esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 4 Experiments
The formula is:  
\[\text{IoU}=(\text{area}(\text{C})\cap\text{area}(\text{G}))/(\text{area}( \text{C})\cup\text{area}(\text{G})) \tag{9}\]  
where \(\text{area}(\text{C})\) is the area of the candidate bounding box; \(\text{area}(\text{G})\) is the area of the ground truth bounding box. We collected the true positives' IoUs which are predicted by the above five methods and calculated the mean IoU (MIoU) to judge the accuracy of the predicted bounding boxes. The formula is shown as follows:  
\[\text{MIoU}=\sum_{\text{i=1}}^{\text{n}}\text{IoU}_{\text{i}}/\text{n}, \tag{10}\]  
where n is the total number of true positives; IoU\({}_{\text{i}}\) is the IoU score of the i-th true positive. The results are shown in Table 1. PMCD achieves the best performance in these six methods. The second best is HR, no matter how small the face is, HR would be detected very precisely when the light is good. However, when the face is in a dark environment, the face's bounding box will be detected with some deviation. While the performance of SSD is the lowest because SSD cannot retain its ability when the target is small even though it can detect tiny faces, because its default anchors are set to  
Figure 4: Face detection results. different shapes, but the box shape with the highest score is not always the most suitable one. The other three methods can get great scores only if the targets are big. In order to verify the effectiveness of PMCD, the F1 measures, where precision and recall are equal, are shown in Table 2. The table of our model is higher than benchmark results of the other three important face detection neural networks. The MTCNN, VJ, and MMSC cannot keep high performance when targets are less than \(20\times 20\), which could result in many misclassifications. The framework proposed in this paper can solve this problem very well because we use two neural networks for grading detection, which can avoid the subjectiveness of hand-crafted features. In addition, the pre-identification mechanism can avoid the information loss of small targets, which often happens in a deep neural network. In addition, we tested the detection speed of these methods based on frames per second (FPS), and the results are shown in Table 3. Although PMCD is not the fastest method, it still exceeds 24 FPS, maintaining real-time performance. PMCD is designed as a cascade framework, in which some time is wasted in passing data, that is, the end-to-end detectors can outperform cascade frameworks in detection speed. Nevertheless, for the tiny-face detection task, the advantages of the cascaded detector are obvious.
# Coupled Mode Sound Propagation in Inhomogeneous Stratified Waveguides
## 2 Method  
### Two-Dimensional Two-Way Coupled Mode Equations  
Let us start with sound propagation in the multilayered waveguide sketched in Figure 1 with interfaces \(h_{j}(x),j=1,2,\dots,J-1\), separating the layers. The upper water column is confined between a flat pressure-release surface and underlying sediment layers of any shape. The Neumann boundary condition is imposed at the bottom of the lower layer. The whole region is divided into three subregions. Subregion \(0\leq x\leq x_{R}\) corresponds to the scattering region where both volumetric and bathymetric variations exist. In subregions \(x<0\) (not shown) and \(x>x_{R}\), mass density and sound speed are assumed to be only depth-dependent, and boundaries and interfaces are horizontal. This assumption allows for consistently formulating the source and radiation conditions by means of normal-mode expansions. The Helmholtz equation governing the acoustic pressure is  
