esa-sceva/satcom-chunk-collection huggingface dataset:
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
###### Abstract  
Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (_SI_) and cloud liquid water path (_CLWP_)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the _RF_SI_ method was evaluated by using individual cases and time series observations. Results demonstrate that the _RF_SI_ method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional _SI_ and _CLWP_-based precipitation sounding algorithms, the accuracy and sounding rate of the _RF_SI_ method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the _RF_SI_ method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation.  
m _remote sensing Article_
## 4 Proposed Alternating Optimization Algorithm to Minimize MSE
### Optimization Problem Transformation  
From problem \(\mathcal{P}1\), we can easily see the high similarity of the configuration matrices \(\mathbf{R}\) and \(\mathbf{G}\). We introduce an auxiliary matrix \(\mathbf{V}\) to express this fact, which is expressed as  
\[\mathbf{V}=\left[\mathbf{v}_{1},\cdots,\mathbf{v}_{MK}\right]\in\left\{0,1 \right\}^{N\times MK}\,\ \mathbf{v}_{i}\in\mathbb{R}^{N\times 1}\,\ i\in \mathcal{I}, \tag{28}\]  
where \(\mathcal{I}\) is defined as \(\mathcal{I}\triangleq\left\{1,2,\cdots,MK\right\}\). More specifically, each column of the \(\mathbf{V}\) matrix can be given by  
\[\mathbf{v}_{k+(m-1)K}=\boldsymbol{f}_{k}\odot\boldsymbol{r}_{m}\,\ \forall\ k\in \mathcal{K},\ \ m\in\mathcal{M}. \tag{29}\]  
Correspondingly, constraint C3-C5 can be transformed into  
\[\sum_{i=1}^{MK}v_{n,i}\leq 1,\ \forall n\in\mathcal{N}, \tag{30}\]  
\[v_{n,i}\in\left\{0,1\right\}\,\ \forall i\in\mathcal{I}. \tag{31}\]  
Similarly, for the convenience of subsequent expression, we restate the channel vector of user \(k\) as  
\[\begin{split}\mathbf{g}_{k}&=\left[(\boldsymbol{f} _{k}\odot\boldsymbol{r}_{1})^{T}\boldsymbol{h}_{k,1}\,\ \cdots\,\ (\boldsymbol{f}_{k}\odot \boldsymbol{r}_{M})^{T}\boldsymbol{h}_{k,M}\right]^{T}\\ &=\text{vec}\Big{\{}\Big{[}\mathbf{v}_{k},\mathbf{v}_{k+K}, \cdots,\mathbf{v}_{k+(M-1)K}\Big{]}\odot[\boldsymbol{h}_{k,1},\boldsymbol{h} _{k,2},\cdots,\boldsymbol{h}_{k,M}]\Big{\}}\\ &=\text{vec}(\boldsymbol{V}_{k}\odot\boldsymbol{H}_{k})\end{split} \tag{32}\]  
where \(\boldsymbol{V}_{k}\triangleq\left[\mathbf{v}_{k},\mathbf{v}_{k+K},\cdots, \mathbf{v}_{k+(M-1)K}\right]\) represents the configuration vector in the IRS auxiliary matrix corresponding to user \(k\), and \(\boldsymbol{H}_{k}\triangleq[\boldsymbol{h}_{k,1},\boldsymbol{h}_{k,2}, \cdots,\boldsymbol{h}_{k,M}]\) represents all possible channel vectors corresponding to user \(k\).  
