 32, 64, 96, 128, 258, 1024, with the number of layers set to 3, aiming to extract features more fully at this resolution. At the same time, we use the Adam optimizer, applying different learning rates and learning rate scheduling strategies for pose parameters and neural radiance field parameters. For the optimization of pose parameters, an exponential decay strategy is used with a decay coefficient of 0.99988, and the learning rate decays from 1 \(\times\) 10\({}^{-3}\) to 1 \(\times\) 10\({}^{-5}\). For the optimization of neural radiance field parameters, the initial learning rate is set to 1 \(\times\) 10\({}^{-2}\), and a cosine annealing learning rate adjustment strategy is adopted, with the length of the learning rate annealing cycle set to 40,000. The entire training process was carried out for 40,000 iterations.
## 2 Theory, Requirements and Synthesis
### Micro-Ring Resonator as a Bandstop Filter  
While there are multiple approaches to overcoming chromatic-dispersion-induced power fading in RoF links, this paper investigates an approach from the rapidly expanding field of MWP. With well-known advantages such as the independence of the radio frequency, excessive bandwidth capabilities, low attenuation, immunity to EMI, reconfigurability, MWP offers new solutions for RF signal (in the microwave and mmW ranges) generation, detection and/or processing. Furthermore, with integrated photonics and narrow bandwidth laser sources coherent applications became possible. An example of interest is the integrated silicon photonic add/drop MRR as an optical (multi)band stop/pass filter. Such a filter exhibits a periodic filter response with resonances spanned by the free spectral range (FSR) parameter. Modern silicon photonics technology allows for MRRs with high FSR values, which effectively eliminates the often-unwanted periodicity within a certain wavelength range.  
We already proposed and investigated the application of a basic single add/drop MRR to establish a nearly two-color (i.e., OSSB) signal that is renowned for its robustness to chromatic dispersion [17]. A significant decrease in power fading was achieved; we managed to limit the ripple in the frequency-dependent PP function to 3 dB within the bandwidth of \(B=20\) GHz and down to 4 dB in the length-dependent PP function. However, we noticed the possibly unfavorable property of thermal dependence and foresaw future activities in that direction.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 3 Modern Internet Transport Protocols
### Evolution of HTTP Protocols
Also, a larger initial window was proposed to reduce web object download times with a moderate cost in terms of increasing the congestion and loss rate [3]. HTTP/3 is a new major version of the HTTP protocol recently standardized in RFC 9114 [22]. Essentially, it is the same as HTTP/2 but runs over QUIC instead of TCP. With stream multiplexing and per-stream flow control, QUIC ensures reliability at the stream level and congestion control across the entire connection. Also, since QUIC multiplexes streams at the transport layer, it eliminates the head of line blocking problem at the transport layer which provides better HTTP performance. Moreover, running HTTP over QUIC reduces the time consumed in connection setup and supports early data transmission compared to HTTP/2 over TCP. Finally, it is worth noting that HTTP/2 over QUIC was recently renamed HTTP/3 by the Internet Engineering Task Force (IETF) in 2018 [19]. Figure 4 illustrates the difference in protocol stacks for HTTP/2 running over TCP versus HTTP/3 running over QUIC.
## 2 Materials and Methods
### Total RNA Isolation and qRT-PCR  
Total RNA isolation was performed based on the MMY method [28]. Rice stem cells (30 mg) were ground in liquid nitrogen using a pestle and mortar. Total RNA was then extracted from the ground powder using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) with chloroform. The complementary DNA (cDNA) was synthesized using SMART(r) MMLV Reverse Transcriptase (Clontech, Mountain View, CA, USA) following the manufacturer instructions. A portion (1 mL) of synthesized cDNA was used as the template for the qRT-PCR. The analysis was performed using iQ SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) and a Real-Time PCR detection system (CFX96, Bio-Rad, Hercules, CA, USA). The relative expression of mRNA for major genes associated with skin aging on HFB cells were analyzed by qRT-PCR. Primer sequences are listed in Table 2. The qRT-PCR was performed by normalization to the mRNA level of GAPDH. GAPDH was used as a housekeeping gene for the qRT-PCR. The PCR thermal cycles involved a three-step protocol: activation at 95 \({}^{\circ}\)C for 3 min, followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 10 s, and an annealing/extension at 60 \({}^{\circ}\)C for 30 s.