\[\frac{\partial}{\partial x}\bigg{[}\frac{1}{\rho(x,z)}\frac{\partial p(x,z)}{ \partial x}\bigg{]}+\frac{\partial}{\partial z}\bigg{[}\frac{1}{\rho(x,z)} \frac{\partial p(x,z)}{\partial z}\bigg{]}+\frac{\omega^{2}}{\rho(x,z)c^{2}(x,z)}p(x,z)=0, \tag{1}\]  
supplemented by boundary conditions  
\[p(x,h_{0})=0,\quad\frac{\partial p}{\partial n}(x,h_{J}(x))=0, \tag{2}\]  
Figure 1: (Color online) Geometry of 2D multilayered waveguide. and continuous conditions  
\[\left\{ \begin{aligned} & p(x,h_{j}^{-}(x))=p(x,h_{j}^{+}(x)),\\ &\frac{1}{\rho_{j}(x,h_{j}^{-}(x))}\frac{\partial p(x,h_{j}^{-}(x) )}{\partial n}=\frac{1}{\rho_{j+1}(x,h_{j}^{+}(x))}\frac{\partial p(x,h_{j}^{+ }(x))}{\partial n},\end{aligned} \right. \tag{3}\]  
where \(j=1,2,\ldots,J-1\), \(c(x,z)\) is sound speed, and \(\rho(x,z)\) is density. \(c(x,z)\) and \(\rho(x,z)\) could exhibit sharp discontinuities at the interfaces. According to the multimodal method, acoustic pressure can be expressed as a sum of transverse eigenfunctions \(\psi_{n}(z;x)\):  
\[p(x,z)=\sum_{n=0}^{N-1}P_{n}(x)\psi_{n}(z;x), \tag{4}\]  
where \(N\) is the truncation number, \(P_{n}(x)\) are modal amplitudes, and \(\psi_{n}(z;x)\) are defined to be normal modes of a one-layer homogeneous waveguide with constant depth equal to the local depth of the multilayered waveguide, satisfying the following equations:  
\[\left\{ \begin{aligned} &\frac{\partial^{2}\psi_{n}(z;x)}{ \partial z^{2}}+\gamma_{n}^{2}\psi_{n}(z;x)=0,\\ &\psi_{n}(h_{0};x)=0,\quad\frac{\partial\psi_{n}(h_{J}(x);x)}{ \partial z}=0.\end{aligned} \right. \tag{5}\]  
Eigenfunctions \(\psi_{n}(z;x)\) in the series representation have analytical expressions, i.e.,  
\[\psi_{n}(z;x)=\sqrt{\frac{2}{h_{J}(x)}}\sin\left(\frac{(n+0.5)\pi z}{h_{J}(x)} \right),\ n=0,1,2,\ldots,N. \tag{6}\]  
In contrast, the usual local modes that traditional coupled mode method used in the series representation for acoustic pressure, namely, transverse modes at \((x,z)\) of the complex multilayered waveguide, can only be numerically computed. The use of eigenfunctions \(\psi_{n}(z;x)\) simplifies computations in this sense.
## 4 Hierarchical Energy Balancing Protocol (HEBP) Design
### Quality of Service in WSN  
QoS is one of the significant aspects of any application, and WS is therefore not spared. For WS, QoS reflects the sensor's ability to guarantee the performance required by the application [49]. QoS can be assessed through many aspects such as delay in data transmission, throughput, reliability, data accuracy, and network lifespan. By summarizing these different aspects, QoS can be assessed through the quantity and/or quality of data that can be transmitted. This work considers the capacity of the sensor nodes about the quantity of data that can be transferred to the BS. The other objective of the proposed protocol is to maximize the number of nodes that can transmit data at a given time.  
Recall that in battery-powered wireless networks, QoS improvement is often achieved through energy efficiency optimization [50]. Energy-efficient management solutions aim to reduce the energy dissipation of the WS. In the case of ambient energy harvesting WSs, effective management consists of ensuring an Energy-Neutral Operation (ENO) at each WS [51]. The ENO condition is assured for node \(i\) when the harvested energy \(E_{H_{i}}\) remains greater than its energy expenditure \(E_{WS_{i}}\). In most cases, this state is achieved by leaving the WS in standby mode for as long as possible, contributing to the deteriorating Quality of Service (QoS).
