esa-sceva/satcom-chunk-collection huggingface dataset:
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
###### Abstract  
Machine learning in photonics has potential in many industries. However, research on patent portfolios is still lacking. The purpose of this study was to assess the status of machine learning in photonics technology and patent portfolios and investigate major assignees to generate a better understanding of the developmental trends of machine learning in photonics. This can provide governments and industry with a resource for planning strategic development. I used data-mining methods (correspondence analysis and K-means clustering) to explore competing technological and strategic-group relationships within the field of machine learning in photonics. The data were granted patents in the USPTO database from 2019 to 2020. The results reveal that patents were primarily in image data processing, electronic digital data processing, wireless communication networks, and healthcare informatics and diagnosis. I assessed the relative technological advantages of various assignees and propose policy recommendations for technology development.  
machine learning; photonics; patent portfolio; correspondence analysis +
Footnote †: journal: 2022  
developmental potential of machine learning in photonics [14; 15; 16; 17]. They did not study the topic from a macro perspective or indicate the individual positioning of technology distribution and technology developers of machine learning in photonics. Specifically, no study has examined technological fields that employ the synergy between machine learning and photonics and the technological advantages of various assignees. Accordingly, this study investigated these topics through patent portfolio analysis.  
This study explored the status of technology distribution and technology developers--namely, the current development focus of this technology. This study employed patent analysis to analyze and compare the focus of each technology developer, along with correspondence analysis and K-means clustering. The patent analysis focused on the United States Patent and Trademark Office (USPTO) database from 2019 to 2020. First, this study analyzed the mainstream technology of machine learning in photonics to understand current developments of the technology. Next, this study analyzed the main assignees and patent portfolios and identified different strategic groups to clarify the trends and positioning of technology developers. This study used the patent portfolio figure to understand the relationship between assignees and technology and used the intuitive effect of the geometric figure to effectively present and reveal the technology portfolio of technology developers, to provide information to serve as a reference for government policies.
## 2 Methodology and Analysis  
Despite recent advances, an up-to-date overview of these non-thermal technologies with particular reference to their application in the winemaking process is lacking. Therefore, the present review aims to provide important information on five selected non-thermal technologies, namely HPU, HHP, PEF, UHPH, and CP, which will certainly be useful for all those involved in the wine sector. The applications, benefits, and challenges of selected technologies are listed in Table 1. To compose this review article, only scientific papers published between 2019 and 2024 (including those available at the time of writing this review in 2024) were retrieved from trusted and reputable online databases such as Google Scholar, Science Direct, Web of Science, and Scopus, using keywords such as non-thermal technologies, high-power ultrasound, high hydrostatic pressure, pulsed electric field, ultra-high pressure homogenization, cold plasma, winemaking, microbial safety, sulfur dioxide, and quality characteristics. Some other useful references have also been added to supplement this review. In total, a comprehensive and detailed analysis of the scientific repository resulted in 85 papers, including 55 research articles, 20 reviews, and 10 book chapters. Figure 1 shows the number of published articles per year and in relation to each technology about the analyzed topic during the last five years. As can be seen, 2020 and 2021 were the years with the highest number of published papers, while cumulatively the most studied technologies were pulsed electric field and high-power ultrasound, followed by high hydrostatic pressure. In addition, the strategy employed in composing this review article is presented in Figure 2. The next section provides some valuable insights into the latest applications of the aforementioned non-thermal technologies (HPU, HHP, PEF, UHPH, and CP) at different stages of winemaking and the changes in certain wine quality characteristics, particularly in phenolics, color, and aroma, based on the collected reports.
###### Abstract  
Urban infrastructure (UI), subject to ever-increasing stresses from artificial activities of human beings and natural disasters due to climate change, assumes a key role in modern cities for maintaining their functional operations. Therefore, understanding UI resilience turns essential. Based on the Pressure-State-Response (PSR) model, this paper built a comprehensive evaluation index system for urban infrastructure resilience evaluation. Four municipalities, including Beijing, Tianjin, Shanghai, and Chongqing in China, were selected for the case study, given their specific significance in terms of geographical location and urban infrastructure scale. Temporal differences of UI resilience in those four cities during 2002-2018 were explored. The results showed that: (1) The various stages of PSR relative importance for the urban infrastructure resilience development in the four cities were different. The infrastructure status, primarily resource environmental benefit, had the most significant effect on urban infrastructure resilience, accounting for 38.73%. (2) While Shanghai ranked first, the levels of urban infrastructure resilience in four cities were generally poor in 2002-2018 with continuously low resilience. (3) Significant differences were found in the resilience levels associated with the three stages of pressure, state and response failing to form a positive development cycle, with the poorest pressure resilience. This paper puts forward some recommendations for providing scientific support for urban resilient infrastructure development in four municipalities in China.  