Substituting the above results into Equation (24), we can get the overall system MSE:  
\[\text{MSE}(\mathbf{V},\mathbf{W})=\sum_{k=1}^{K}\text{MSE}_{k} \tag{33}\] \[=\sum_{k=1}^{K}\left\{\begin{array}{c}\eta^{2}\Big{(}\text{ vec}(\boldsymbol{V}_{k}\odot\boldsymbol{H}_{k})^{T}\mathbf{W}\Big{)}^{2}+\eta \text{vec}(\boldsymbol{V}_{k}\odot\boldsymbol{H}_{k})^{T}\mathbf{w}_{k}+\eta^ {2}\Big{(}\text{vec}(\boldsymbol{V}_{k}\odot\boldsymbol{H}_{k})^{T}\mathbf{D} \Big{)}^{2}\\ \qquad\qquad\qquad+\eta(2n_{b}+1)\text{vec}(\boldsymbol{V}_{k}\odot \boldsymbol{H}_{k})^{T}\mathbf{D}+n_{b}^{2}+n_{b}+1\end{array}\right\}\]  
Based on the above discussion, we can transform problem \(\mathcal{P}1\) into the following problem \(\mathcal{P}2\):  
\[\begin{split}\text{s.t.}&\text{C1}:\sum_{k=1}^{K}\mathbf{w}_{k }u_{k}+\mathbf{D}\geq\mathbf{0}.\\ &\text{C2}:\sum_{k=1}^{K}\mathbf{w}_{k}u_{k}+\mathbf{D}\leq\frac{P_{ \text{max}}\tau}{h\
u}\mathbf{1},\\ &\text{C3}:\sum_{i=1}^{MK}v_{n,i}\leq 1,\ \forall n\in\mathcal{N},\\ &\text{C4}:v_{n,i}\in\left\{0,1\right\}\,\ \forall i\in\mathcal{I}.\end{split} \tag{34}\]  
**Proposition 1**.: _The problem \(\mathcal{P}2\) is equivalent to \(\mathcal{P}1\)._  
Proof.: See Appendix A.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 3 Biochemical Effects of EGCG  
The effects of EGCG on various biochemical pathways and potential pharmacological effects are summarised in Figure 2.  
EGCG administration led to a reduction in osteoclast differentiation and activation, achieved through an increased expression of OPG mRNA [19] and the inhibition of RANKL production [54]. Additionally, it showed a greater ability to enhance OPG mRNA expression in pluripotent stem cells compared to other green tea catechins [42; 43]. Numerous in vitro studies demonstrated that its supplementation was capable of suppressing RANKL-induced osteoclastogenesis [44; 47; 50]. As mentioned previously, another crucial factor in physiological bone growth is ALP, which could be significantly activated by it. This effect was attributed to the upregulation of _Runx2_ gene expression and the inhibition of acid phosphatase mRNA expression [26; 29; 33; 40; 41; 55]. _Runx2_, a key gene involved in osteoblast differentiation, was efficiently activated by it at low concentrations (typically up to 5 \(\upmu\)M) [29; 33; 40; 41]. Furthermore, in vitro studies showed that EGCG could overexpress other genes related to bone development, such as _Oss_ and _Bmp-2_, at concentrations of up to 10 \(\upmu\)M [26; 29; 31; 32; 33; 35; 40; 41].  
The compound also promoted the synthesis of VEGF [34; 56], stimulating bone growth by influencing the SAPK/JNK pathway and reducing interleukin 6 (IL-6) synthesis, which is responsible for bone resorption [54; 57; 58; 59]. EGCG was not only implicated in osteogenesis processes but also in the treatment of bone-related neoplasms, such as chondrosarcoma, through the activation of caspase-3 [60]. Finally, it affected MAP kinases involved in the regulation of osteoblast proliferation and differentiation, either attenuating or inhibiting their activity [27; 39; 57; 58]. It was also demonstrated that it could impede bone resorption by inducing a Fenton-type reaction, leading to the production of radicals that trigger osteoclast apoptosis [36]. Conversely, EGCG protected the osteogenic differentiation of human bone marrow-derived mesenchymal stem cells by mitigating hydrogen peroxide-related adverse effects [40].
## 4 System Simulation Analysis
### Analysis of Input and Intermediate Stages  
The input stage adopted a three-phase uncontrolled rectification strategy, with a three-phase sinusoidal AC input voltage characterized by a grid-side phase voltage RMS of 660 V and a frequency of 50 Hz. The grid-side filter inductors \(L_{\mathrm{p1}}\), \(L_{\mathrm{p2}}\), and \(L_{\mathrm{p3}}\); capacitor \(C_{\mathrm{1}}\); and simulation time were configured as 4 \(\times\) 10\({}^{-3}\) H, 5 \(\times\) 10\({}^{-3}\) F, and 0.2 s, respectively. For a detailed analysis of the simulation results, the oscilloscope data were imported into Origin. The simulation outcomes are presented in Figure 9a.  