## 3 Results  
### Wetland Mapping  
The temporally and spatially varying wetland maps demonstrate the inter-annual and inter-seasonal variability in wetland coverage not observed in the MODIS MCD12Q1 static land cover data (Figures 5 and 6). The areal extent of wetlands in the MODIS product falls within the range of the minimum and maximum extent from the dynamic wetland maps, except for in 2005 which was an extremely dry year when the timing of wetland expansion appears to be controlled by groundwater contributions rather than seasonal rainfall (Figure 5). Deficits in groundwater estimated by the Gravity Recovery and Climate Experiment (GRACE) peak during the 2005 drought in the Zambezi basin and are associated with a large decrease in terrestrial water storage [(78; 32)]. The annual variability in maximum wetland extent in Figure 5 follows the maximum observed discharge rates for the Zambezi River [(79)]. The spatial patterns of the maximum wetland area in the dynamic maps reflect the observed locations in the MODIS land cover data during the wet year (Figure 6). While in the dry year, the riparian wetlands observed in the MODIS data that are not present on the day of maximum wetland extent, but are observed over the course of the year (see Supplemental Materials Videos S1-S3).  
Figure 5: Bar graph showing the annual variability in wetland area. The minimum (black) and maximum (gray) wetland areas for each year are displayed alongside the wetland area from the annual MODIS MCD12Q1 product (white). The dates for the minimum and maximum areal extents are displayed above their respective bars.
# EfficiencyX: Study of Energy Efficiency between Two Robotic Arms
## 2 Materials and Methods
### Energy Efficiency  
To calculate the energy efficiency of this robot arm (see Equation (1)), the useful power it produces (\(E_{u}\)) in Watts (W), and the total power it consumes (\(E_{t}\)) in Watts (W) will be measured through an IoT device. The useful power is the effective energy used to perform the desired work, while the total power is the energy supplied to the robot arm. Subsequently, the useful power is divided by the total power and the result is multiplied by 100 to obtain a percentage. The formula is:  
\[\eta=\frac{E_{u}}{E_{t}}\times 100 \tag{1}\]  
where:  
(\(\eta\)) is energy efficiency,  
(\(E_{u}\)) is useful energy,  
and (\(E_{t}\)) is total energy consumed.
## 3 Results  
### Performance of GPP Estimation Using SIF and PRI  
First, we explored the performance of GPP estimates based on SIF using a linear model. Compared to \(\text{SIF}_{\text{TOC}}\), \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) could quantify canopy photosynthesis more accurately for both half-hourly (R\({}^{2}\) = 0.61, RMSE = 8.67 \(\mu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\), Figure 4a,b) and daily (R\({}^{2}\) = 0.63, RMSE = 7.54 \(\mu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\), Figure 4e,f) data. This indicates that broadband photosystem-level PSII SIF can better track the dynamics of GPP.  
Figure 3: Overview of the GPP estimate flowchart. The linear model was based on only one variable, \(\text{SIF}_{\text{TOC\_760}}\) or \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\); the multi-linear model was made up of two variables, \(\text{SIF}_{\text{TOC\_760}}\) and PRI or \(\text{SIF}_{\text{TOT\_FULL\_PSII}}\) and \(\Delta\text{PRI}\). The RF model refers to the Random Forest regression model.  
We further analyzed the GPP estimates based on SIF and PRI using a multi-variable linear model. GPP was more accurately estimated using both SIF and the PRI than when using only SIF (Figure 4). Compared to GPPTOC_PRI estimated by SIFTOC_760 and PRI, GPPTOT_APRI estimated by SIFTOI_FULL_PSII and \(\Delta\)PRI correlated better with the GPPEEC for both half-hourly (R\({}^{2}\) = 0.63, RMSE = 8.47 \(\mu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\), Figure 4c,d) and daily (R\({}^{2}\) = 0.66, RMSE = 7.17 \(\mu\)mol\(\bullet\)m\({}^{-2}\)\(\bullet\)s\({}^{-1}\), Figure 4g,h) data. This indicates that the corrected PRI (APRI) and broadband photosystem-level PSII SIF (SIFTOI_FULL_PSII) can monitor dynamic changes in GPP better.
