esa-sceva/satcom-chunk-collection huggingface dataset:
# Symmetry of the Non-Analytic Solution of the Dirac Equation Inside the Proton of Hydrogen Atoms
## Appendix A The Experimental Charge Density Distribution Inside the Proton  
The most recent Charge Density Distribution (CDD) inside protons \(\rho(\mathbf{r})\), deduced from the corresponding experimental electric form-factors \(\mathrm{G_{e}(q)}\), was presented in 2018 by [PERSON] [45]--see Figure 11.  
The experimental \(\mathrm{G_{e}(q)}\) values were taken from a variety of world data. In the non-relativistic limit, \(\rho(r)\) is related to \(\mathrm{G_{e}(q)}\) via the Fourier transform:  
\[\rho(r)=\left[1/\left(2\pi^{2}r\right)\right]\int\limits_{0}^{\infty}\mathrm{G_ {e}(q)\sin(qr)\ q\ dq} \tag{1}\]  
The proton form factor is roughly described by the dipole shape:  
\[\mathrm{G_{D}(q)}=1/(1+q^{2}\mathrm{R_{D}}^{2}/12)^{2}, \tag{2}\]  
where \(\mathrm{R_{D}}\) is the corresponding rms proton charge radius. The CDD corresponding to this form factor has the shape of an exponential:  
\[\rho_{\mathrm{D}}(r)\propto\exp(-12^{1/2}r/\mathrm{R_{D}}). \tag{3}\]  
It is seen that _the experimental CDD in protons_ (solid line) _has the maximum at \(r=0\)_ and then monotonically falls-off to the periphery. This kind of CDD yields the potential satisfying, as a particular case, the general condition from Equation (14) from paper [3], under which the wave functions inside the proton can be matched at the proton boundary with the wave function of the singular solution outside the proton.  
The commonly used characteristic parameter of any distribution (or of a spectral line shape) is its Half Width at Half Maximum (HWHM). For the experimental CDD shown by the solid line in Figure 11, we found HWHM \(\approx\) 0.32 fm \(\approx\) 0.00089 n.u.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Dataset Construction
#### 4.1.2 Input Sequence Division  
The trajectory restoration model is mainly for the secondary restoration of the interpolated trajectory restoration data. To better capture the local patterns of the trajectory data and accelerate the training efficiency of the model, this study adopts the sliding window method to refine the segmentation of the trajectory data and generates several segmented trajectories as the input sequence to be supplied to the model after each trajectory is fixedly cut. Assuming a trajectory containing \(m\) trajectory points, the input sequence step size is set as \(l\), the output sequence step size is set as \(n\), and each slide is \(h\) steps. After being processed by the sliding window method, the original continuous trajectory data is transformed into a series of independent input/output sequences, and the process is shown in Figure 5.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 4 Simulation Results of Tri-Band Antenna on Human Body  
The proposed antenna is tested on a watch strap made of a rubber material and on human tissue, such as skin, to see human closeness effects. The small size antenna is maintained near the skin (\(\varepsilon_{\mathrm{r}}\) = 41.4 and \(\sigma\) = 0.88 s/m) [20]. The size of the phantom box is equal to 20 \(\times\) 20 mm\({}^{2}\). Figure 10a shows the proposed structure when the antenna is in proximity to the human tissue, and the simulated return loss is shown in Figure 10b.  
Figure 11a-c describes the radiation pattern of the proposed tri-band millimeter-wave antenna at 28 GHz, 38 GHz, and 60 GHz frequency bands. The elliptical radiation pattern in the E and H-plane is observed at 28 GHz, as shown in Figure 11a, while the broadsided directional pattern can be seen along the E and H-plane at 38 GHz. The proposed antenna shows the directional radiation pattern at 60 GHz, as shown in Figure 11c. Figure 12 illustrates the simulated gain and efficiency of the proposed antenna operating at a variety of frequency bands, including 28 GHz, 38 GHz, and 60 GHz. The simulated gain is 5.3 dB, 7.5 dB, and 9 dB at 28 GHz, 38 GHz, and at 60 GHz frequency bands, respectively. The antenna's efficiency across all frequency bands is lower than 80%.  
Figure 11: Antenna’s 2D radiation pattern over phantoms at the following frequencies: (**a**) 28 GHz, (**b**) 38 GHz, and (**c**) 60 GHz.  
Figure 10: (**a**) Proposed antenna over rubber and skin tissues; (**b**) simulated return loss.  