As shown in Figure 9a, when the three-phase input stage operated steadily, the three-phase sinusoidal AC voltages \(U_{\mathrm{a}}\), \(U_{\mathrm{b}}\), and \(U_{\mathrm{c}}\) on the grid side were symmetrical and the voltage amplitude reached 933 V. According to Figure 9b, the input voltage \(U_{\mathrm{dc1}}\) quickly reached a steady state at 0.02 \(s\) and stabilized at 1602 V. The simulation results show that the three-phase uncontrolled rectifier input stage had good stability.  
The intermediate stage adopted an EPWM control strategy. The simulation model parameter transformer ratio \(n\) was set to 1:1. To specifically analyze the simulation results, the oscilloscope data were imported into Origin, and the simulation results are shown in Figure 10.  
The DC side input voltage \(U_{\mathrm{dc1}}\) was fed to the primary side of the intermediate stage transformer to obtain the inverted square wave \(U_{\mathrm{h1}}\). As shown in Figure 10a, the voltage amplitude was approximately 1602 V, with a frequency of 10 kHz. The inverted square wave \(U_{\mathrm{h1}}\) was converted to a secondary side coupled square wave \(U_{\mathrm{h2}}\) by the high-frequency isolation transformer. The voltage amplitude was seen to be about 1602 V and the frequency was 10 kHz. Applying the second-side-coupled square wave \(U_{\mathrm{h2}}\) to a secondary side uncontrolled rectifier H-bridge resulted in the intermediate-level output voltage \(U_{\mathrm{dc2}}\). As shown in Figure 10b, the voltage waveform rose smoothly and stabilized at 1602 V after 0.02 s. The simulation results demonstrate that the high-frequency isolated DC-DC stage exhibited excellent stability.  
Figure 9: (a) Grid-side three-phase input phase voltage. (b) DC side input voltage \(U_{\mathrm{dc1}}\).
###### Abstract  
The existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems that are deployed to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.  
communication standards; cyber security; intrusion detection system; smart grid; topology control; wireless sensor networks +
Footnote †: journal: Electronics Electronics
# Stress Analysis with Dimensions of Valence and Arousal in the Wild
## 2 Related Work  
### Existing Stress Dataset  
In recent years, numerous datasets related to emotion recognition have been published. However, only a few of them focus on a specific emotion, such as stress. Among the stress-related datasets, the WeSAD dataset [15] has been widely explored in recent stress recognition studies. The dataset consists of physiological and motion data collected from 15 subjects using wearable devices worn on the wrist and the chest. The ground truth was assigned based on the data collection protocol, where the neutral, stressed, and amused states were induced using stressors. The dataset aims at classifying the neutral, stressed, and amused state of the participants; however, it does not focus on stress with a granular classification of stress at multiple levels.  
MuSE dataset [16] consists of the recording of 28 University of Michigan college students in the process of answering monologue questions and watching emotional videos. After responding to the questions, the participants were asked to mark themselves on two emotion dimensions: activation and valence. After watching each video clip, the participants marked an emotion category (angry, sad, etc.).  
Similarly, the SWELL-KW dataset [17] is the result of experiments conducted on 25 subjects doing office work, such as writing reports, making presentations, reading e-mail, and searching for information. The collected data consists of the computer usage patterns, facial expressions, body postures, electrocardiogram (ECG) signal, and electrodermal activity (EDA) signal. All participants worked under all three conditions: no stress, time pressure, and interruptions.  
However, the WeSAD [15], MuSE [16], and SWELL-KW [17] datasets were collected in controlled laboratory conditions with limited context, and stressors such as time pressure and interruptions [17], or emotion elicitation clips [15; 16], while stress has many different levels and is variously expressed in each particular case. Therefore, we have built a large video dataset to solve these limitations. The dataset focuses on stress detection based on facial expression. The stress values also were evaluated with multi-levels. Besides, the collected data has diverse faces in various situations under the impact of many different factors. This eliminates the limitation of datasets collected from the lab and makes the dataset closer to the real-world environment. The comparison with related datasets is summarized in Table 1. Furthermore, the models used by these datasets for evaluation, while achieving high prediction accuracy, are inflexible and expensive to deploy in natural environments because these models need many items of equipment to measure many parameters. In contrast, with the proposed dataset, the models only use cameras to catch the user's face.