## 3 Results
### Environmental Impact and Accident Analysis
#### 3.3.2 Environmental Impact  
We also conducted a comprehensive assessment of the potential environmental impact of the R-barricade by implementing a full life cycle assessment (LCA) to measure its impact at every stage, from material procurement, manufacturing, and use to final disposal. The key areas of focus include the following:  
* Environmental Friendliness of Materials: We selected sustainable materials that meet safety standards and have high recycling value;
* Long-term Impact on the Ecosystem: This includes the potential effects of light pollution and disturbances to wildlife during prolonged use;
* Preventive Measures: In the product design, we incorporated preventive measures to reduce these risks, such as optimizing material usage, reducing waste during production, and designing an efficient recycling system.
###### Abstract  
Aerial operation with unmanned aerial vehicle (UAV) manipulator is a promising field for future applications. However, the quadrotor UAV manipulator usually suffers from several disturbances, such as external wind and model uncertainties, when conducting aerial tasks, which will seriously influence the stability of the whole system. In this paper, we address the problem of high-precision attitude control for quadrotor manipulator which is equipped with a 2-degree-of-freedom (DOF) robotic arm under disturbances. We propose a new sliding-mode extended state observer (SMESO) to estimate the lumped disturbance and build a backstepping attitude controller to attenuate its influence. First, we use the saturation function to replace discontinuous sign function of traditional SMESO to alleviate the estimation chattering problem. Second, by innovatively introducing super-twisting algorithm and fuzzy logic rules used for adaptively updating the observer switching gains, the fuzzy adaptive saturation super-twisting extended state observer (FASTESO) is constructed. Finally, in order to further reduce the impact of sensor noise, we invite a tracking differentiator (TD) incorporated into FASTESO. The proposed control approach is validated with effectiveness in several simulations and experiments in which we try to fly UAV under varied external disturbances.  
super-twisting; sliding mode extended state observer; saturation function; fuzzy logic; attenuate disturbance +
Footnote †: journal: Applied Mechanics and System Safety
## 4 Discussion
The benchmarking exercise will help determine how well the state-of-the-art models can simulate the magnitude, patterns, and interannual variability of terrestrial photosynthesis and shed light on directions for future model improvement. Our study demonstrated the tremendous capability of SIF observed from space in mapping terrestrial GPP globally, and is also enlightening for future efforts using SIF measured from space. This advance leads to a new, independent approach in measuring terrestrial photosynthesis compared to traditional methods (e.g., process-based models, light use efficiency models, data-driven methods). The process-based models generally have complex structure, and require a variety of input data that could have significant uncertainty [55]. The data-driven methods also rely on a large number of explanatory variables to estimate GPP [38; 52]. Even the light use efficiency (LUE) models [56; 57] with a simple model structure (e.g., the MODIS GPP model) have several different input variables such as land cover, the fraction of PAR absorbed by vegetation canopies (_f_PAR), PAR, and two environmental scalars used to characterize temperature and water stresses and also include multiple models parameters (e.g., maximum LUE, optimum temperature). The use of these ancillary data such as gridded land cover and meteorological data can lead to significant uncertainty to the resulting GPP estimates [58]. Our novel, SIF-based approach can lead to more accurate GPP estimates at regional to global scales by avoiding uncertainties from other data (e.g., climate, soil properties). Although the gridded SIF data (GOSIF) were generated based on OCO-2 SIF along with EVI, temperature, PAR, and VPD, these ancillary data layers were simply used to help generate spatially continuous, fine-resolution SIF estimates from discrete OCO-2 soundings. With the availability of spatially continuous, high-quality SIF measurements (e.g., the ongoing TROPOspheric Monitoring Instrument or TROPOMI, 0.1\({}^{\circ}\) resolution; the upcoming FLuorescence EXplorer or FLEX, 300 m resolution), our SIF-based approach will be able to generate more accurate GPP estimates regionally and globally by avoiding the uncertainty associated with input data (e.g., climate forcing). The use of the universal SIF-GPP relationship across a variety of biomes [17] can also avoid the uncertainty associated with land cover maps. Our novel method also has lower computational demand than other approaches, and can potentially provide more accurate, near real-time estimates of terrestrial photosynthesis globally from space.
# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
## 4 Results and Discussion  
### Dataset  
Medical image processing has emerged as a crucial domain for research, particularly in the field of diagnosis of malignant lesion. The collection of relevant lesion images and training the classifier with suitable dataset is a perplexing task. The proposed algorithm was trained, tested and evaluated on three most commonly used, publicly available dataset: PH2 [34], International Skin Imaging Collaboration (ISIC) 2017 [35] and ISIC 2018 [36] datasets. Both the ISIC datasets are used for training the classifier and testing the proposed algorithm, whereas the PH2 datasets are only used for testing and evaluating the proposed method, thereby intending it as a holdout dataset for our algorithm. These images are composed by specialized dermatologists after performing a series of dermoscopic analysis on dots, pigmented region, and streaks of the lesion image. These publicly available datasets were developed with the aim to aid researcher for their specialized research on analysis of skin lesion, classification and segmentation. The PH2 datasets are published from dermatology department of Pedro-Hispano hospital. A sum total of 200 dermoscopic lesion images are present in this dataset, with 40 instances of melanoma lesion and remaining 160 instances of atypical nevi and normal nevi divided equally into two halves. These dermoscopic images are of 768 \(\times\) 560 pixels in resolution, captured with 20\(\times\) magnification.  
Figure 9: Classification of lesion using YOLO.  
Whereas a dermoscopic image resolution of ISIC 2017 dataset rages from 540 \(\times\) 722 pixels to 4499 \(\times\) 6748 pixels. A total number of 2000 RGB image of 8-bit are used for training the classifier and remaining 600 and 150 dermoscopic lesion images for testing and validation respectively. ISIC 2018 dataset embodies 10,015 lesion images, where 1320 images are assigned for testing and 8695 lesion images for training. Grouped dermoscopic images are of 24-bit RGB and are sourced from ISIC library and HAM10000 dataset. These datasets are annotated by expert dermatologist and have ground truth values for classification and segmentation which make it favorable for research. Table 1 provides an overall tabulated figure of each data used for training, testing, and validation.
# Tuning of a Viscous Inerter Damper: How to Achieve Resonant Damping Without a Damper Resonance
## 3 Viscous Inerter Damper
### Correction Coefficients  
For the damper ratio \(\beta\to 0\), the solution to the characteristic Equation (30) becomes \(\rho=0\) and \(\rho=1\). In Figure 6, the undamped pole (or normalized free natural) frequencies are depicted by circles, which are located at the origin, at \(\rho=1\) for mode 1 and then at \(\rho=\omega_{2}/\omega_{1}\simeq 3\) for the next vibration mode \(j=2\).  
The aim of the tuning procedure is to place the first two clamped natural frequencies (\(\hat{\rho}_{1}\) and \(\hat{\rho}_{2}\)) so that they create apparent resonant absorber characteristics around the first free natural frequency \(\rho=1\). Thus, the two solutions to the characteristic equation in (30) for \(s=1\) must be  
\[\hat{\rho}_{1}=\frac{\hat{\omega}_{1}}{\omega_{1}}\qquad,\qquad\hat{\rho}_{2}= \frac{\hat{\omega}_{2}}{\omega_{1}} \tag{31}\]  
when \(\beta\to\infty\).  
In the clamped limit \(\beta\to\infty\), the last term (proportional to \(i\rho\)) in (30) vanishes, whereby the characteristic equation reduces to  
\[\rho^{4}-\rho^{2}\bigg{(}1+\kappa_{1}^{\prime}+\frac{\kappa_{1}^{\prime}}{ \hat{\mu}_{1}}\bigg{)}+\frac{\kappa_{1}^{\prime}}{\hat{\mu}_{1}}=0 \tag{32}\]  
with subscript \(s=1\) indicating that the damper design targets the first vibration mode of the building model. For a given value of the front-end inertance \(m\), the clamped natural frequencies are determined by solving the clamped eigenvalue problem (9). Thus, the solutions \(\rho=\hat{\rho}_{1}\) and \(\hat{\rho}_{2}\) to (32) are known from (9).  