urban infrastructure; resilience; pressure-state-response; Chinese Municipalities; temporal differences
## 4 Validation by Numerical Simulation and Results
### Effects of Brake Frame on the Net Protection System for Debris Flow Impact  
The horizontal wire ropes were numbered from 1 to 7 starting from the bottom wire rope. The tensile forces obtained at the end of wire rope without brake frames according to time are shown in Figure 12. Without installing brake frames in the net protection system, the maximum tensile forces (reaction force) among all seven wire ropes were obtained to be 117.8 kN and 117.0 kN, respectively, in wire ropes 3 and 4. The brake time (time required to attain maximum tensile force) for wire ropes 3 and 4 was observed as 0.09 s and 0.12 s, respectively, as shown in Figure 12. The stress contours in the net protection system during this process are shown in Figure 13. When the impact block was rolled down the slope, it made an impact with the wire rope 2 in the first step and a tensile force of 32.2 kN was observed to be developed in wire rope 2. In the next step, the impact block made impact with wire ropes 3 and 4 developing maximum tensile forces in it among all the seven wire. Afterwards, the impact block was observed to be rebounding towards the slope and making another impact with the net protection system due to which the tensile force developed in all the wire ropes except 3 and 4 were observed to be around 25 kN, which lasted for 1.2 s after the second impact. The impact block was observed to get rebounded to a relatively greater extent because it produced relatively smaller displacement in the net protection system.  
Figure 11: Height and velocity of impact block while rolling, (**a**) height of impact block; and (**b**) velocity of impact block.  
Figure 12: Tensile force acting on seven wire ropes without brake frame.  
When the brake frames were installed and the impact block was rolled down the slope, the maximum tensile force developed in wire rope 4 was observed to be 9.9 kN in the brake time of 0.225 s, as shown in Figure 14. Further, the maximum tensile forces were developed in wire ropes 5 and 6 due to the impact and were recorded as 28.4 kN and 29 kN in brake times of 0.27 s and 0.33 s, respectively. The stress contours in the net protection system during all this process are shown in Figure 15.  
Figure 14: Tensile force acting on seven wire ropes with a brake frame.  
Figure 13: Stress distribution in the net protection system after impact, without using brake frame, (**a**) 0.015 s after impact; (**b**) 0.09 s after impact; (**c**) 0.195 s after impact; and (**d**) 0.27 s after impact.
## 2 Dataset Description
### Data Acquisition  
The smartphone used in the experiments was a Samsung Galaxy Nexus I9250 with the Android OS version 5.1.1 and equipped with a Bosh BMA220 acceleration sensor. This sensor is a triaxial low-g acceleration sensor. It allows measurements of acceleration in three perpendicular axes, and allows acceleration ranges from \(\pm 2g\) to \(\pm 16g\) and sampling rates from 1 KHz to 32 Hz. The Android OS both limits to \(\pm 2g\) with a resolution of 0.004\(g\) the acceleration range, and takes samples at a maximum frequency of 50 Hz. However, the Android OS does not guarantee any consistency between the requested and the effective sampling rate. Indeed, the acquisition rate usually fluctuates during the acquisition. For the experiments presented in this paper, we resampled the signal in order to have a constant sampling rate of 50 Hz, which is commonly used in literature for activity recognition from data acquired through smartphones [28; 29; 30]. The accelerometer signal is for each time instant made of a triplet of numbers (x, y, z) that represents the accelerations along each of the 3 Cartesian axes.  
We used also the smartphone built-in microphone to record audio signals with a sample frequency of 8000 Hz, which are used during the data annotation process.  
The subjects were asked to place the smartphone in their front trouser pockets: half of the time in the left one and the remaining time in the right one.  
Acceleration triples and corresponding audio signals have been recorded using a mobile application specially designed and implemented by the authors, which stores data into two separated files inside the memory of the smartphone.