Specific Absorption Rate (SAR)  
Electromagnetic radiation may pose health concerns to humans, and these risks are quantified using the SAR. The following equation describes the link between input power and SAR (9):  
\[\text{SAR}=\frac{\sigma\big{|}E^{2}\big{|}}{\rho} \tag{9}\]  
where '\(\sigma\)' _and_ '\(\rho\)' are the thermal conductivity (S/m), and mass density (kg/m\({}^{3}\)), respectively while electric field intensity (V/m) are denoted by E. According to the equation below (10), the electric power intensity varies proportional to the signal power.  
\[\text{Power}\left(\text{W}/\text{m}^{2}\right)=\frac{(\text{E}(\text{V}/\text{m }))^{2}}{377} \tag{10}\]  
It is possible to conduct SAR simulations if the antenna is retained over the phantom. An average of 0.5 W of power is used to determine a device's SAR based on the IEEE/IEC 6270-1 standard. Figure 13 shows the SAR values of 0.9 W/kg at 28 GHz, 0.62 W/kg at 38 GHz, and 0.74 W/kg at 60 GHz over 1 g of mass tissue. While over 10 g of mass tissue, SAR values are 1.49 W/kg, 1.17 W/kg, and 1.47 W/kg at 28 GHz, 38 GHz, and 60 GHz, respectively.  
Figure 12: Efficiency and gain curves at operational frequency bands over phantoms.
## 3 Description of Numerical Algorithms
### Krylov Subspace Methods  
The power method consists in the calculation of the products \(\mathbf{A}\mathbf{b}\), \(\mathbf{A}^{2}\mathbf{b}\), \(\mathbf{A}^{3}\mathbf{b}\), and so on, until convergence. The method utilizes the converged vector, let us say \(\mathbf{A}^{\mathrm{c}}\mathbf{b}\), which corresponds to the LM eigenvalue. However, the power iteration discards the information of all vectors calculated in the meantime. The main idea of Krylov subspace eigenvalue methods is to utilize this intermediate information in order to calculate the \(p\) LM eigenvalues of \(s\mathbf{I}_{r}-\mathbf{A}\). The Krylov subspace that corresponds to \(\mathbf{A}\) and vector \(\mathbf{b}\) is defined as:  
\[\mathcal{K}_{p}(\mathbf{A},\mathbf{b})=\text{span}\{\mathbf{b},\mathbf{Ab}, \mathbf{A}^{2}\mathbf{b},\dots,\mathbf{A}^{p-1}\mathbf{b}\}\,. \tag{25}\]  
Subspace (25) is spanned by the columns of the Krylov matrix, which is defined as:  
\[\mathbf{K}_{p}=[\mathbf{b}\ \ \mathbf{Ab}\ \ \mathbf{A}^{2}\mathbf{b}\ \ \dots\ \ \mathbf{A}^{p-1}\mathbf{b}]\,. \tag{26}\]  
The matrix-vector multiplications typically render the columns of \(\mathbf{K}_{p}\) linearly dependent. Thus, these columns require orthonormalization. The orthonormalized vectors can be then employed in order to provide approximations of the eigenvectors that correspond to the \(p\) eigenvalues of LM. The eigenvalues are extracted by projecting \(\mathbf{A}\) onto the Krylov subspace, typically through the Rayleigh-Ritz procedure (see the Appendix A).
## 6 Energy Management
### Smart Home and Smart Grid  
As energy consumption has become a global concern within the last decade, energy efficiency and utilization of renewable energy resources became feasible with the advent of the smart grid (SG) concept and modernizing the electricity grid. A significant number of SG infrastructure specifications presented in the earliest first official definition [92], is in affiliation with smart homes, such as demand-side management and timely provision of energy price information.  
A smart grid can significantly enhanced energy efficiency within distributed networks and demand response (DR) is considered a substantial characteristic of a smart grid [93]. Within smart homes the cooperation of low precedence home appliances like washing machines and water heaters allows DR to utilize energy sources at lower prices. Energy scheduling can be classified as scheduling the resource of energy (i.e., when particular energy sources are utilized), and scheduling the consumption of energy (i.e., when various home appliances should be operating).  
The smart home energy management system (HEMS) is introduced as a device operating within the building to collect, store, and analyze the energy consumption information of the home. HEMS schedules the operating time of appliances in order to augment the efficiency and attenuate the electricity expenditure, taking into account if the usage of appliances are schedulable or not [94]. A smart grid is dependent on smart homes. Homes that utilize smart appliances enables data collection via sensors and smart meters, which facilitates optimization of electricity requirements and reduces the electricity cost. Smart grids can also take advantage of the electricity generated on-site and exported to the grid.