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
###### Abstract  
This paper presents a dominant channel occupancy (DCO) mechanism for the Wi-Fi backscatter uplink in the industrial Internet of things (IIoT). The DCO provides high-priority channel access and reliable burst transmission to the Wi-Fi backscatter devices, thereby enabling the Wi-Fi backscatter tag to deliver its tag information to the Wi-Fi reader without interference from neighboring legacy Wi-Fi devices to guarantee the timeliness and reliability of the IIoT system. For the former, we consider three types of medium access control (MAC) configurations: \"carrier sense multiple access with collision avoidance (CSMA/CA) starting with short inter-frame space (SIFS)\", \"freezing of the backoff period\", and \"reduced CWmin.\" In addition, the DCO uses the SIFS between burst packets to guarantee reliable burst transmission. To verify the effectiveness of DCO and determine a proper value for MAC parameters, we conduct experimental simulations under IEEE 802.11n PHY/MAC environments. The simulation results show that the reduced CWmin has the most significant effect on the channel occupancy. The Wi-Fi backscatter devices achieve much higher throughput than the separate cases when two or more configurations are used simultaneously. Moreover, the results exhibit that the use of SIFS between consecutive packets supports reliable burst transmission regardless of the transmission of the legacy Wi-Fi devices in the vicinity.  
d  
ominant channel occupancy; industrial Internet of things; MAC configuration; RF-powered device; Wi-Fi backscatter Article
## 5 Discussion
### Limitations and Future Work  
The proposed method has some limitations. Firstly, the method is highly reliant on the selection of parameter \(m\). To achieve optimal path following, different path types and environments may need different values of \(m\). Thus, tests are required to determine the optimal value of \(m\). Secondly, in practical environments, paths may be significantly varied, ranging from straight lines to curves and crossings, among others. Given the experimental results, the method appears proficient in following paths consisting of straight lines, although it may have limitations when dealing with more diverse paths. Additionally, the experiments described in this paper feature a certain level of regularity and specificity in corner size and path design. Although path segments with different angles and flight tasks are designed, it is necessary to increase the variety of tracked paths and further investigate the problem of path following under multiple forms of paths for robots in constrained spaces.
## 5 Results and Discussion
### Significance of the CVROSS  
The feasibility and the performance of the CVROSS have been demonstrated by the case study and implementation. The proposed system makes three major contributions to research and society: (i) smart parking for roadside operations, (ii) applied artificial intelligence for roadside parking activities and (iii) an environmentally-friendly business model for property management companies.
# Ambulance Locations in a Tiered Emergency Medical System in a City
## 2 Materials and Methods
### The Modular Capacitated Location Problem
Variables \(x_{ik}\) define how many type \(k\) ambulances will be located at the candidate location \(i\). Variables \(z_{ijkl}\) are continuous, and they represent the share of the priority \(l\) patients in zone \(j\) that will be served by the type \(k\) ambulances housed at station \(i\). The mathematical model is as follows:\[minimize \sum_{i\in I}\sum_{j\in I}\sum_{k=1}^{2}\sum_{l=1}^{2}b_{jl}t_{ij}z_{ ijkl}\] (18) \[subject\;to \sum_{i\in I}\sum_{k=1}^{2}z_{ijkl}=1 forj\;\in\;J,\;l=1,\;2\] (19) \[z_{ijkl}\leq\gamma_{kl}x_{ik} \qquad for\;i\;\in\;I,\;j\;\in\;J,\;l=1,\;2,\;k=1,\;2\] (20) \[\sum_{i\in I}z_{ik}\leq p_{k} \qquad for\;k=1,\;2\] (21) \[\sum_{j\in I}\sum_{l=1}^{2}b_{jl}z_{ijkl}\Big{(}t_{ij}+t_{kl}+a_{ k}\Big{(}t_{jl}+td+\overline{t}_{li}\Big{)}+(1-a_{k})\overline{t}_{ji}\Big{)} \leq 365*24*60*\] (22) \[\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad x_{ik}\in Z ^{+} \qquad\qquad\qquad\qquad\  
this town as a case study because it is the only town with multiple stations whose local administration publicly shares information on the number of citizens in every street.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Data
#### 3.1.5 Drought Index Data  
The standardized precipitation evapotranspiration index (SPEI) and the self-calibrating Palmer drought severity index (SCPDSI) are two commonly used drought indices in drought monitoring. Therefore, we calculated the correlation coefficients between these two drought indices and the GRACE-DSI drought index. The SPEI is a meteorological index used to monitor and analyze drought conditions. It combines PRE and potential ET data to provide a standardized measure of moisture deficit or excess [40]. The SCPDSI is an improvement on the original PDSI and is proposed to provide a more accurate and consistent measure of drought conditions in different regions. It takes into account SM, PRE, temperature and other meteorological factors, and through a self-calibration process, it adapts to changes in different climatic regions, overcoming the limitations of the original PDSI in its application to different climatic regions [41; 42].  