## 5 Discussion  
### NDV1 Time Series Reconstruction  
To ensure the reliability of optical remote sensing-based studies, particularly in cloudy and rainy regions, a basic step is to improve the data quality of \(NDVI\) time series by introducing a further pre-processing step that reduces noise caused by poor atmospheric conditions in the time series, and thus, key points within the growth cycle can be effectively captured [26]. In that light, the UE-WS method used in this study involves a robust approach to extract the upper envelope of the \(NDVI\) time series in order to remove outliers and then uses the WS filter to smooth random noise in essence. In our experiments, the sample results we present above clearly demonstrate the advantages of UE-WS when evaluated on the basis of general noise reduction under a variety of conditions, as illustrated in Figure 4a. This is not surprising, as the combination of another robust reconstruction method based on envelope detection and the SG filter has shown to be more stable for high temporal resolution \(NDVI\) time series reconstruction compared to conventional methods [51], and similar findings have also been reported in another study that proposed a two-level filtering approach for noise removal \(NDVI\) that employed the BISE and SG filter [59]. The question can be asked as to why a new type of approach might be needed since already existing ones have shown good performances. The answer is that it is not a matter of the nature of the method, but the type of problem for which it was designed, for instance, BISE and its improved method have proven effective at eliminating the influence of low-value noise caused by clouds and shadows; however, they were originally designed for daily and hypertemporal \(NDVI\) time series [60] and may perform poorly when there is a long-term, gradual decrease in \(NDVI\) as the one investigated in our study while using the S2 approach in a cloudy area such as Germany. Additionally, based on the reality that the phenology of vegetation is consecutive and gradual, upper envelope detection in other approaches [51] moves chronologically only forward along the time series \(NDVI\) while eliminating low-value noise and retaining high-value \(NDVI\) as envelope nodes. While, our approach provides the option to process the time series in both forward and backward directions, enhancing the flexibility and accuracy of extracting the upper envelope. Finally, the WS filter chosen to smooth the remaining speckles demonstrates high performance, as verified for time series reconstruction and vegetation phenology extraction [33; 61].
## 3 GNSS-Free IoT Localization Methods
### Localization Accuracy Improvement Methods
To solve this restriction, the authors in [82] proposed a new RSSI and ToA-based localization method. Table 2 provides a summary of different localization methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Localization Technique** & **Method** \\ \hline [PERSON] et al. (2019) [57] & RSSI & Wiener Filter for noise decrement \\ [PERSON] et al. (2021) [58] & RSSI & GSAM to decrease interference effect \\ [PERSON] et al. (2019) [59] & RSSI & Six algorithm for non-Gaussian noise decrement \\ [PERSON] et al. (2019) [63] & RSSI & NN for Location estimation \\ [PERSON] et al. (2019) [67] & RSSI & DNN for location estimation \\ [PERSON] et al. (2021) [68] & RSSI & DNN for location estimation \\ [PERSON] et al. (2020) [69] & RSSI & Exploit from UAV for accuracy improvement \\ [PERSON] et al. (2019) [54] & RSSI-based Fingerprint & Analysis of regression and ML models \\ [PERSON] et al. (2020) [72] & RSSI-based Fingerprint & High-resolution satellite images adoption \\ [PERSON] et al. (2018) [73] & RSSI-based Fingerprint & Use interpolated fingerprint map \\ [PERSON] et al. (2018) [74] & TDoA & Road map and movement speed consideration \\ [PERSON] et al. (2020) [75] & TDoA & Combine [PERSON]’s and [PERSON]’s TDoA algorithms \\ [PERSON] et al. (2019) [78] & TDoA & adopts ultrasonic signals \\ [PERSON] et al. (2019) [79] & TDoA-based fingerprint & Random Forest and NN \\ [PERSON] et al. (2020) [44] & AoA and RSSI & Dynamic path-loss exponent consideration \\ [PERSON] et al. (2019) [81] & RSSI and ToA & Regression analysis \\ [PERSON] et al. (2020) [82] & RSSI & ToA & Regression analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of proposed accuracy improvement methods for localization regardless of system category.
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 2 Materials and Methods  
We have studied the notched samples made from hot-rolled sheets with a thickness of 3-5 mm made of industrial steels 09G2S (similar to steel A 516-55) and 45 (similar to steel 1045) with a ferrite-pearlite structure and nominal chemical composition shown in Table 1[12].  
The samples were tested under static tension using a loading schedule described in [23]. Flat samples with an edge notch were made according to ASTM E1930/E1930M-17. In total, 50 samples were made of 09G2S steel and 10 samples of steel 45. Fatigue cracks were grown in all samples by cyclic tensile loading with a maximum cycle stress of \(\sigma_{max}\approx 0.6\cdot\sigma_{y}\) (\(\sigma_{y}\) is the yield strength). The loading stops when fatigue crack developing in the lateral notch area reaches a certain length. Nominal crack lengths vary from 3 to 15 mm. Thus, the total length of the notch and crack is 15 to 28 mm depending on the sample.  