## 4 Discussion
### Advantages and Limitations  
Previous reports have used the GPP/SIF ratio as a diagnostic indicator of the relationship between GPP and SIF to explore their sensitivity to environmental factors [53; 54; 55]. Due to the light saturation point, some limitations exist at a higher-resolution temporal scale, such as the intraday scale. In this study, we evaluated the intraday GPP-SIF relationship using a logarithmic relationship, and, consequently, the results obtained are more reliable. Our results contribute to the formation of a more comprehensive understanding of the relationship between SIF and GPP under drought stress. Through machine learning and process-based analyses, we identified the primary drought synergistic factor contributing to the weakened GPP-SIF connection under drought stress, namely VPD, and incorporated it into the empirical GPP-SIF relationship to offset the accuracy loss of GPP estimation caused by drought stress. The results of independent verification confirmed that our attempt was significantly effective, reducing the decline in GPP-SIF coupling (R\({}^{2}\)) caused by drought stress and considerably improving the GPP estimation accuracy under drought stress. [PERSON] et al. (2021) [20] introduced canopy stomatal conductance (gc) and used gc \(\times\) SIF as an independent variable to improve the accuracy of estimating GPP based on SIF under drought stress, finding that VPD data were more easier to obtain than gc. Compared to machine learning models that integrate various canopy-related, physiological, and environmental factors, our approach clarified the main contributing factors to a greater extent, and it was more straightforward and physiologically based.  
However, our results were based on the assumption that there is a simple empirical relationship between SIF and GPP, constituting a limitation of this study. Numerous earlier studies have demonstrated that the SIF-GPP relationship is complex due to the mechanical linkage between SIF and GPP. According to the mechanistic light response (MLR) model, the SIF-GPP relationship can be understood as being mediated by the electron transport rate (J), which can be divided into the SIF-J relationship (determined by the initial energy allocation in the light reaction) and the J-GPP relationship (defined by carbon assimilation in the carbon reaction) [56; 16]. The MLR model separates the light and carbon reactions, providing new approaches to exploring the SIF-GPP relationship under drought stress. It is critical to further improve GPP estimation models in the context of climate change by using the mechanistic linkage between SIF and GPP to explore their relationship under drought stress.
# Wireless-Channel Key Distribution Based on Laser Synchronization
## 3 Experimental Results
### Secure Key Generation Rate
In this robust quantization method, only a certain proportion of the sampled points are used to generate the key. The variation of the retained ratio \(R_{dt}\) in relation to the quantization threshold coefficients \(\text{C}_{+}\) is illustrated in Figure 5a. Given this fact, the key rate can be calculated using  
\[r=f_{s}R_{dt}R_{b}, \tag{7}\]  
where \(f_{s}\) is raw sample rate. Considering the bandwidth of the laser output, the raw sampling rate is set to \(f_{s}=1.0\) GHz to ensure the randomness of the generated key. Figure 5b shows the key rate and the BER between Alice and Bob (\(R_{full}\)) in relation to the quantization threshold coefficients \(\text{C}_{+}\). \(R_{full}\) decreases as \(\text{C}_{+}\) increases and remains below the BER threshold of \(3.8\times 10^{-3}\) for hard-decision forward-error correction (HD-FEC) when \(\text{C}_{+}>0.4\). When \(\text{C}_{+}\) is excessively small or large, the key rate will decrease due to a high BER or low retained ratio. The maximum key generation rate  
Figure 5: Effects of quantization threshold coefficients on (**a**) the retained ratio, (**b**) the BER between Alice and Bob, and the key generation rate. threshold is achieved when \(C_{+}=0.4\), resulting in 150 Mbit/s. In this configuration, we summarized statistical measures of secure key generation in Table 1.  
Considering the influence of uncertain factors such as noise interference, one can enhance the robustness of the system by increasing the threshold coefficient, at the cost of reducing the key rate. For example, when \(C_{+}=0.8\), the BER decreases to \(3.6\times 10^{-4}\), while the key generation rate decreases to 80 Mbit/s. In order to verify the randomness of the generated keys, we employed the National Institute of Standards and Technology Special Publication 800-22 statistical tests (NIST SP800-22) to test the generated keys. As shown in Figure 6, the \(p\)-values are all greater than 0.01, indicating that the generated keys successfully pass all the 15 NIST tests and thereby verify their randomness.
# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
## 2 Breif Introduction of YOLOv7
### YOLOv7 Object Detection Algorithm
#### 2.1.4 Head  
The detection header of the target detection model is a vital component of the entire YOLOv7 architecture and is responsible for generating the final target detection results. The detailed functions of the detection head in YOLOv7 are below:  
* Bounding Box Regression The target's position in the input image is usually represented as the bounding box coordinates. For each detection box, the header generates four values representing the coordinates of the bounding box's upper left and lower right corners.
* Class Classification Another critical function is to categorize the detected targets. The detection header generates a category probability distribution for each detection frame, indicating the probability that the target belongs to each possible category. Usually, the model selects the category with the highest probability as the predicted category.
* Object Confidence Estimation In addition to location and category, the detection header generates a target confidence score, which indicates the model's confidence in whether each bounding box contains a valid target. This score is typically used to filter out detections with high confidence.