For the present normalization of the clamped-limit characteristic Equation (32), the product of the roots \(\hat{\rho}_{1}\) and \(\hat{\rho}_{2}\) is equal to the constant term, while the sum is the linear coefficient with the opposite sign. These relations can be used to solve for the modal damper parameters as a function of the clamped natural frequencies. The expressions can be written as  
\[\bar{\mu}_{1}=\bigg{(}1-\frac{1}{\hat{\rho}_{2}^{2}}\bigg{)}\bigg{(}\frac{1}{ \hat{\rho}_{1}^{2}}-1\bigg{)} \tag{33}\]  
and  
\[\kappa_{1}^{\prime}=(\hat{\rho}_{2}^{2}-1)(1-\hat{\rho}_{1}^{2}) \tag{34}\]  
which are similar to the corresponding expressions for resonant inerter-based absorbers in [47]. When introducing \(\hat{\mu}_{1}\) from (33), the correction mass ratio \(\mu_{1}^{\prime}\) is isolated in (23) as  
\[\frac{1}{\mu_{1}^{\prime}}=\frac{1}{\hat{\mu}_{1}}-\frac{1}{\mu}=\frac{1}{ \left(1-\frac{1}{\hat{\rho}_{2}^{2}}\right)\!\left(\frac{1}{\hat{\rho}_{1}^{2} -1}\right)}-\frac{1}{\mu} \tag{35}\]  
where the front-end mass ratio is \(\mu=m/m_{1}\) is defined in (24).
## 2 System Model, Problem Formulation and the PS Solution
### Problem Formulation and PS Solution  
In this work, our target is to minimize the sum of the transmit power of the transmitters by optimizing the PS ratios and the transmit power subjects to the required SINR and harvested energy. Then, we formulate the power optimization problem as follows:  
Figure 1: The SWIPT system model with a PS scheme.  
\[\underset{\{p_{k}\},\{\theta_{k}\}}{\text{minimize}} \sum_{k=1}^{K}p_{k}\] (4a) subject to \[\frac{\theta_{k}p_{k}|h_{kk}|^{2}}{\theta_{k}\sum\limits_{j\in K \backslash\{k\}}p_{j}\big{|}h_{jk}\big{|}^{2}+\theta_{k}\sigma_{k}^{2}+\delta_ {k}^{2}}\geq\gamma_{k},\quad\forall k \tag{4b}\] \[\mu_{k}\left(1-\theta_{k}\right)\left(\sum_{j=1}^{K}p_{j}\big{|}h _{jk}\big{|}^{2}+\sigma_{k}^{2}\right)\geq e_{k},\quad\forall k\] (4c) \[0<\theta_{k}<1,\quad\forall k\] (4d) \[p_{k}>0,\quad\forall k \tag{4e}\]  
In constraint Equation (4b), in order to guarantee the information services of receivers, the received SINR is greater than the minimum required SINR, \(\gamma_{k}\). Constraint Equation (4c) guarantees providing the harvested energy at the receiver. The problem Equation (4) is changed to Equation (5):  
\[\underset{\{p_{k}\},\{\theta_{k}\}}{\text{minimize}} \sum_{k=1}^{K}p_{k}\] (5a) subject to \[-\left(\frac{1}{\gamma_{k}}+1\right)p_{k}|h_{kk}|^{2}+\sum_{j=1}^ {K}p_{j}\big{|}h_{jk}\big{|}^{2}+\sigma_{k}^{2}+\frac{\delta_{k}^{2}}{\theta_{ k}}\leq 0,\quad\forall k \tag{5b}\] \[\frac{e_{k}}{\mu_{k}\left(1-\theta_{k}\right)}-\left(\sum_{j=1}^ {K}p_{j}\big{|}h_{jk}\big{|}^{2}+\sigma_{k}^{2}\right)\leq 0,\quad\forall k\] (5c) \[0<\theta_{k}<1,\quad\forall k\] (5d) \[p_{k}>0,\quad\forall k \tag{5e}\]