At the next stage, testing of the samples with fatigue cracks was carried out by Instron 8801 testing machine (Figure 1a). AE signals were recorded during the tests using A-Line 32D industrial system. The measuring path consisted of resonant sensors GT200 (LLC \"Global test\") which have resonance frequency 180 kHz and preamplifiers of the electrical signal PAEF-014. The total noise of the equipment is 26 dB in reference to 1 \(\upmu\)V at preamplifier input. The threshold for acoustic signals discrimination was set to 40 dB.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Steel**} & \multicolumn{8}{c}{**The Content of Chemical Elements (\% wt)**} \\ \cline{2-10}  & **C** & **Mn** & **Si** & **Cr** & **Ni** & **Cu** & **As** & **S** & **P** \\ \hline
09G2S (AS16-55) & \(\leq\)0.12 & 1.3–1.7 & 0.5–0.8 & \(\leq\)0.3 & \(\leq\)0.3 & \(\leq\)0.3 & \(\leq\)0.08 & \(\leq\)0.04 & \(\leq\)0.035 \\
45 (1045) & 0.42–0.5 & 0.5–0.8 & 0.17–0.37 & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.25 & \(\leq\)0.08 & \(\leq\)0.04 & \(\leq\)0.035 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition of the studied steels.  
The loading diagram is shown in Figure 1b. Two triangular cycles at the initial stage are necessary to equalize the internal mechanical stresses that occurred during the production and placing of the samples. The main loading cycle consists of four stages: first, the load gradually increases from 0 to 0.5 \(\sigma_{max}\), second--from 0.5 \(\sigma_{max}\) to 0.75 \(\sigma_{max}\), third--from 0.75 \(\sigma_{max}\) to \(\sigma_{max}\). The fourth stage is carried out with a stress exceeding \(\sigma_{max}\) by 25%. A more detailed description of the experiment is given in [12].
## 4 Discussion
Additionally, spraying inducible (3S)-linalool on OsLIS-silenced rice plants could repel infestation by the rice brown planthopper Nilaparvata lugens [57]. TPS family genes that catalyze the production of linalool have also been found in _fresia hybrids_[58], _Malus domestica_[59], and _Vitis vinifera_[60]. TPS genes are mainly responsible for the production of volatile terpen in plants [52]. In the present study, the LIS genes we found in _D. moniliforme_ and _D._ \"H1\" that synthesize linalool belong to the TPS gene family. TPS is the primary enzyme that catalyzes the formation of linalool from the substrate GPP. In orchid flowers, the high volatility of terpenoids promotes floral fragrances. It was confirmed that linalool and geraniol were the main floral compounds in _Phalenopsis bellina_[61]. In in vitro experiments, the TPS10 gene uniquely converted GPP to linalool in _D. officinal_[24]. In our present research, combined analysis of volatile compounds and transcriptomes in two _Dendrobium_ species revealed that four LIS genes belonging to the TPS family, which were selected from the terpenoid biosynthesis, were involved in the positive regulation of floral formation in high-scent species. We speculate that inducing specific LIS gene expression can increase the production of linalool, which promotes the formation of flower fragrance. These results deepen our understanding of the molecular mechanism of _Dendrobium_ flowers. It is valuable to further study the function of these genes, and these findings will provide a valuable reference about the terpene biosynthetic pathway in orchids.
# Fusion Information Multi-View Classification Method for Remote Sensing Cloud Detection
###### Abstract  
In recent years, many studies have been carried out to detect clouds on remote sensing images. Due to the complex terrain, the variety of clouds, the density, and content of clouds are various, and the current model has difficulty accurately detecting the cloud in the image. In our strategy, a multi-view data training set based on super pixel is constructed. View A uses multi-level network to extract the boundary, texture, and deep abstract feature of super pixels. View B is the statistical feature of the three channels of the image. Privilege information View P contains the cloud content of super pixels and the tag status of adjacent super pixels. Finally, we propose a cloud detection method for remote sensing image classification based on multi-view support vector machine (SVM). The proposed method is tested on images of different terrain and cloud distribution in GF-1_WHU and Cloud-38 remote sensing datasets. Visual performance and quantitative analysis show that the method has excellent cloud detection performance.  