* Other function In multi-scale processing for multi-scale target detection, the detection head needs to be able to process information from different layers and sizes of feature maps. It is responsible for merging this information to detect small and large targets. Activation functions: the detection head usually includes activation functions for classification and regression tasks, such as sigmoid for confidence score generation and softmax for category probability calculation. For loss function calculation, the detection header is also responsible for computing loss functions for the target detection task. These loss functions typically include location regression loss, category classification loss, and confidence loss. These loss functions are used to train the model to optimize target detection accuracy. The detailed framework of the YOLOv7 is in Figure 1 below.
## 2 Materials and Methods
### Filtering and Averaging Process  
Preliminary analysis of the optical data indicated invalid proxy NDVI values during periods of rainfall. These periods were easily detectible by evaluating relative humidity (RH) values. Proxy NDVI values associated with RH values greater than 76% tended to be outliers (data not shown) and were discarded. Similarly, automatic gain control (AGC), an IRGA diagnostic value, identified obstructions blocking the sensor's optical path. Water droplets, rime, dew, dust or pollen on the optical path impede sensor function and reduce data quality. Proxy NDVI data with associated AGC values greater than 64% were filtered from the dataset. To achieve data comparable to the MODIS values, a midday 5-h averaging (aggregation) was also applied to both proxy NDVI and flux data. In preliminary analyses, temporal averaging of proxy NDVI over 5 h (Figure 4) reduced short-term variability and clarified seasonal patterns. The impact of temporal aggregation was quantified by calculating the \(R^{2}\) for the three time intervals considered (1, 3, 5 h midday periods). \(R^{2}\) values increased from 1-h to 5-h averaging periods considering the whole season; similar results were found when considering green up and senescence periods separately (Table S1, Supplementary Materials). Since the relationship between proxy NDVI and net CO\({}_{2}\) flux showed the best fit using the 5-h aggregation period (Table S1), and encompassed the MODIS overpass times, this time period was used in all subsequent analyses of proxy NDVI.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 5 CPS-WSS Monitoring Application Challenges and Future Directions
### Future Directions
#### Optimized Sensor Coverage  
Coverage of CPS sensing devices is an important issue for a reliable leakage and water quality monitoring across the complex water distribution networks in both underground, underwater, and above-ground applications. Fortunately, the design of mmWave sensors offers precise and multidimensional sensing with a capacity to penetrate materials such as plastic and drywall with good resistance to environmental conditions such as rain, fog, dust, and snow [48]. As these sensors are arranged in a sensor node connected via wireless means, more research studies should be conducted in the area of sensor network coverage optimization. As such, efficient optimization algorithms for sensor coverage enhancement should be investigated.
## 7 Conclusions  
An innovative and unique approach to tackle the threats posed by the extinction of animal species and its impact on agricultural farms has been introduced in this research. The research successfully achieves real-time detection and classification of endangered and harmful animals in farming using a sophisticated object-detection system that combines the ESP32-CAM and the YOLOv8 model. The system's impressive performance is evident through its efficiency and accuracy, with a mean average precision (mAP) of 92.44% and a sensitivity rate of 96.65% on the unseen test dataset. The significance of this research lies in its dual purpose: promoting the conservation of endangered species and mitigating the agricultural damage caused by these animals. By providing farmers with real-time warnings when animals approach their fields, the proposed system enables a humane approach to protect crops, eliminating the need for harmful and lethal measures against the animals. This research approach fosters a harmonious coexistence between farming communities and wildlife, ensuring the safety and well-being of humans and endangered animals. The research's comprehensive workflow, from data collection and model training to implementing the IoT system, showcases a holistic solution that preserves agricultural productivity and ecological balance. This research represents a crucial stride towards sustainable agriculture and wildlife conservation, addressing the challenges posed by the encroachment of human settlements into natural habitats. The novel contributions of this work lie in seamlessly integrating deep learning object-detection models and IoT systems to develop an animal-detection system that prioritizes animal welfare while safeguarding crops. The resulting system benefits farmers by protecting their livelihoods and contributes to the preservation of endangered species, thereby promoting a balanced ecosystem.  
Conceptualization, M.A.K.R., N.M.F., D.S. and M.M.L; methodology, M.A.K.R., N.M.F., S.C., D.S. and S.S.M.; validation, M.A.K.R., N.M.F. and M.M.I.; formal analysis, M.A.K.R., N.M.F., S.C., D.S. and S.S.M.; investigation, M.A.K.R., N.M.F., S.C., D.S. and S.S.M.; writing--original draft preparation, M.A.K.R., N.M.F., S.C., D.S. and S.S.M.; writing--review and editing, M.M.I.; supervision, M.A.K.R. and M.M.I.; project administration, M.M.I. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Institute for Advanced Research Publication Grant of the United International University, Ref. No.: IAR-2023-Pub-034.  
Dataset access is only granted for legitimate research purposes.  
The authors declare no conflict of interest.