In addition to the two commonly used drought indices mentioned above, we also employed the comprehensive meteorological index (CMI), which is a multi-scalar drought monitoring tool based on the vine copula theory designed to capture the complex interactions among meteorological, agricultural, and hydrological droughts. Unlike traditional indices, it integrates multiple drought dimensions to quantify \"super droughts\"--extreme events where the superposition of different types of droughts amplifies negative impacts [43].  
In this study, the monthly 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) SPEI and SCPDSI gridded data were from the Consejo Superior de Investigaciones Cientificas and the Climate Research Unit at University of East Anglia, respectively, and the monthly 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) CMI gridded data were provided by [PERSON] et al.'s team.
## 2 Materials and Methods
### HPLC-UV Characterization of the Phenolic Fraction  
Phenolic compounds were determined by HPLC (Perkin-Elmer series 200, Monza, Italy) equipped with an autosampler and a UV-Vis detector (Perkin Elmer LC 240, Monza, Italy) set at 280 nm. For separation, a Phenomenex Kinetex C18 column was used (dimensions: 250 \(\times\) 4.6 mm, particle size: 5 \(\upmu\)m, pore size: 110 A; Phenomenex, Bologna, Italy). The mobile phases used were: (A) 1% acetic acid in water and (B) acetonitrile. For analyte separation, the mobile phase gradient was programmed as follows: from 10% to 100% solvent B for 30 min and subsequent return to initial composition in 4 min, achieving mobile phase stabilization for 10 min.  
40 mg of dry extracts sample was dissolved in 1 mL of water/methanol (50:50), vortexed for 3 min, centrifugated for 15 min, filtered with 0.2 \(\upmu\)m PTFE filter and analyzed.  
Quantification of polyphenols was carried out by the external standard method. Linear regression curves based on peak area were calculated for each phenolics compound after injection of mix phenolic standard solutions covering the sample range of concentrations (6-12-25-50-100 ppm).  
For quantitative analysis, a calibration curve for each available phenolic standard were constructed based on the UV signal: gallic acid (y = 36,255x - 26,062; R\({}^{2}\) = 0.9983), p-OH-benzoic (y = 31,711x - 16,966); R\({}^{2}\) = 0.9992), vanillic acid (y = 33,123x - 52,417; R\({}^{2}\) = 0.9974), rosmarinic acid (y = 34,344x - 40,066; R\({}^{2}\) = 0.9992), ferulic acid (y = 61,245x + 74,735; R\({}^{2}\) = 0.9912), caffe acid (y = 44,841x - 416,813; 0.9952), syringic acid (y = 39,490x - 106,101; R\({}^{2}\) = 0.9987), luteolin (y = 7593,1x - 19,075; R\({}^{2}\) = 0.9991), apigenin (y = 83,755x - 7443,3; R\({}^{2}\) = 0.9979), and chlorogenic acid (y = 29,136x - 63,864; R\({}^{2}\) = 0.9971).