cloud detection; feature extraction network; fusion information; multi-view learning
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 2 Algorithm Description
### Synchronization Parameter Estimation  
Once the frame header of the burst signal is detected, the following work will be the synchronization parameter (frequency offset, phase offset, and timing error) estimation.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## Appendix A Proof of Lemma 1
### Cable Signal Vectorization  
The vector signal \(\mathbf{r}_{k}\in\mathbb{C}^{n_{F}\times 1}\) received at the BBU from the \(k\)-th EN over all the radio frequency channels can be obtained by vectorizing matrix \(\mathbf{R}_{k}\) in (10) as  
\[\begin{split}\mathbf{r}_{k}&=\text{vec}(\mathbf{Y}_ {k}\mathbf{H}_{c}^{\eta}+\mathbf{W}_{k})\\ &=\left(\mathbf{H}_{c}^{\eta}\otimes\mathbf{I}_{I_{F}}\right) \text{vec}(\mathbf{Y}_{k})+\text{vec}(\mathbf{W}_{k})\\ &=\left(\mathbf{H}_{c}^{\eta}\otimes\mathbf{I}_{I_{F}}\right) \mathbf{y}_{k}+\mathbf{w}_{k},\end{split} \tag{11}\]  
where \(\mathbf{y}_{k}\) is the radio signal received at EN \(k\)-th. The overall cable noise vector \(\mathbf{w}_{k}\) can be rewritten as  
\[\begin{split}\mathbf{w}_{k}&=\text{vec}(\mathbf{W}_ {k})\\ &=\text{vec}(\tilde{\mathbf{W}}_{k}\mathbf{G})\\ &=\left(\mathbf{G}^{T}\otimes\mathbf{I}_{I_{F}}\right)\text{vec} (\tilde{\mathbf{W}}_{k})\\ &=\frac{1}{\eta}\left(\mathbf{I}_{\frac{1}{\tilde{\eta}}}\otimes \mathbf{I}_{\eta}^{T}\otimes\mathbf{I}_{I_{F}}\right)\tilde{\mathbf{w}}_{k}. \end{split} \tag{12}\]  
It is important to notice that since the cable noise \(\tilde{\mathbf{W}}_{k}\) is white Gaussian and uncorrelated over cable pairs (see (10)), \(\tilde{\mathbf{w}}_{k}=\text{vec}(\tilde{\mathbf{W}}_{k})\) is also white Gaussian distributed as \(\tilde{\mathbf{w}}_{k}\sim\mathcal{CN}(\mathbf{0},\mathbf{I}_{I_{S}I_{F}})\). Hence, the covariance \(\mathbf{R}_{w}\) of the overall cable noise vector \(\mathbf{w}_{k}\) yields  
\[\begin{split}\mathbf{R}_{w}&=\mathbb{E}\left[ \mathbf{w}_{k}\mathbf{w}_{k}^{H}\right]\\ &=\frac{1}{\eta^{2}}\left(\mathbf{I}_{\frac{1}{\tilde{\eta}}} \otimes\mathbf{I}_{\eta}^{T}\otimes\mathbf{I}_{I_{F}}\right)\left(\mathbf{I}_ {\frac{1}{\tilde{\eta}}}\otimes\mathbf{I}_{\eta}^{T}\otimes\mathbf{I}_{I_{F}} \right)^{T}\\ &\overset{(a)}{=}\frac{1}{\eta}\left(\mathbf{I}_{\frac{1}{\tilde{ \eta}}}\otimes\mathbf{I}_{I_{F}}\right)\\ &=\frac{1}{\eta}\mathbf{I}_{n_{F}},\end{split} \tag{13}\]  
where the equality \({}^{(a)}\) comes again from the mixed-product property of Kronecker product. Equation (13) shows that the MRC allows to take advantages from the signal redundancy over the cable, which results in a reduction of the cable noise power by a factor of \(\eta\).  
The proof is completed by gathering the signals \(\mathbf{r}_{k}\) (11) received at the BBU from all ENs as  
\[\begin{split}\mathbf{R}&=\left[\mathbf{r}_{1}, \mathbf{r}_{2},...,\mathbf{r}_{M}\right]\\ &=\left(\mathbf{H}_{c}^{\eta}\otimes\mathbf{I}_{I_{F}}\right) \left[\mathbf{y}_{1},\mathbf{y}_{2},...,\mathbf{y}_{M}\right]+\left[\mathbf{w} _{1},\mathbf{w}_{2},...,\mathbf{w}_{M}\right]\\ &=\left(\mathbf{H}_{c}^{\eta}\otimes\mathbf{I}_{I_{F}}\right) \mathbf{Y}+\mathbf{W},\end{split} \tag{14}\]  
where \(\mathbf{Y}\) is the signal received by all ENs over all radio channels in (5).