## 4 Results and Discussion  
### Behaviour of PIC Mean for APP Feedback  
Figure 5 is evaluated for the given parameters; \(K/N=20/10\), (5,7) encoding, \(L=1000\), \(E_{b}/N_{0}=6\) dB. In Figure 5, we see the results for the sample mean \(\hat{n}_{k,a}^{(n)}\) at the PIC output plotted against iterations for a system with the (5,7) encoding scheme. For this data random interleaving was used and the data was averaged over 1000 experiments. Plotted on the same graph are the expectation of the residue multiplied by the transmitted data \(\mathcal{E}\left[x_{k}\sum_{j\
eq k}c_{kj}\hat{x}_{j,R}^{(n-1)}\right]\) and the sum of the sample mean and expectation of the _residue_ multiplied by the transmitted data \(\hat{n}_{k,a}^{(n)}+\mathcal{E}\left[x_{k}\sum_{j\
eq k}c_{kj}\hat{x}_{j,R}^{ (n-1)}\right]\) the same conditions. The _residue_ expectation was calculated using knowledge of the transmitted data. We note from the Figure 5 that when the sample mean and the _residue_ are summed, the result is near unity; confirming that the _residue_ is a key difference between the PIC output statistics for the APP-feedback and extrinsic-feedback cases. This result also confirms that the expectation in (36) is the key problem to solve when considering the PIC output mean for extrinsic feedback.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Systems** & **Maximum Users** & **Systems** & **Maximum Users** \\ \hline PIC with no Combining & 9 & PDA with no Combining & 16 \\ \hline PIC with Full MRC & 13 & PDA with Full MRC & 17 \\ \hline PIC with TMRC & 15 & PDA with TMRC & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum Users for MRC Variants, \(N=8\), \(E_{b}/N_{o}=5\) dB, (5,7) code.  
We note from Figure 5 that the deviation of the sum of sample mean and residue from unity. It is also revealed that when calculating the mean based on extrinsic parameters for the case where APP data is passed in the system, that:  
* This output mean drops below unity.
* This output mean value is the sum of the APP mean and the residue.  
We therefore account for the deviation of the mean from unity by the fact that we are calculating an extrinsic mean in an APP system.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 4 xPON Activation Processes
### GPON Activation States
State O5, Operationstate: In this state, the ONU can now send data, physical layer operations and administration and maintenance (PLOAM) messages according to the OLT instructions. Once the network is equalized and all the ONUs are working with the correct equalization delay, all upstream bursts will be synchronized among all ONUs. State O6, POPUP state: An ONU enters this state when any of the LoS or LoF alarms (if the signal is lost or the frame is poorly assembled) is detected. Therefore, if this condition occurs, the ONUs immediately stop sending data. After the POPUP status occurs, the ONU first attempts to retrieve the optical signal, recover the synchronization of the GTC frame and remove the LoS/LoF alarm. The ONU goes either to the Operationstate or to the Rangingstate according to the particularly targeted POPUP messages. If the ONU receives the targeted POPUP message, it returns to the Ranging State. If the ONU cannot restore the optical signal or reset itself to recover the GTC frame synchronization, it does not receive a targeted POPUP message and is moved to the Initialstate. This is where the TO2 timer is used (the recommended time for the timer is 100 ms). State O7, Emergencystop state: An ONU that receives a Disable Serial Numberwith the \"deactivate\" option goes to the emergency stop state and shuts off the laser. During this state, the ONU is not allowed to send any data. If a failure on the deactivated ONU is resolved, the OLT can activate the ONU to return it to its functional state. The activation is accomplished by sending the Disable Serial Number message with the \"enable\" option. Subsequently, the ONU returns to the Standbystate, and all parameters are discarded and retrieved.